AI-Driven Communication Protocol for a Power Grid: Transforming Canada’s Security Framework

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Today we remember Grenfell Tower fire 00:54 14, June 2017, Forever in our hearts. Forever on our conscience. Fuelling our commitment to systemic change.

#Fire#safety#equity

Glossary

This glossary provides definitions for key terms used throughout this article. If you come across any unfamiliar terms while reading, please refer back to this section for clarification. Understanding these terms will enhance your comprehension of the content and concepts discussed.

Advanced Distribution Management Systems (ADMS): Software platforms that manage the distribution grid, incorporating real-time data to optimize operations.

Advanced Energy Management Systems (AEMS): Systems that optimize energy use across multiple buildings or regions using advanced algorithms.

AI-Driven Communication Protocol: A framework integrating AI into energy infrastructure to optimize grid performance and ensure stability.

Artificial Intelligence (AI): Technology enabling machines to perform tasks that typically require human intelligence.

Autonomous Monetization: Using automated systems to manage and optimize financial transactions, often related to energy trading.

Belt and Road Initiatives (BRI): China's global development strategy involving infrastructure and investments in various countries.

Blockchain Technology: A decentralized digital ledger that securely records transactions across many computers.

Blockchain-Based Energy Trading: Using blockchain technology to enable secure and transparent energy trading.

Building Energy Management System (BEMS): Systems that monitor and optimize building energy consumption.

Canadian Shield: A proposed global AI-driven climate emergency response system.

Climate Change Adaptation: Adjustments in systems or practices to minimize damage from climate change effects.

Climate Change Mitigation: Efforts to reduce or prevent the emission of greenhouse gases.

Cooling Degree Days (CDD): Measure used to estimate cooling requirements based on temperature differences.

Cybersecurity: Measures to protect computer systems and networks from digital attacks.

Data Privacy and Security: Protection of data from unauthorized access and ensuring user control over personal information.

Decentralized Finance (DeFi): Financial services using blockchain technology to remove intermediaries.

Demand Response Management Systems (DRMS): Systems that adjust energy consumption based on real-time demand to stabilize the grid.

Digital Governance Model: A framework for managing digital interactions and infrastructures within a democratic context.

Digital Identity Verification: Processes for confirming the identity of individuals online.

Digital Silk Road (DSR): Part of BRI focusing on digital infrastructure and governance.

Digital Twin: A virtual model of a physical system that allows for real-time monitoring and optimization.

Discrepancy Analysis: Identifying and addressing differences between predicted and actual energy use to find faults.

Distributed Energy Resource Management Systems (DERMS): Systems managing distributed energy resources like solar and wind power.

Global Energy Interconnection (GEI): China's initiative to connect energy grids across continents.

High-Voltage Alternating Current (HVAC): Standard technology for transmitting electricity.

High-Voltage Direct Current (HVDC): Technology for transmitting electricity over long distances with minimal loss.

Heating Degree Days (HDD): Measure used to estimate heating requirements based on temperature differences.

Integrated Digital Hub (IDH): A platform that integrates multiple data sources and technologies for collaborative solutions.

Internet of Things (IoT): A network of interconnected devices that collect and exchange data.

Mass Migration: Large-scale movement of people from one region to another due to various factors.

National Inquiry: A proposed comprehensive investigation into corruption and economic mismanagement in Sri Lanka.

New Global Consensus (NGC): A comprehensive economic and security framework designed to address structural issues in Canadian democracy and global challenges.

One Sun One World One Grid (OSOWOG): Initiative to connect renewable energy grids globally, co-founded by India and France.

Passive House Planning Package (PHPP): A tool for designing energy-efficient buildings.

Peer-to-Peer Energy Trading Platform: A system allowing direct energy transactions between producers and consumers.

Quantum Computing: Advanced computing using quantum-mechanical phenomena to perform operations on data.

Real-Time Energy Management (RTEM): Systems that monitor and manage energy use in real-time.

Satyam Studio: A start-up based in Quebec that originated the New Global Consensus.

Smart Contracting: Self-executing contracts with terms directly written into code, used for automated transactions.

Smart Grid: An electricity supply network that uses digital communications technology to detect and react to local changes in usage.

Social Credit System: A system in China for monitoring and influencing citizen behavior using data.

Supply Chain Transparency: Visibility into the supply chain to ensure accountability and traceability.

Web3: A decentralized internet infrastructure using blockchain technology for enhanced privacy and user control.

Transforming Canada’s Security Framework

The New Global Consensus (NGC) is a comprehensive economic and security framework designed to protect Canadians. Originating from Satyam Studio, a start-up based in Quebec, the NGC aims to address broad structural issues within Canadian democracy, including the erosion of its democratic institutions, corruption, electoral interference, and misinformation, as well as Canada’s lack of effective frameworks to respond to existential threats posed by shifting global power structures, climate change mitigation and adaptation, and mass migration.

The NGC centers on an AI-driven communication protocol to integrate artificial intelligence into its existing energy infrastructure. This protocol enables an advanced smart grid and serves as a blueprint for a real-time AI-driven climate emergency response system called the “Canadian Shield.” It also supports a peer-to-peer energy trading platform, facilitating the decentralization of the power grid and cross-border trading.

Furthermore, the NGC advocates for modernizing global food supply chains using the Internet of Things (IoT), smart contracting, and decentralized financial frameworks based on blockchain technology. By integrating these initiatives within a unified digital infrastructure, the NGC creates a foundation for a new decentralized and democratized internet (Web3), providing legitimacy to raise $4 trillion USD for its ambitious initiatives.

This new internet infrastructure—aimed at countering China’s Belt and Road Initiatives—will modernize cybersecurity while offering a comprehensive framework and ecosystem for innovation in 4th industrial technology. This refers to the convergence of advanced digital technologies, including artificial intelligence (AI), blockchain, quantum computing, and the Internet of Things (IoT), to revolutionize industries, enhance automation, and drive innovation for sustainable and equitable development.

The new internet can offer services such as peer-to-peer energy trading, decentralized finance (DeFi), smart contracting, real-time climate monitoring and response, digital identity verification and authentication, data privacy and security, supply chain transparency, and access to essential services such as basic healthcare, education, and legal resources.

The NGC aims to establish a secure, efficient, and equitable digital future by building political and financial capital to integrate an AI-driven energy management system and a modernized supply chain framework into a single comprehensive digital governance model to counter China’s digital governance model.

To address broad structural issues within Canadian democracy, the NGC seeks to radically expand Canada’s normative power structure to lead a grassroots, equitable, and global farmer-led 4th industrial revolution. Through partnerships and building political capital, the NGC seeks to advocate for good, democratic, and transparent governance. Coupled with strategic infrastructure investments and building transparent democratic institutions, these efforts help introduce climate resilience into vulnerable regions and create stability to prevent mass migration.

This document is structured into two parts. The first section presents an overview of China’s Belt and Road Initiatives and its vision to transform the global world order, highlighting the vulnerabilities for Canadians. It introduces the NGC as a comprehensive framework intended to address these vulnerabilities and put forth a democratic AI-driven governance model to counter China’s Belt and Road Initiative. It seeks partnerships with the United States, India, Sri Lanka, global farmers, Indigenous communities, and other grassroots groups. This section outlines the importance of each partner in securing political capital, establishing a new democratic global power structure, and promoting good democratic and transparent governance models in the Global South.

The second part of the document details the structure of the AI-driven communication protocol, how it communicates with existing infrastructures, and methodologies for machine learning models. It provides examples of how these components work together to create a robust, integrated system that enhances grid stability, modernizes infrastructure, and promotes sustainable development.

These NGC initiatives are designed to empower billions of people in the Global South, accelerate development in both Sri Lanka and India, and modernize infrastructures in North America, including addressing Canada’s housing crisis. The NGC proposes a comprehensive and democratic digital governance model and a framework to address climate change, which includes addressing mass migration.

Today, Canada sits at a crossroads, and the New Global Consensus seeks to radically expand Canada's normative power structure by centering it as a leader in 4th industries and leading a grassroots and equitable 4th industrial revolution. The New Global Consensus calls on Canadians to take control of their nation and choose their own destiny.

AI-Driven Communication Protocol: A Framework for the New Global Consensus

The presented AI-driven communication protocol in this paper introduces methodologies to integrate artificial intelligence into existing energy infrastructure. The framework uses a hierarchical systems model that views the power grid as a network of nested subsystems, each representing different scales of operation, which mirrors the flow of electricity from its generation to consumption. The protocol identifies different scales (Levels) of operation:

• Level 1 nodes: Track individual energy systems within buildings using sensors and provide detailed and granular data for machine learning models.
• Level 2 nodes: Represent individual buildings and community energy infrastructure such as photovoltaic systems or energy storage.
• Level 3 nodes: Cover district-level operations.
• Level 4 nodes: Encompass multiple Level 3 nodes.

At each scale, the multi-level hierarchical protocol aggregates and propagates data from the foundational Level 1 nodes. The protocol enables seamless communication and optimization across the power grid using machine learning algorithms at each operational scale. It supports building-level optimization by integrating Passive House whole-building energy modeling and proposes a mathematical framework for advanced grid management, ensuring optimal performance and stability using predictive modeling. The protocol facilitates a peer-to-peer energy trading platform, crucial for decentralizing the power grid and integrating renewable energy sources.

The protocol’s modular and scalable design enables additional layers of sensors and algorithms to interface with the AI-integrated power grid and serves as a blueprint for the “Canadian Shield,” an AI-driven climate emergency response system. The framework’s flexibility lies in integrating independent components and modules, promoting power grid sovereignty and the development of custom AI-driven climate mitigation/adaptation solutions tailored to local socio-economic, geographic, and cultural challenges. It supports collaboration among research institutions, grassroots organizations, and private enterprises and enables a comprehensive approach towards citizen-led AI legislation to address ethical concerns, including data privacy.

