Issue 31: IOT, AI and Greentech

The cross section of IOT, AI and Greentech is where my passion and excitement is - making technology work to help our planet and humanity at large. Everything I learn and do, ladders up to this mission on some vector. I touched upon the Sustainable Development Goals in a previous issue of AI in food security. Many of my subscribers have reached out directly expressing their shock at the scale of the problem - which is exactly the reason I write these - for my own learning and to contribute to others' learning. I am currently working on several other projects mapping to the UN SDG's and Greentech overall, l will share as we are ready - today I will simply introduce the space of IOT, AI and Greentech.

Introduction

Greentech, short for "green technology," refers to technology and innovations aimed at conserving the natural environment and resources, reducing pollution, and promoting sustainability. It encompasses a wide range of fields and applications designed to mitigate the impact of human activity on the planet. Key areas of Greentech include:

1. Renewable Energy: Technologies that generate energy from renewable sources such as solar, wind, hydro, and geothermal power.
2. Energy Efficiency: Innovations aimed at reducing energy consumption through efficient appliances, smart grids, and better energy management systems.
3. Waste Management: Solutions for reducing, recycling, and managing waste to minimize environmental impact.
4. Water Management: Technologies for water conservation, purification, and efficient distribution.
5. Sustainable Agriculture: Practices and technologies that promote environmentally friendly farming and food production.
6. Green Building: Construction practices that increase the efficiency of buildings in terms of energy, water, and material usage, and improve the health of their occupants.
7. Transportation: Development of low-emission and energy-efficient vehicles, such as electric cars, bicycles, and public transport systems.
8. Pollution Control: Technologies that reduce emissions and pollutants in air, water, and soil.

Greentech aims to create sustainable solutions that balance economic growth with environmental impact, and aligns with the broader set of the United Nations Sustainable Development Goals (SDGs).

Market Size

The green technology and sustainability market, was valued at $14.3 billion in 2023 and is projected to grow at a CAGR of over 19.5% from 2023 to 2032, reaching $83 billion by 2032. The global IoT market is projected to reach $1.1 trillion by 2026. The cross section of these segments is what is interesting to me, but I did not find reliable market sizing for it (some were dated 2021, which as we know is way before critical gen AI milestones).

Significant investments in smart grids, renewable energy, and precision agriculture. AI in the energy sector alone is expected to grow to $4.5 billion by 2026.

Drivers

This a vast area, and the growth is fueled by three factors at a high level:

• increasing awareness of corporate social responsibility
• stringent government regulations
• technological advancements.

There are some growth inhibitors or challenges such as high initial costs, access to data, quality of data, ethical and privacy concerns and regulatory barriers and complex policies by industry.

That said, there are few areas to call out where we are making the most progress, with the help of IOT and AI:

• Advancing Renewable Energy: GenAI can enhance renewable energy technologies by improving efficiency, lowering costs, and integrating renewable sources into the energy grid more effectively.
• Sustainable Resource Management: Utilizing predictive analytics and smart automation, GenAI can optimize the management of essential resources like water and land.
• Enhanced Environmental Monitoring: GenAI increases the accuracy and efficiency of monitoring environmental conditions, aiding in better conservation and disaster response through real-time data analysis.
• Promoting Circular Economy: By refining recycling processes and fostering sustainable material design, GenAI supports the advancement of circular economy principles.

The good news: Massive volumes of IOT data generated

As IOT continued to expand rapidly in 2023, it also contributed significantly to data generation, particularly within Greentech applications. Experts predict that more than 75 billion IoT devices will be connected to the web by 2025, and interestingly 60% of those are in the consumer space. The global IoT market is expected to generate an immense volume of data, with sensors and devices being adopted across use cases in a variety of industries. It is estimated that IoT devices will produce up to 73.1 zettabytes (ZB) of data by 2025 (DataProt).

This surge is driven by various innovative applications in the Greentech sector.

Key Greentech IoT Use Cases

1. Renewable Energy Integration