The NGC aims to foster local 4th industrial capacity, support ethical AI and 4th industry legislation, and promote participatory, inclusive, and democratic industrial advancement, positioning Canada as a leader in 4th industrial technologies.

Understanding the New World: China's Digital Governance System

Today, China is leading the world in multiple high-impact sectors: quantum computing, artificial intelligence, 5G technology, renewable energy, e-commerce, fintech, biotechnology, pharmaceuticals, and space exploration (World Economic Forum, 2021).

China’s comprehensive development approach was initiated in 2013, called the Belt and Road Initiative (BRI). With estimated investments ranging up to $1 trillion USD, the BRI aims to enhance regional connectivity, secure supply chains, expand influence, diversify trade routes, and promote the Chinese yuan, fostering economic and geopolitical stability. The BRI has three main components: the Silk Road Economic Belt, a transcontinental passage linking China with Asia and Europe with railways, highways, and energy pipelines; the 21st Century Maritime Silk Road, a sea route linking China’s coastal regions with Asia and Africa; and the Digital Silk Road (DSR).

The DSR promotes China's digital governance and is central to China’s vision to reshape the existing international order. Its digital governance is based on its "Social Credit System," which encompasses various digital and data-driven initiatives aimed at monitoring, surveillance, and influencing citizen and business behavior to enhance social trust and compliance with laws and regulations (China’s Digital Silk Road Initiative, 2021).

Between 2012 and 2020, Chinese-owned companies laid 70,000 kilometers of undersea cables in 27 countries in the Indo-Pacific. By 2025, China is on track to capture 60 percent of the world’s fibre-optic communications market (OrfOnline, 2024). The dominance of its telecommunication companies gives China access to vast amounts of data passing through its cables - raising concerns for its partners (Atlantic Council, 2021).

Between 2017 and 2022, Chinese private and state-owned companies collectively invested nearly US$23 billion in 24 countries in Information and Communication Technology infrastructure to expand 4G and 5G networks within the Indo-Pacific region (OrfOnline, 2024).

China has also strengthened its presence in the global CCTV market as part of its much broader smart-city solution and installed over 6.3 million cameras outside the country - with almost a million in the United States of America (Migliano & Woodhams, 2021). The use of AI-enabled facial recognition software in China’s Social Credit System has also sparked concerns about cybersecurity, surveillance fears, and ethical concerns (Bernot & Smith, 2023).

These concerns are exacerbated by Chinese laws that mandate its organizations and citizens to support, assist, and cooperate with state intelligence work (China Law Translate, 2017). China’s 2021 comprehensive AI regulatory framework stresses the importance of protecting China's state ideology and establishes a central algorithm repository, giving the Chinese Communist Party significant leverage (National Law Review, 2024). Such centralization of AI algorithms can lead to enhanced state surveillance, control over information, and suppression of dissent, as it allows the government to monitor and influence AI development and deployment across various sectors.

Still, through China’s Belt and Road Initiative (BRI), China has fostered good relationships with the Global South. China has capitalized on historic grievances against Western policies, which often prioritized profit over democratic principles, leading to instability and resentment, as local populations perceived these actions as undermining their sovereignty and democratic aspirations.

China’s diplomacy is centered on its Five Principles of Peaceful Coexistence—mutual respect for sovereignty and territorial integrity, mutual non-aggression, non-interference in each other's internal affairs, equality and mutual benefit, and peaceful coexistence. Furthermore, China's Belt and Road Initiative (BRI) has enhanced global connectivity and improved infrastructure in participating countries, including roads, railways, ports, and airports (World Bank, 2019), boosted economic growth in developing countries by creating jobs and improving productivity (Asian Development Bank, 2020), fostered economic integration between Asia, Europe, and Africa while increasing trade volumes and economic cooperation (Economist Intelligence Unit, 2019). Additionally, the Digital Silk Road initiatives and expansion of 5G networks (International Telecommunication Union, 2021) spurred global technological innovation, benefiting various industries worldwide (World Economic Forum, 2021; Asian Development Bank, 2020).

China is successfully positioning itself against the existing international and democratically-led global order and emerging as a Global Power with technological supremacy. China has also proactively secured its supply chains, ports, infrastructure, and access to critical minerals and rare earth elements required for digital transformation (World Economic Forum, 2021).

Many nations, especially in the Global South, look to China for leadership. The ascension of China presents an existential challenge for the United States, Canada, and democracy itself. As we embark on a new Cold War rooted in technological supremacy, it is crucial to reaffirm the importance of democratic values and governance.

The New Global Consensus (NGC) proposes a transformative approach to address these challenges. By fostering a decentralized and democratic web3 and implementing AI-driven governance models, the NGC aims to counter China's expanding influence. This framework promotes transparency, accountability, and citizen participation, ensuring that technological advancements benefit all members of society.

By securing partnerships and support from democratic nations, the NGC can lead the way in modernizing democratic institutions, addressing climate change, and enhancing global cooperation. Through these efforts, the NGC seeks to reinforce the values of democracy and provide a viable alternative to China's authoritarian models of governance, thereby preserving the principles of freedom and equality in the face of rising technological and geopolitical challenges.

Countering China's Belt and Road Initiative: A Citizen-Governed Digital Strategy

The New Global Consensus (NGC) is a comprehensive, citizen-led strategy designed to counter China’s Belt and Road Initiative by promoting a democratic governance model. Originating from Satyam Studio, a start-up based in Montreal, Quebec, the NGC aims to address broader structural issues within Canadian democracy. These issues include unrecognized vulnerabilities in the 21st century, such as climate change, the preservation of democratic values, and adapting to new global power structures.

The NGC seeks to build grassroots and broad political coalitions across Canada to radically expand Canada's normative power structure through democratic reforms. These approaches aim to reinforce Canada’s commitment to democratic values, good and transparent governance, diplomacy, human rights, environmental stewardship, peacekeeping, technological innovation, cultural diversity and inclusion, education and knowledge sharing, and supporting global public health security.

Justified by the profound impact of 4th industrial technologies and their potential to transform every aspect of Canadian life, the NGC calls for a national platform to empower Canadians to decide on their own future. This platform will facilitate high-level integration, collective learning, and public engagement on the benefits and challenges of these technologies.

With political support in Canada, the NGC seeks to establish a new technological and security partnership with the United States of America, India, Sri Lanka, global farming communities—especially smallholding farmers representing nearly 1 billion individuals (Food and Agriculture Organization of the United Nations, 2021)—and Indigenous populations worldwide, numbering around 476 million (World Bank, 2021). The proposed political alliance aims to unite approximately 2.5 billion inhabitants, providing significant legitimacy to raise the $4 trillion USD needed for the development of NGC projects.

The NGC’s $4 trillion USD investments, acting as a global economic stimulus, will focus on large-scale climate mitigation and adaptation infrastructures, such as funding a globally connected power grid integrating the proposed AI-driven communication protocol and expanding 5G technologies. It will also include modernizing agricultural supply chains and investing in robust institutions and transparent, democratic governance. The NGC, through its investments, will support grassroots and community-led 4th industries and help build local knowledge capacity and industry. The collaborative development of a decentralized internet with a democratic and transparent governance framework can also deliver streamlined digital educational and healthcare infrastructures.

In summary, the NGC is an ambitious framework to modernize Canada’s security, address climate change, and strengthen democratic governance across the world to counter China. The NGC encompasses:

• Financial backing with $4 trillion USD in investments.
• A framework to promote economic innovation and modernization of critical infrastructure.
• A framework to promote ethical and inclusive 4th Industrial Revolution.
• Mobilizing political capital from a coalition of 2.5 billion people, particularly Indigenous populations and smallholding farmers, to advocate for democratic governance models globally.
• A framework to address climate change.

United States of America: A Strategic Partner in the New Global Consensus?

China’s Belt and Road Initiative challenges the United States-led global democratic rule-based order. Despite the United States' strong commitment to promoting democratic values and human rights globally, it has struggled to effectively counter China’s expanding influence and strategic initiatives.

The New Global Consensus (NGC) offers a strategic platform for the United States to promote democratic governance reforms, transparency, and anti-corruption measures, helping retain its global leadership position.

Historically, the U.S. has played a crucial role in shaping the international order, but current geopolitical dynamics necessitate innovative approaches to maintain its influence. By providing a clear direction and robust framework, the NGC aims to address America’s existential crisis and serve as a counterbalance to authoritarian models of governance, reinforcing democratic principles worldwide (U.S. Department of State, 2021). The NGC supports democratic values and proposes specific initiatives to modernize infrastructure, enhance cybersecurity, and promote sustainable development.

Through the NGC, the United States can support regional stability, particularly in areas vulnerable to geopolitical tensions such as the Indian Ocean and South Asia, while also strengthening its own sovereignty and democracy. The NGC framework accelerates innovation in artificial intelligence, renewable energy, and smart grid technologies—critical for modernizing America (Brookings, 2021; CFR, 2021).

The proposed technology, security, and economic partnership can enhance the collective security framework and promote peacekeeping efforts while also establishing a digital governance structure rooted in democratic values.

While the NGC provides a pathway for the United States to counter China’s growing influence and technological supremacy, the United States’ technological, economic, and military might are crucial for creating a robust framework, providing legitimacy, and ensuring the successful implementation of the NGC’s initiatives. Through its mutual benefits, the NGC, with the United States as a founding member, can help secure support from democratic nations, lead the way in addressing climate change, enhancing global cooperation, and reinforcing the principles of freedom, equality, and democracy in the face of rising technological and geopolitical challenges.

India: A Strategic Partner in the New Global Consensus

India is the world's largest democracy. Its alignment with democratic principles and active participation in international forums have helped strengthen the global democratic order. Relationships between the United States and India have seen significant growth, with bilateral trade increasing and numerous dialogue mechanisms established to enhance trade and investment (Kapila, 2020; U.S.-India Business Council, 2021). This economic partnership not only boosts the economies of both countries but also integrates India more closely with the global economic system led by the U.S. (Council on Foreign Relations, 2021).

The two nations have also seen significant defense and industrial collaboration. The launch of the U.S.-India Defense Acceleration Ecosystem (INDUS-X) in June 2023 focuses on fostering innovation in defense technologies. Similarly, agreements like the one between General Electric (GE) Aerospace and Hindustan Aeronautics Limited (HAL) to manufacture GE F-414 jet engines in India help enhance indigenous defense production. Key agreements like the Logistics Exchange Memorandum of Agreement (LEMOA) and joint military exercises enhance logistical support and operational reach for both nations’ militaries (U.S. Department of Defense, 2023). Both nations have committed to bolstering counter-terrorism and law enforcement cooperation.

India's ideological alignment with the United States and the New Global Consensus is crucial to promote democracy and counter threats, particularly from China’s authoritarian models of governance.

India also has a rapidly growing economy, a large market, and a substantial labor force, which are attractive for Western investments and companies. Through the NGC, India can also sponsor and promote anti-corruption measures and good, transparent democratic governance, which are crucial for sustainable development. This aligns with India's ongoing efforts to improve governance and foster economic growth. Furthermore, 42% of India’s population work in its agricultural industry, making India critical for securing global farmers' political support.

Given the United States' and India's positions as leading global economies, a strategic economic, technology, and security partnership between the two, including Canada and Sri Lanka, can ensure a robust global democratic power structure. This partnership can help counter authoritarian models of governance and reinforce democratic principles worldwide (U.S. Department of State, 2021). By integrating India as a key partner and founding member of the New Global Consensus, the NGC can accelerate India’s ascent through strategic infrastructural investments and ensure it remains a cornerstone of global democratic values while also strengthening the economies and protecting the sovereignty of its partner nations.

Sri Lanka: A Strategic Partner in the New Global Consensus

The New Global Consensus with Sri Lanka and India in the Indian Ocean, along with the United States and Canada, can transform this alliance into a significant geopolitical power. With support from global farmers and Indigenous communities, the NGC bloc can act as a hegemony, balancing regional power dynamics and ensuring a stable, democratic influence in a crucial area. For Sri Lankans, membership in the NGC, especially as a founding member, can accelerate the nation's development, positioning it as a hub for 4th industrial technology and democratic governance. Additionally, Sri Lanka’s Hambantota Port is central to China’s Belt and Road Initiative, and the people of Sri Lanka hold strategic leverage to potentially undermine China's strategic maritime and economic ambitions (Colombo Telegraph, 2024).

Today, Sri Lanka faces significant structural barriers. Corruption at various levels of government undermines public trust and diverts resources away from critical development needs, impacting its healthcare and educational systems and limiting public investment and social programs. The nation's foreign exchange shortages impact its ability to import essential goods, including fuel and medical supplies.

Sri Lanka is highly vulnerable to climate change, including rising sea levels, extreme weather events, and changing rainfall patterns, which impact agriculture, water resources, and its power sectors dependent on hydropower. Food security amidst economic and environmental challenges remains a critical issue. Outdated farming practices, infrastructure, and limited access to markets affect agricultural productivity and the livelihoods of farmers.

Political instability, frequent changes in government, and its inability to address historical ethnic tensions, particularly between the Sinhalese majority and Tamil minority, continue to affect social cohesion and national unity, leading to policy uncertainty and affecting investor confidence and long-term planning.

Sri Lanka’s over-dependence on China for loans, investments, and trade has raised concerns about its economic sovereignty. In 2017, China Merchants Port Holdings Company Limited (CM Port) acquired a 70% stake and a 99-year lease on Hambantota Port in the south of the island. In 2023, Sri Lanka approved a $4.5 billion oil refinery at Hambantota port by China's Sinopec, further consolidating China’s influence in Sri Lanka. Sri Lankan leaders have also greenlighted Chinese naval vessels, including submarines, to dock at Sri Lankan ports.

Sri Lanka’s current economic reforms aimed at improving governance, managing debt, and enhancing private sector investment align with the vision of the New Global Consensus, which can help accelerate these transformations. It has a thriving start-up culture, and the nation has a proactive commitment to sustainability and climate action, including the Sri Lanka Carbon Crediting Scheme (SLCCS) and initiatives like mangrove restoration and reforestation projects aiming to restore 10,000 hectares by 2030 (UNEP, 2024). These reforms align with the vision of the New Global Consensus (World Bank, 2024).

The New Global Consensus offers a pathway for Sri Lankans to choose their own destiny. Through its partnership with the NGC, Sri Lanka can secure significant investments to modernize its infrastructure and democratic institutions. It supports democratic reforms and calls for the restoration of judicial and parliamentary independence and a free press, as well as a national inquiry into corruption and addressing human rights violations, including establishing a truth and reconciliation commission.

The National Inquiry should be comprehensive, seeking to tackle corruption and focus on economic mismanagement, especially regarding large-scale projects such as Magampura Mahinda Rajapaksa Port, Mattala Rajapaksa International Airport (MRIA), and Mahinda Rajapaksa International Cricket Stadium, which used both Chinese investment and contractors but only bear the Rajapaksa namesake. If the National Inquiry shows widespread corruption and mismanagement of Sri Lankan assets, it provides legitimacy for Sri Lanka to renegotiate its port deal to ensure Sri Lanka’s strategic autonomy remains with Sri Lankans.

Through this partnership, the New Global Consensus can significantly accelerate Sri Lanka’s ascent to developed nation status. By integrating advanced technologies, promoting sustainable practices, and enhancing governance, Sri Lanka can become a center for diplomacy, governance, renewable energy, and artificial intelligence. Sri Lanka's strategic location, commitment to sustainability, proactive carbon credit initiatives, economic reforms, and advancements in technology and education make it a valuable partner for the New Global Consensus and can serve as a model nation for other Global South nations.

Rural and Indigenous Alliance

Building political consensus in Canada, the United States, Sri Lanka, and India, along with forming alliances with rural and Indigenous communities, is central to the proposed comprehensive security framework to promote good democratic and transparent digital governance. Despite holding little assets or political power, global farmers and Indigenous populations are essential to modern civilization. Smallholder farmers, who own and work on less than a hectare of land, are crucial to global food security, producing an estimated 70-80% of the world's food. Yet, smallholder farmers face numerous challenges, such as limited access to markets, financing, modern agricultural technologies, and extension services, which can affect their productivity and economic stability.

Indigenous communities and smallholder farmers often contend with significant economic and social challenges. They frequently experience low income levels, limited access to markets, and insufficient resources, which collectively hinder their economic stability and development. A large percentage of smallholder farmers live below the poverty line. In many developing regions, such as Sub-Saharan Africa and South Asia, a significant portion of smallholders earn less than $1.25 a day, placing them in extreme poverty (Food and Agriculture Organization of the United Nations [FAO], 2015; International Fund for Agricultural Development [IFAD], 2013).

Additionally, these communities are highly vulnerable to the impacts of climate change, with environmental changes threatening their traditional ways of life and food security (Intergovernmental Panel on Climate Change [IPCC], 2014; United Nations Department of Economic and Social Affairs [UN DESA], 2009). The intersection of these factors contributes to the ongoing marginalization and vulnerability of Indigenous populations (FAO, 2010; United Nations Permanent Forum on Indigenous Issues [UNPFII], 2009). These communities are also key stakeholders in climate mitigation and adaptation strategies due to their crucial role in managing vast land areas that directly impact soil health, water usage, and carbon sequestration.

In South America, the NGC can gain broad political support in nations such as Guatemala, Bolivia, Peru, and Mexico, where significant portions of the population identify as Indigenous: Guatemala at 41% (6.7 million), Bolivia at 47% (5.5 million), Peru at 25.7% (8.3 million), and Mexico at 21.5% (27.5 million). The agrarian and Indigenous populations in these regions hold significant political capital and represent a crucial demographic for advocating democratic reforms. Additionally, they can help build support for joining as members of the new alliance (FAO, 2010; UN DESA, 2009).

Similarly, in South Asia, farmers represent roughly 40% of the total workforce in India, Pakistan, and Bangladesh. In addition to securing political support for democratic reforms, farmers can play a powerful role in uniting the subcontinent. While agrarian and Indigenous populations are often marginalized and lack political power, the NGC’s strategic vision seeks to elevate and empower these communities to fight for democratic governance and improve living standards for all (FAO, 2015; IFAD, 2013; IPCC, 2014; UNPFII, 2009).

The NGC proposes three major initiatives to secure agrarian and Indigenous populations' partnership:

1. The modernization of power grids and the integration of AI-driven communication protocols with a peer-to-peer energy trading platform to generate community wealth in rural and Indigenous areas.
2. The integration of artificial intelligence into existing and new power infrastructure, requiring the expansion of 5G technology, helping connect farmers and Indigenous communities to a decentralized internet.
3. Blockchain and AI solutions for modernizing supply chains to enhance transparency and efficiency by automating tracking, verification, and reconciliation of deliveries and payments. These technologies also improve data accuracy, reduce fraud, enable real-time monitoring and decision-making, and facilitate better coordination and organization among smallholder farmers and Indigenous communities. By supporting the formation of cooperatives and optimizing resource management, these innovations lead to substantial operational improvements and cost savings.

For example, Walmart Canada created a blockchain-based freight and payment network, recognized as the world's largest full-production blockchain solution for industrial application (DLT Labs, 2019). While Walmart Canada modernized only a segment of its supply chain, the technology saved millions of dollars annually. Deployment of the blockchain-based system reduced administrative costs and improved the efficiency of payment processing, cutting transportation costs by 20% (Hyperledger, 2019).

The NGC's vision to modernize global food supply chains and propose smart contracting infrastructure will enhance transparency and integrity, optimize processes, reduce costs, and improve overall efficiency. These substantial savings will be transferred back to the farmers, Indigenous populations, and their communities.

Agri-tech solutions integrated directly into a proposed Web3 framework also provide access to decentralized digital services, including digital governance and finance tools. The agrarian and Indigenous communities can access financing, as well as legal and political power through the NGC, which will be transformative. Digital forums through a decentralized internet allow for the sharing and exchange of data, knowledge, and methodologies, including access to Fourth Industry technologies such as drone technology for precision agriculture.

These initiatives support the development of community-owned digital assets, ensuring that those who contribute to these innovations can also partially own them. Furthermore, through Web3, improved education and healthcare services can also be accessed. Altogether, these advancements empower smallholder farmers and Indigenous communities, enhance agricultural productivity, and promote sustainable development in rural areas.

This partnership between the NGC, global farmers, and Indigenous populations is critical in advocating for good, transparent, and democratic governance, leveraging the strengths and resources of rural and Indigenous areas.

Grassroots, creative and promoting regional Fourth Industry capacity

The New Global Consensus (NGC) envisions a new decentralized internet to promote ethical and equitable Fourth Industrial Revolution initiatives and regional Fourth Industry capacity building. The AI-driven communication protocol presented in this paper provides a framework to integrate artificial intelligence into existing infrastructure, enabling the creation of a digital twin of entire nations. This digital twin offers real-time insights into total energy use across cities, districts, and regions, as well as detailed energy consumption for individual buildings and their subsystems.

The protocol is modular and scalable, allowing independent layers of sensors and algorithms to interface with the system. The framework acts as an Integrated Digital Hub (IDH), allowing multiple public and private stakeholders to collaborate in developing Fourth Industry technology-driven solutions to critical challenges, incorporating local Indigenous knowledge and contextualizing solutions to regional, cultural, and socio-economic contexts. These technologies represent the synergy of emerging technologies working together, including artificial intelligence, big data and analytics, blockchain technology, and the Internet of Things (IoT).

The NGC’s initiatives support regional Fourth Industry capacity and promote region-specific digital solutions and initiatives that emphasize the involvement and leadership of local communities in driving these projects. This includes grassroots development of blockchain-based transparent contracts, decentralized and accessible financial institutions, and free or low-cost education, legal, and basic medical services.

The ability to provide key public and essential services through a digital interface using Fourth Industry technologies, coupled with state-of-the-art ethical legislation, aims to position New Global Consensus-supported private enterprises as leaders in 4th industrial technology, further giving legitimacy to raise $4 trillion USD.

Through the legitimization of the New Global Consensus and its funds, it can support a global revival in arts and culture, including verification and authentication programs to protect creative jobs. In an era where artificial intelligence threatens to displace human creativity, there is growing anxiety among artists and creators about job security and the preservation of authentic, human-generated art (Canada Council for the Arts, 2021). The NGC can provide funding to address these concerns, ensuring that creative industries remain vibrant and secure.

The New Global Consensus (NGC) represents a comprehensive framework to modernize Canada’s democracy and security in the 21st century. It aims to address critical vulnerabilities for Canadians by advocating for a new partnership among the United States of America, India, Canada, Sri Lanka, global farmers, Indigenous communities, and other grassroots organizations.

Central to the NGC is an AI-driven communication protocol presented in this paper, which allows the integration of artificial intelligence directly into existing infrastructure. Additionally, the modernization of food supply chains and their integration into the same digital infrastructure provide legitimacy to develop a new decentralized internet and raise funds.

The next section outlines the detailed structure of the AI-driven communication protocol, its various modes of communication, and how it integrates with existing and new infrastructures to enhance grid stability, promote sustainable development, and address the pressing challenges of our time.

A Globally Connected Power Grid

There is an urgent need to transition to renewable energy sources. The development of a globally connected power grid spanning regions and continents would revolutionize the way we produce, distribute, and consume energy while providing platforms to facilitate global cooperation for the efficient harnessing and sharing of solar power across borders.

The development of High-Voltage Direct Current (HVDC) transmission and technological advancements in High-Voltage Alternating Current (HVAC) systems now allow for the transfer of electricity across thousands of kilometers with minimal energy loss, enabling grids to span continents (Watson et al., 2020). Smart power grids also facilitate the large-scale integration and distribution of renewable energy sources such as solar and wind farms. By leveraging AI-driven insights and advanced grid management algorithms, smart grids can optimize energy flows, balance the variability and intermittency of renewable sources across a wide area, and enhance the overall resilience and efficiency of the power grid.

There are currently several major initiatives to develop a globally connected grid. The "One Sun One World One Grid" (OSOWOG), supported by India, France, the United Kingdom, and various other international agencies, including the International Solar Alliance (ISA), aims to connect South Asia with the Middle East and Southeast Asia, with a long-term vision to expand globally.

The Global Energy Interconnection (GEI) is proposed by China to connect energy-rich regions with energy-deficient ones across continents using ultra-high voltage grid technologies, forming part of the Belt and Road Initiative (BRI) (International Energy Agency [IEA], 2019). Similarly, a German-led consortium aims to supply clean solar and wind power from the deserts of the Middle East and North Africa to meet European electrical demands (Deutsche Welle, 2020). These initiatives reflect global efforts to enhance regional connectivity and promote sustainable energy development through large-scale infrastructure projects.

The International Energy Agency, in their 2023 report "Electricity Grids and Secure Energy Transitions," stresses the importance of modernizing power grids: “Grids need to both operate in new ways and leverage the benefits of distributed resources such as rooftop solar, and all sources of flexibility. This includes deploying grid-enhancing technologies and unlocking the potential of demand response and energy storage through digitalization” (International Energy Agency [IEA], 2023). Modernizing the power grid in the United States alone could save the country up to $50 billion annually by improving efficiency and reducing energy losses (Smart Electric Power Alliance [SEPA], 2019).

Grid-enhancing technologies, including the integration of data-driven solutions, optimize power grid performance. Studies such as NREL's (Rose et al., 2022) modeled the impact of Cross-Border Electricity Trade between India and Sri Lanka with a 500-MW high voltage direct current transmission link connecting the island. This link could generate annual production cost savings of USD 180 million due to grid optimization, improve power system operations, and reduce Sri Lanka's annual production costs by 35% with a good payback period of under five years.

There are significant technical challenges and complexities, including high initial investments for infrastructure, technology integration, and maintenance, lack of skilled labor, as well as coordinating between different countries and their regulatory interests and policies. The International Energy Agency (IEA) (2023) estimates that to meet national climate targets globally, grid investment needs to nearly double by 2030 to over USD 600 billion per year. Additionally, there is a need to protect the grid from cyber-attacks, especially given its extensive and interconnected nature.

Furthermore, there is a lack of standards and communication protocols to enable the smart grid to communicate with itself. The IEA stresses the need to build in future flexibility by ensuring interoperability of all the different elements of the system. The paper below presents an AI-driven communication protocol to address this critical barrier.

The Structure and components of the AI-driven communication protocol

The structure of the AI-driven communication protocol mirrors the flow of electricity from its generation to consumption. Consider how Hydro-Québec manages electricity transmission over long distances: High-voltage electricity, often transmitted at 735 kV, is generated from hydroelectric plants in northern Quebec. This high voltage is necessary to minimize energy loss over long distances to urban centers like Montreal (Hydro-Québec, 2024; Hydro-Québec Transénergie et équipement, n.d.).

As high-voltage electricity reaches the city, it first enters primary substations where the voltage is reduced from the high transmission voltage to medium voltages, around 230 kV, that are easier to manage within urban infrastructure. The medium-voltage electricity is distributed through a network of underground or overhead lines throughout the city. Local transformers further step down the voltage to levels used by consumers, typically 120/240 volts for residential use. This multi-stage process is essential to ensure efficient and safe delivery of electricity to end users (Hydro-Québec, 2024; Hydro-Québec Transénergie et équipement, n.d.).

Similarly, the proposed AI-driven communication protocol mirrors the flow of electricity and employs a hierarchical structure with its various scales of operation. It defines important nodes within this structure that represent different levels of the energy management system, from individual building systems to district-level operations. Each node, acting as an agent, has its set of strategies and objectives. The interactions between these agents can be modeled using game theory, where each agent seeks to optimize its utility function while considering the actions of other agents. This hierarchical approach ensures efficient data management and optimization across all levels of the energy grid.

Understanding Modules: Scales of operation

The proposed AI-driven communication protocol identifies all important nodes within the network and classifies each node according to different scales of operation across power grid energy management. For each scale of operation, the protocol outlines machine learning methodologies to optimize grid performance.

The first scale of operation—Level 1 nodes—includes individual energy systems within a building, representing energy end-uses such as lighting, plug loads, heating, cooling, and hot water systems. The proposed protocol uses the Internet of Things (IoT), enabling real-time energy management (RTEM) to track Level 1 nodes' energy use. IoT devices are interconnected digital devices that track, communicate, and exchange data. RTEM systems monitor energy use in real-time using IoT data to optimize efficiencies.

The second scale represents Level 2 nodes, which include individual buildings or other stand-alone community energy systems such as photovoltaic (PV) or energy storage (ES) systems. The RTEM data from Level 1 nodes are aggregated, summarized, and then propagated to their respective Level 2 nodes. Community generation (PV), energy storage, and other auxiliary energy systems have their own devices tracking their performance.

Similarly, Level 3 nodes represent the district level, serving multiple Level 2 nodes or large stand-alone buildings such as campuses. Level 3 nodes function similarly to local distribution panels within the transmission network. RTEM data from Level 1 nodes, along with data tracked from other Level 2 nodes, are continuously aggregated and summarized to form a foundational dataset. This dataset is critical for the system's overall performance, providing granular and detailed data at specified time intervals.

A Level 4 node will use data aggregated, summarized, and propagated from its respective Level 3 nodes. Ultimately, this hierarchical data structure ensures that each level benefits from the detailed, foundational data collected at the lower levels, providing clean, detailed data for machine learning models.

A Level 3 node represents a microgrid or a module. By microgrid, it means a Level 3 node can optimize the grid—even if it involves just a few buildings. This allows individual energy systems and buildings to communicate within the module, utilizing data-driven insights to automatically adjust and perform other functions to optimize the power grid for performance and stability.

Additionally, while a module can be independent (off-grid), the AI-driven communication protocol enables seamless interoperability and data exchange between modules. Multiple Level 3 nodes can connect under a Level 4 node, enabling different modules to communicate and optimize energy resources amongst each other. This hierarchical structuring continues with multiple Level 4 nodes optimized through a higher-level node using foundational data propagated from RTEM data.

The modular and scalable algorithm supports cross-border electricity trading and establishes a foundation for a globally connected power grid. It also enables the integration of different algorithms and sensors while still allowing the grids to communicate with each other, which helps ensure grid or regional sovereignty.

Understanding scalability: Modes of communication

Along with the foundational dataset from Level 1 and 2 nodes propagated to higher nodes, the proposed AI-driven communication protocol enables at least three modes of communication that integrate machine learning models: Internal Optimization, Bi-Directional Communication, and Fault Detection and Diagnostics.

The first mode of communication optimizes internal processes. A Level 2 node (e.g., a single-family residence) independently optimizes the functioning of all Level 1 nodes within its purview. Examples of Level 2 optimization include adjusting the heating, ventilation, and air conditioning (HVAC) system in response to occupancy patterns and real-time weather data, thereby maximizing energy efficiency and comfort while minimizing energy consumption. This paper proposes a methodology to develop steady-state whole building energy modeling for optimization of Level 2 nodes.

Level 3 nodes and higher use the Advanced Energy Management System (AEMS) to operate autonomously and optimize internal processes. The AEMS can dynamically distribute electricity among connected buildings and systems to ensure that demand is evenly spread out, identify ideal charging and discharging times for energy storage systems, and manage distributions of community power generation. The AEMS protocols are based on the machine learning methodologies presented in this paper.

The second mode of communication enables interaction between nodes but constrains the communication to those directly above or below in the hierarchy, allowing for bi-directional communication. This means, for instance, a Level 2 node can only communicate with its respective Level 3 node or its Level 1 nodes, but not with another Level 2 node within the same Level 3 node or another Level 3 node. These constraints are important to structure the flow of information, as they allow for clear isolation and interfacing with data within the hierarchical structure. This structured flow of information ensures optimal management and scalability of the system.

Bi-directional communication allows for advanced grid interactions, such as peak shaving, which reduces energy consumption during peak demand periods to alleviate strain on the grid. Bi-directional communication can override internal optimization and re-optimize the node to meet directives sent by Level 3 nodes. A Level 3 node can send signals to each Level 2 node to reduce energy consumption by a specified percentage.

The hierarchical structure and separate modes of communication are designed to create a clear flow of communication, allowing for easy integration of priority functions, isolation, and optimization of specific regions. A key feature of the protocol is its modularity and scalability.

The third mode of communication aids in fault detection and diagnostics. It proposes using sensors to track grid components and circuits to identify overloads. Additionally, this mode can detect conditions such as equipment overheating, gas leaks, voltage fluctuations, and communication network issues. Sensors can monitor the health of various grid components, including transformers, breakers, and switches, to preemptively identify and address potential failures. This comprehensive approach ensures timely maintenance and enhances overall grid reliability and safety.

The isolation of data and regions enables additional layers of sensors and data to connect with foundational data, allowing for robust, adaptable solutions tailored to various needs. The AI-driven communication protocol can be scaled either vertically or horizontally. Vertical integration represents the addition of different layers of sensors, data, and algorithms to interface with the foundational model, while horizontal scalability refers to integrating different modules and allowing separate and sovereign power grids to communicate with each other.

This section provides an overview of the structure of the AI-driven communication protocol and its communication modes. This structure enhances the ability to manage and adapt different parts of the grid independently, ensuring efficient overall grid management while maintaining stability. In the next section, the paper will further identify each component of the protocol and put forth methods for machine learning models.

Level 1 Nodes: Individual Building’s subsystems

Level 1 nodes track the energy end use and other data from individual building energy components such as appliances, heating systems, cooling systems, lighting systems, plug loads, ventilation systems, and energy storage systems, including charging stations and photovoltaic (PV) systems meant solely for the building.

Level 1 nodes are fundamental components of the AI-driven communication protocol, providing foundational data that gets propagated to the higher levels of the protocol. Level 1 nodes are tracked by the Internet of Things (IoT), which represents a network of physical devices embedded with sensors, software, and other technologies that enable these devices to connect, collect, and exchange data over the internet or other communication networks. An IoT device, such as a smart thermostat connected to the home's heating system, can track significant data, including occupants' behavior, set-point, and indoor temperatures. These sensors are part of a real-time energy management (RTEM) system, typically installed at the electrical panels. RTEM data provides accurate and granular real-time data on energy consumption by end-use type (Massachusetts Institute of Technology, 2021). RTEM programs are incentivized by various initiatives, including the New York State Energy Research and Development Authority (NYSERDA), which states, “RTEM systems continuously collect live and historical performance data through a cloud-based or on-site system. Sensors, meters, and other equipment, along with data analytics and information services, show how your building or facility is performing at any point, in real time" (NYSERDA, n.d.). Similar initiatives are led by BC Hydro's Continuous Optimization Program and Alberta's Custom Energy Solutions, which provide incentives for energy efficiency upgrades, including the installation of real-time energy monitoring systems. Existing programs that encourage RTEM installation provide pathways to adjust terms to request access to data.

The data from the RTEM provides foundational data that enables the integration of artificial intelligence to optimize power grids to ensure grid resilience and stability. Data from Level 1 nodes is aggregated, summarized, and propagated to the next level. This process continues up the hierarchy, with data being aggregated, summarized, and propagated at each level. Ultimately, at the highest node, the system can gain a high-level overview of the power grid's performance while also having detailed information on the performance of individual buildings and their components using machine learning and other predictive algorithms.

A Note on Benchmarking Programs and Their Data: The greenhouse gas emissions of over 250 municipalities and their buildings in Canada are tracked using the Partners for Climate Protection (PCP) Milestone Tool. This web-based resource supports PCP members in their greenhouse gas emissions reduction activities by providing a framework to quantify, monitor, and manage these emissions.

The EnerGuide Rating System (ERS), administered by Natural Resources Canada, and the Novoclimat program, administered by Transition énergétique Québec, both provide homeowners with a comprehensive report on their home's energy performance, helping to identify areas for improvement and increase overall energy efficiency. Along with programs such as Passive House, ERS and Novoclimat also provide datasets for machine learning models.

Many machine learning protocols attempt to develop predictive building energy models using benchmarking data; however, the quality of data is dependent on user inputs and often requires substantial effort to clean and sort the data (Eskwelabs, 2023; NYSERDA, n.d.).

The proposed AI-driven communication protocol instead uses RTEM data, which solves these concerns by providing granular and detailed data on energy end use. The ability to process real-time data effectively is essential for predictive analytics. This requires robust data processing infrastructure and advanced algorithms to handle the large volumes of data generated by RTEM systems (IndaLabs, 2023), while benchmark data typically calculate energy and water use per month.

Level 2 Nodes: Understanding Building and Community Energy Systems

Level 2 nodes are fundamental for understanding the energy consumption patterns of entire buildings and the performance of other community energy systems. They represent individual buildings, auxiliary systems (such as traffic lights), and community photovoltaic (PV) generation and energy storage systems.

Level 2 nodes for buildings depend on the granular data collected from IoTs and sensor feeds (based on real-time energy management) from Level 1 nodes. RTEM data is used in a Building Energy Management System (BEMS). The aggregated, summarized, and propagated data from Level 1 nodes to Level 2 nodes for BEMS are then similarly aggregated, summarized, and propagated to Level 3 nodes, enabling Advanced Energy Management Systems (AEMS).

While real-time energy management systems emphasize monitoring and managing energy usage in real-time, their objective is localized to providing immediate insights and recommendations for adjustments in energy consumption. The actual implementation of these adjustments is typically managed by building energy management systems (BEMS) or other control systems. RTEM systems are a component of BEMS, which can monitor and manage overall energy consumption but with a broader focus, acting as a centralized platform for energy management and optimization. It includes comprehensive systems used to monitor, control, and optimize the energy consumption of buildings. BEMS enables high levels of automation and integration of machine learning by controlling HVAC, lighting, and other systems—maximizing efficiency while maintaining thermal comfort for occupants. BEMS and AEMS are examples of the first mode of communication which optimizes internal processes.

BEMS can integrate whole building energy optimization by monitoring, controlling, and optimizing various energy systems within a building. Siemens Building Technologies, Johnson Controls, and Honeywell Building Technologies are some industry names that offer BEMS technology.

Open-Source and Subscription-Based Whole Building Energy Modeling Tools:

There are various open-source and subscription-based whole building energy modeling tools available. Programs like eQUEST and EnergyPlus employ complex computational methods for dynamic and hourly simulations of building energy. The AI-driven communication protocol proposes achieving similar computational accuracy and precision using simplified equations. Tools like the Passive House Planning Package (PHPP) are more accessible, using Excel to perform steady-state calculations to estimate energy use on a monthly basis. PHPP is a holistic whole-building energy program that captures interactions between different energy systems, such as heat gain from passive solar gains. PHPP has been validated through comparative studies and real-world applications, demonstrating its reliability and accuracy for designing energy-efficient buildings (Feist, Peper, Gorg, & Schleicher, 2005; Passive House Institute, n.d.; Lawrence Berkeley National Laboratory, 2013).

Building Energy Management Systems (BEMS) can integrate methodologies and tools from these whole building energy modeling programs to adjust and optimize energy performance. By leveraging real-time data from RTEM systems and using modeling tools, BEMS can implement effective energy optimizations and control strategies.

Several studies, including Chandrasekaran et al. (2023) and Muhammad et al. (2023), have employed machine learning algorithms for predicting building energy consumption. These studies demonstrate the application of machine learning algorithms using RTEM data for effective prediction and management of building energy consumption and its systems. In the next section, the paper will present a framework to develop a machine learning protocol for building energy optimization and district-level energy optimization.

Level 3 and higher Nodes: Microgrids and Modules

Level 3 nodes represent microgrids or modules within the AI-driven communication protocol. A microgrid is a localized group of electricity sources and loads that can operate independently or in conjunction with the main grid. Level 3 nodes can manage and distribute electricity among connected buildings and systems within their purview. These nodes use an Advanced Energy Management System (AEMS) to operate autonomously and optimize internal processes. AEMS can dynamically distribute electricity, identify ideal charging and discharging times for energy storage systems, and manage the distribution of community power generation. Level 3 nodes utilize real-time data from lower-level nodes (Level 1 and Level 2) to make informed decisions.

Level 3 nodes are referred to as modules because they can function independently within a larger system. This modularity allows for localized control and optimization, providing flexibility and resilience to the overall power grid.

Level 4 nodes function similarly to Level 3 nodes but operate at a higher hierarchical level, viewing Level 3 nodes as localized groups of electricity sources and loads. In essence, Level 4 nodes are responsible for optimizing the performance of multiple Level 3 nodes. They employ the same mathematical equations, processes, and load-balancing strategies as Level 3 nodes but focus on managing and optimizing the aggregated performance of several Level 3 microgrids.

Load balancing refers to the process of matching electricity generation with electricity consumption in real-time to maintain system stability, efficiency, and reliability. In an electric grid, the demand for electricity fluctuates throughout the day due to factors such as weather, time of day, and activity, but the grid must remain balanced. When load balancing in the electrical grid is not maintained, it can lead to frequency deviations, which can have several consequences. Frequency in an electrical system refers to the rate at which alternating current (AC) changes direction per second.

In most power systems, the frequency is kept constant, and the electrical grid is typically maintained at a constant level (e.g., 50 Hz or 60 Hz) to ensure the stability of the grid. When there is an imbalance between generation and consumption, the frequency can deviate from this standard, introducing grid instability, causing fluctuations in voltage levels that can damage electrical equipment and lead to power outages and blackouts (U.S. Department of Energy, 2019).

Both Level 3 and Level 4 nodes utilize real-time data and advanced energy management systems to ensure efficient and stable grid performance. This hierarchical approach allows for localized optimization while maintaining the flexibility to manage larger, interconnected sections of the power grid.

Similarly, the hierarchy can continue to Level 5, Level 6 nodes, etc., providing significant flexibility in how power grid infrastructure and electricity sources and loads can be arranged and managed. As the mathematical equations seek to balance the load, the higher-level nodes eventually connect to power plants and provide insight into ramping up or lowering electricity production to balance the grid, enabling real-time response.

Methodology: Machine Learning Protocols for Building Energy Optimization?

This section outlines frameworks for developing machine learning models aimed at optimizing building energy use for Level 2 nodes and optimizing the power grid at Level 3 nodes or higher.

While frameworks for node or grid optimization are detailed here, this protocol integrates existing concepts and technologies that have been empirically tested. Although similar algorithms and frameworks exist, this protocol's modularity and scalability allow for the integration of custom solutions. Additionally, the AI-driven communication protocol provides a robust framework for addressing legislative and ethical concerns, ensuring compliance with regulations and promoting transparency and fairness.

The proposed methodology leverages foundational datasets from RTEM systems, which provide granular and real-time data on energy consumption. Using this data, machine learning models can predict and optimize energy use across different nodes. This approach ensures that the energy management system dynamically adjusts to varying conditions, enhancing efficiency and reducing energy waste. The modular design enables the integration of new technologies and methodologies as they become available, ensuring the system remains state-of-the-art.

In summary, this section details the steps for developing and implementing machine learning models for building energy optimization, emphasizing the protocol's unique features of modularity and scalability that set it apart from existing solutions.

Energy Consumption Calculation Model: Level 2 Nodes

An initial program should aim to survey at least 100,000 buildings with Real-Time Energy Management (RTEM) integrations. The survey scope includes capturing a range of information about the buildings, including their construction date, usage patterns, sizes, locations, orientations, equipment types, and demographic factors such as family type. By collecting data from a large number of buildings, the survey ensures a broad representation of different building characteristics and usage scenarios. This data will be used to train machine learning models to predict energy performance based on these properties, providing a foundation for scalable and accurate whole-building energy modeling.

The primary purpose of this survey is to provide the necessary input data for training machine learning models to identify patterns related to different end uses of energy within buildings, such as lighting, heating, cooling, and other loads.

The proposed machine learning model for whole-building energy analysis calculates total energy consumption (Et) as a weighted sum of various Level 1 components (lighting, plug loads, heating, cooling, hot water, ventilation, appliances, etc.). Initially, survey data and Level 1 RTEM data collected over a few months will help develop predictive machine learning models. The proposed methodology employs multi-regression analysis to determine each energy end-use weight (ω) based on factors like building type and geographical location, and continuously auto-adjusts and improves its accuracy. The error term (?) accounts for the portion of total energy use that is not explained by the other variables.

The Passive House Planning Package (PHPP) is a detailed energy modeling tool used for designing and evaluating the performance of low-carbon-emission buildings. It employs calculations to ensure buildings achieve high energy efficiency, comfort, and reduced ecological footprints through meticulous planning of insulation, windows, ventilation, and airtightness.

The PHPP uses monthly climate data to accurately simulate a building's energy performance over a year, allowing for the assessment of heating, cooling, and other energy needs. PHPP also employs a component-based energy modeling approach, calculating each energy end-use independently, including heating, cooling, ventilation, lighting energy use, plug loads, and miscellaneous loads. This method uses different sets of steady-state equations for each component, ensuring precise and modular energy analysis (Passive House Institute, n.d.; 2005; Lawrence Berkeley National Laboratory, 2013). The proposed AI-driven communication protocol employs a similar steady-state component-based energy modeling approach based on PHPP for machine learning models to evaluate and optimize Level 2 nodes.

Consider Heating Degree Days (HDD), which help approximate heating energy demand. HDDs are calculated as the sum of the differences between the mean daily temperature and a base temperature (usually 18°C or 65°F), summed over the heating season, on days when the mean temperature is below the base temperature. This measure helps estimate the heating requirements of buildings over a specified period, typically used in energy analysis and planning. Similarly, the cooling energy demand can be calculated as a function of Cooling Degree Days (CDD).

To calculate heating energy use as a function of HDD in North America, the following simple equation can be used:

E1=b1+h1×HDD1 (Equation 3)

Where:
- E1 is the energy usage over the period.
- HDD1 is the heating degree days over the period.
- b1 is the intercept or constant, representing the baseload energy usage.
- h1 is the slope of the regression line, indicating the rate of increase in energy -   consumption per HDD.

Similarly, to calculate the cooling energy use as a function of CDD, the following equation can be used:

E2=b2+c2×CDD2 (Equation 4)

Where:
- E2E is the energy usage over the period.
- CDD2 is the cooling degree days over the period.
- b2 is the intercept or constant, representing the baseload energy usage.
- c2 is the slope of the regression line, indicating the rate of increase in energy consumption per CDD.

For lighting energy use, the equation is: 

E3=LPD ×A3×H3 (Equation 5)

 Where:
- E3 is the lighting energy used.
- LPD  is the Lighting Power Density (watts per square foot or watts per square meter).
- A3 is the area of the space being lit.
- H3  is the number of hours the lighting is used.        

These equations enable the estimation of energy consumption based on heating and cooling degree days and lighting power density; using RTEM data that provides granular, and detailed breakdown of energy use by different end-use, they allow for predictive modeling of energy use (GreenBuildingAdvisor, 2014; DegreeDays.net, n.d.; Firstgreen Consulting, 2020). Similar simplified equations to calculate energy use can be used for other building loads.

Taken together, using Real-Time Energy Management (RTEM) data, a simplified steady-state and component-based method like PHPP with weights for correction provides a framework for algorithms to optimize Level 2 nodes. By integrating these data sources with multi-regression analysis for the development of predictive models, it can account for various factors influencing energy consumption. These equations help provide a framework to enable the grid to communicate and optimize performance for Level 2 nodes.

In the next section, the paper presents a Network Diagram to illustrate both the hierarchical structure and the relationships and data flows between different elements, highlighting the integration of machine learning algorithms and predictive modeling for optimizing building energy use.

Network Diagram of The Power Grid?

Consider the district of Park-Extension in Montreal, where there is a mixture of residential, commercial, and school buildings within a Level 3 (district-level) node. Each independent building functions as a Level 2 node and is highlighted in blue (commercial), green (school), and yellow (residential). Community solar generation, installed on the school, is also considered a separate Level 2 node.

Further, note that some residential building nodes have ‘demand response’ capabilities. Typically, the comfort range for cooling is between 23°C and 26°C. Buildings participating in demand response programs enable automatic adjustment of cooling within the comfort range in real-time to shave peak load. In many dense metropolitan cities, cooling drives peak load and demand response strategies are effective in reducing peak load.

In figure 3, approximately half of the residential buildings (yellow) within the highlighted district have Real-Time Energy Management (RTEM) systems. Further, from the 40 residential buildings with RTEM building energy management systems, 9 integrate 'demand response' capabilities. By identifying and categorizing similar building clusters, sampling techniques can be employed to determine the energy performance of each building. During the late 19th and early 20th centuries, Montreal experienced rapid urbanization. Many neighborhoods were developed in a short period, leading to uniformity in building styles and types as developers constructed large numbers of similar buildings to meet housing demands quickly. There are also other various factors, such as zoning and regulation, that lead to similar size buildings with similar mass and orientation. The RTEM data from the 40 residential buildings is sufficient to predict the energy use of the remaining residential buildings.

This approach allows for a cost-effective and scalable development of a smart grid without the need for full-scale deployment of RTEM and IoT in every building. Similarly, the demand response integration adds smart responsiveness and flexibility to the grid, enhancing its efficiency and stability.

A key aspect of the scalability of the modules presented in this paper is the ability to integrate buildings and data without modernizing each building individually. Similar to using sampling methodologies, a sensor can help track the energy consumption of a Level 3 node, even if there are no Level 2 nodes or RTEM data integration. While this may not allow this particular Level 3 node to optimize energy use independently or enable demand response and other functions, it still collects valuable data on the performance of that grid segment, allowing for predictive modeling of that particular segment. Similarly, entire national grids can be integrated at the points where they connect to the smart grid, enabling the selling or buying of electricity between nations. This means that even if a nation's entire electric grid is not modernized or integrated with smart sensors, it can still communicate with the smart grid.

Level 3 Nodes: Mathematical equations for Level 3 Nodes and Higher

This paper proposes mathematical frameworks to optimize Level 3 nodes and higher using concepts in load balancing to enable Advanced Energy Management System (AEMS). Load balancing refers to the process of matching electricity generation with electricity consumption in real-time to maintain system stability, efficiency, and reliability. In an electric grid, the demand for electricity fluctuates throughout the day due to factors such as weather, time of day, and activity, but the grid must remain balanced. When load balancing in the electrical grid is not maintained, it can lead to frequency deviations, which can have several consequences. Frequency in an electrical system refers to the rate at which alternating current (AC) changes direction per second. In most power systems, the frequency is kept constant, and the electrical grid is typically maintained at a constant level (e.g., 50 Hz or 60 Hz) to ensure the stability of the grid. When there is an imbalance between generation and consumption, the frequency can deviate from this standard, introducing grid instability, causing fluctuations in voltage levels that can damage electrical equipment and lead to power outages and blackouts (U.S. Department of Energy, 2019).

At each Level 3 node or higher, the load must balance where the sum of energy consumed at the node must equal the total electricity consumed by all the nodes under Level 3 for a specified time step. The AI-driven protocol is envisioned for an idealized power grid designed to meet its power demand, where power plants connected to the higher nodes use predictive modeling to ramp up or down electricity production to ensure meeting demands and balancing the load. Further, energy storage and other localized generation capacities help shift peak loads and improve grid resilience. In cases where electricity generation cannot meet demand, other Advanced Energy Management System (AEMS) strategies, including extreme measures such as turning off power for low-priority industries or buildings, can help ensure the stability of the grid.

Note, the AI-driven communication protocol uses the following polarity assignment. The equations are written for pre-defined time steps - such as every 5 minutes (the time-step can be optimized for computing capacity required and precision of the AI-driven algorithm). Assume buildings and other auxiliary energy-consuming systems represent positive energy consumption. A photovoltaic solar system produces energy and is assigned negative electricity consumption (see image x below). Similarly, energy storage can be modeled as a discharging (negative) or charging energy system (positive) or 0 when it is neither in charging or discharging mode.

Hierarchical Communication and Control Protocols

The First Mode of Communication: Internal Optimization?

Advanced Energy Management System: Prioritizing Optimization of the Node First In the previous segment, this paper presents a framework for a modular and scalable AI-driven communication protocol for a power grid enabling a peer-to-peer energy trading platform. This framework envisions the power grid as a hierarchical structure with Level 1 nodes representing individual energy systems of a building. These systems' (Level 1 nodes') energy performance is measured using real-time energy management (RTEM). The RTEM data is aggregated, summarized, and propagated to Level 2 nodes. Level 2 nodes represent individual structures, but also community-level solar and energy storage systems, which are monitored using sensors, meters, and data loggers to track data and provide additional data.

The data from Level 2 nodes is then aggregated, summarized, and propagated to Level 3 nodes, and so forth. At Level 2 nodes and higher, propagated RTEM data enables the development of machine learning algorithms. The previous sections presented methodologies for machine learning algorithms to optimize whole building energy consumption. These algorithms are calibrated using an initial sample of 100,000 homes.

This segment expands the methodologies for predictive modeling using the load balancing concept where the sum of energy consumption across all the nodes within the Level 3 district must equal the total energy available at the Level 3 nodes for a certain time-step. The calculation includes any energy available from solar or energy storage for each time interval. Energy storage systems can be in a charging mode, where they function like energy-consuming buildings, or in a discharging mode, where they function as power generation systems. Data across the module is synchronized so measurements are taken at the same time intervals. Integrating real-time energy performance data, weather data, and identifying other characteristics allows for the development of predictive algorithms for an Advanced Energy Management System (AEMS).

Consider the same example of the district of Park-Extension in Montreal. Figure 4 below shows a network diagram for the district. In this arrangement, Level 2 nodes connect directly to the Level 3 node, forming a star topology. This means that each building or system represented by a Level 2 node communicates directly with its Level 3 node, ensuring efficient and direct data transmission.

Similarly, Figure 5 shows energy balance equations for only a partial network. The full network diagram contains over 40 nodes, so the partial diagram, which includes just 8 nodes, is provided for clarity. In this partial network, the total energy use, which sums all Level 2 nodes (PV1, H1, H2, H3, H4, H5, H6, H7) at a specified time-step, must equal the energy transmitted at Level 3 nodes (N1) for the same time-step. If there is excess electricity generation from solar panels, then the Level 3 node produces a surplus, and N1 must still equal the sum of its lower nodes.

At nodes higher than Level 3, a similar energy balance equation provides optimization. Here, the surplus energy production is internally optimized within the higher (Level 4) node, which is more efficient and cost-effective rather than transferring it back into the grid.

Figure 6 visually demonstrates the hierarchical model, showing the relationship between Level 3 nodes and their connection to a higher-level node at Level 4, highlighting the modular and scalable nature of the AI-driven communication protocol. Consider again the Parc-Extension district in Montreal; F1 from Figure 6 represents a Level 4 node, optimizing energy use between N1, N2, N3, N4, and N5 using a similar energy balance equation. Machine learning to develop predictive mathematical equations depends on data from real-time energy management systems and sensors from Level 2 nodes, which are aggregated, summarized, and propagated to Level 3 nodes. All Level 3 nodes' data are individually aggregated, summarized, and propagated to the Level 4 node, and so forth.

Finally, to clarify the diagram above, while N1 and F1 may be located next to each other, they are different nodes. By using RTEM data from Level 1 and Level 2 nodes, Level 4 can get granular data for each of its nodes (N1, N2, N3, N4, etc.). Having this data allows for predictive modeling on how each of the N1, N2, and N3 nodes will behave based on weather data and other factors.

This section of the paper provides methodologies for internal optimization, representing the first mode of communication. For Level 2 nodes, simplified steady-state equations from the Passive House Planning Package (PHPP) and Real-Time Energy Management (RTEM) data are employed to develop predictive models for energy consumption. These models are calibrated using historical data and machine learning algorithms.

At Level 3 and higher nodes, similar energy balance equations are utilized to optimize energy use across aggregated data from lower-level nodes, enabling the Advanced Energy Management System (AEMS). By integrating real-time performance data, weather data, and other relevant characteristics, the AI-driven communication protocol ensures efficient load balancing and energy distribution within the grid. This includes using predictive modeling to identify optimal charging and discharging times for energy storage, integrating information such as pricing, and coordinating with Level 3 nodes to determine these timeframes. This hierarchical and modular approach enables progressive aggregation, summarization, and propagation of data, ultimately enhancing grid stability and performance.

The Second Mode of Communication: bi-directional communication

A modular and scalable AI-driven communication protocol enables different modes of communication to ensure the proper functionality of the power grid. The first mode of communication focuses on internal optimization, with frameworks for machine learning models presented in the previous section. This section introduces a second mode of communication that enables bi-directional communication between nodes, facilitating advanced grid interactions.

Consider Demand Response Management Systems (DRMS), which automatically adjust the set-point temperature to reduce cooling demands within an occupant’s comfort range of 23°C to 26°C (ASHRAE, 2023). In the summer months, cooling drives peak loads. For example, in the Parc-Extension scenario illustrated in Figure 6, if node F1 (a Level 4 node) receives a directive to reduce energy consumption by a certain percentage, it can send signals to its lower Level 3 nodes (N1, N2, N3, N4, N5).

Using real-time weather data and predictive modeling, whole-building predictive modeling at Level 2 nodes can help identify reduction potential using various strategies. This process requires bi-directional communication, with information being shared with higher nodes. Similar to RTEM data, the strategies and potential reduction targets are summarized and propagated to higher levels. Thus, the Level 5 node connected to F1 should already understand different strategies and the energy reduction potential for each of its nodes (F1, F2, F3, F4).

Smart contracting with tenants' utility providers allows participating buildings to enable DRMS. Predictive modeling at Level 2 nodes can identify energy savings potential by evaluating the difference in energy use between the default set-point temperature and a reduced set-point temperature that still maintains the occupant’s comfort range of 23°C to 26°C.

Strategies need not be purely algorithm-driven. Text message communiqués can lead to voluntary reductions in energy use, with AI models predicting reductions in energy use due to these messages. In extreme cases, approaches can involve shutting off non-essential equipment or facilities completely.

In addition to DRMS, other types of bi-directional communication that enable advanced grid interactions include:

• Peer-to-Peer Communication: Nodes communicate directly with each other to manage local generation and consumption without a central control point.
• Device-to-Grid Communication: Smart devices report their status and receive commands from the grid, allowing for real-time adjustments in energy use.
• Microgrid Communication: Communication within microgrids, as well as between microgrids and the main grid, supports local energy management and grid stability.
• Vehicle-to-Grid (V2G) Communication: Electric vehicles interact with the grid to provide energy storage and supply services, charging during low demand and discharging during peak periods.
• Blockchain-Based Energy Trading: Peer-to-peer energy trading via blockchain technology ensures secure and transparent transactions between producers and consumers.

Ultimately, the AI-driven communication protocol, with its modular and scalable characteristics, allows for the addition of information layers and the use of priority functions to implement strategies such as DRMS to shave peak load. This flexibility ensures that the system can adapt to various scenarios by integrating additional information layers and dynamically employing a range of strategies, including priority functions, and allowing for region-specific or culture-specific responses. The framework's adaptability and flexibility enable multiple stakeholders and private businesses to collaborate effectively, making it a vital blueprint and giving legitimacy to a decentralized and democratized Web3.

Third Mode of Communication: Fault Detection and Diagnostics??

The third mode of communication within the proposed AI-driven communication protocol integrates additional layers of sensors and mathematical frameworks for fault detection and diagnosis. The protocol takes measurements at predetermined intervals (e.g., every 5 minutes) across all levels and predicts grid performance at various future time intervals, enabling discrepancy analysis.

Consider again the example of Parc-Extension (Figure 5). The load balancing equation at N1 is equal to the sum of H1, H2, H3, H4, H5, H6, H7, and PV1. If the predicted value for N1 differs from the actual value by more than a specified threshold, the system will evaluate the predicted value against the actual value for each node in its set (H1, H2, H3, H4, H5, H6, H7, PV1), allowing the model to flag the node causing the discrepancy. This process, called discrepancy analysis, can also be extended to Level 2 nodes to identify faults within energy systems in buildings and communicate these issues to equipment owners, machines, or appropriate authorities.

In addition to discrepancy analysis, the protocol’s algorithms can recalibrate the entire power grid. If PV1 stops producing energy, it will be identified at N1 and propagate through the hierarchy. Communicating with power plants to adjust their output based on frequency deviations allows the grid to adapt in real-time. The protocol’s predictive algorithms enable the grid to prepare strategies days and weeks in advance.

The protocol’s modular and scalable nature allows the integration of additional layers of sensors and algorithms. It can detect malfunctioning sensors, faulty valves, or compressors not operating at optimal levels, monitor energy flows, and detect components or circuits experiencing overloads.

These sensors and layers provide flexibility in the smart grid and can trigger load shedding or energy redistribution to prevent equipment damage or blackouts. Monitoring voltage levels throughout the grid helps identify areas where voltage is fluctuating beyond acceptable limits, triggering adjustments to stabilize voltage levels and prevent equipment damage. Sensors can also detect conditions posing safety hazards, such as overheating equipment or gas leaks, and trigger safety protocols like shutting off power to affected areas or alerting maintenance crews. The system monitors the health of its communication network to ensure effective communication, addressing issues like network congestion or equipment failures.

While this paper presents the fundamental modes of communication and a mathematical framework to develop machine learning models to optimize and maintain power grid stability in real-time, the plug-and-play nature of the AI-driven communication protocol allows for additional functions to be integrated, including enabling regional or cultural-specific responses.

Examples of Data-Driven Strategies for Grid Optimization?

This paper presents an AI-driven communication protocol using a hierarchical data aggregation and propagation framework to integrate artificial intelligence into existing power grid infrastructure. The protocol employs a multi-level hierarchical model that aggregates and propagates real-time energy management data. It is modular and scalable, proposing various machine learning models and communication modes to enable the grid to evaluate and respond in real-time, ensuring stability and reliability. The protocol creates a digital twin of the city, offering real-time insights into total energy use across the city or district, as well as detailed energy consumption for individual buildings and their subsystems. While the proposed scale is large, this paper integrates technologies and methods that are well understood and empirically validated.

Advanced Distribution Management Systems (ADMS) are software platforms that manage the distribution grid, incorporating real-time data to optimize operations, manage outages, and integrate distributed energy resources. Energy Management Systems (EMS) monitor and optimize the performance of the transmission and generation components of the grid. EMS can work on a city-wide scale and integrate various data sources for comprehensive energy management. Distributed Energy Resource Management Systems (DERMS) manage and optimize distributed energy resources, such as solar panels, wind turbines, and battery storage, often integrating with the larger grid.

Various cities around the world have adopted smart city initiatives that integrate IoT devices, real-time data analytics, and advanced energy management systems to improve energy efficiency and sustainability. Examples include Amsterdam Smart City, Barcelona Smart City, and Singapore Smart Nation. Companies like Siemens, GE, and IBM are developing digital twin technologies that create virtual models of physical systems, including city infrastructure and power grids, to simulate, predict, and optimize performance.

Blockchain technology is being increasingly integrated into solar energy projects to enhance transparency, efficiency, and security. Blockchain allows for the creation of decentralized energy markets where solar energy producers can trade energy directly with consumers. One example is Power Ledger, an Australian technology company that has developed a blockchain-based platform enabling peer-to-peer energy trading. This platform allows households with solar panels to sell excess energy to their neighbors, providing a more efficient and localized energy distribution system.

In Brooklyn, New York, the Brooklyn Microgrid project uses blockchain technology to create a local energy market where residents can buy and sell solar power directly from one another. This project demonstrates how blockchain can facilitate the integration of renewable energy sources into the grid while empowering consumers to become active participants in the energy market.

California Independent System Operator (CAISO) Demand Response predicts periods of high demand and requests participating customers to reduce their energy use, helping to balance the grid and avoid power outages (CAISO, 2022). Tesla's Autobidder is a real-time trading and control platform that provides independent power producers, utilities, and capital partners the ability to autonomously monetize battery assets. By strategically charging and discharging energy storage systems, Autobidder can help offset peak energy demands. It has been employed in Hornsdale Power Reserve in South Australia (Tesla, 2023a).

In Vermont, USA, Green Mountain Power (GMP) has implemented an energy storage system using Tesla Powerwall batteries in homes. These batteries are connected to the grid and can be controlled remotely to store energy during off-peak times and supply it back to the grid during peak demand. This helps in reducing the load on the grid and provides backup power to homeowners (Green Mountain Power, 2022).

Data Privacy and Cybersecurity: The 5th Layer of Architecture?

A key component of the framework is its privacy and cybersecurity. Its framework on Blockchain technology improves cybersecurity for Web3 and AI-driven communication protocols by creating a decentralized, tamper-proof ledger that ensures data integrity and prevents unauthorized changes. This immutable and transparent system makes it difficult for malicious actors to alter records, enhancing trust and security in data exchanges and communications (Zyskind et al., 2015; IEEE, 2017).

Further, the framework considers data privacy through a hierarchical model that uses foundational data propagated from Level 1 nodes, thus preserving the data within the building. While most frameworks upload data to a central cloud, here, the preservation of data within the building allows personal data to be physically separated and kept on a separate device, using call functions to access critical data.

It presents a paradigm shift considering the protocol’s multipotentiality. While the paper presented the framework through the lens of optimizing energy use and maintaining thermal comfort, climate-resilient building design must respond by optimizing multiple objectives. Consider indoor air quality: normal operations call for 18 cubic feet of fresh air per minute per person (American Society of Heating, Refrigerating, and Air-Conditioning Engineers [ASHRAE], 2016). However, during a pandemic such as COVID-19, building ventilation systems would prioritize the health of occupants and increase fresh air into the building (Centers for Disease Control and Prevention [CDC], 2021). Conversely, during wildfires or periods of poor outdoor air quality, the system would need to filter more air, ensuring that harmful particulates are removed before the air enters the building (Environmental Protection Agency [EPA], 2020).

A key to adaptability is upgrading our standards and emergency protocols. Many of our emergency codes, standards, and procedures are outdated as they are based on 100-year-old worst-case scenarios. However, climate change has been driving both the intensity and frequency of weather anomalies, and we are frequently experiencing worst-case weather events. Worse, there can be co-occurring events. Peak loading in our power grid happens during a heat wave but would be exacerbated if a pandemic occurred simultaneously; such co-occurring disasters are also increasing in their frequency.

The AI-driven communication protocol presented in this paper provides a comprehensive framework to modernize Canada’s security and democracy, offering an opportunity to adapt to a rapidly shifting world. Consider heat waves: the AI-driven communication protocol, with its modularity and scalability, provides an interface to add independent layers of sensors and algorithms to address climate emergencies in real-time. Such strategies include the integration of health infrastructure. While the power grid can use predictive and real-time data to stabilize, integrating data into the architecture—the 5th layer—enables the integration of health sensors, such as electroencephalography (EEG), which can provide real-time feedback to keep occupants healthy. For example, EEG data can help monitor signs of heat stress and prevent heat stroke by alerting individuals or families and adjusting indoor temperatures and increasing ventilation as needed. The potential of the proposed AI-driven communication protocol to transform our security, education, and health is limitless and represents a comprehensive digital governance framework to compete with China’s Digital Silk Road, positioning Canada as a centre for AI governance.

In conclusion, the AI-driven communication protocol offers a robust framework for enhancing data privacy, cybersecurity, and overall system resilience. Its modular and scalable design, coupled with the use of Blockchain technology, ensures a high level of data integrity and security. By keeping data within the building and employing hierarchical call functions, the framework significantly enhances privacy protections. The multipotentiality of this protocol extends beyond energy optimization to addressing critical health and climate challenges, making it a comprehensive solution for modernizing infrastructure.

A separate article will delve deeper into the intricacies of cybersecurity within this framework, providing a detailed analysis of its components and potential applications. The possibilities are endless, and the framework offers an ecosystem and interface that are both innovative and essential for future-proofing our systems.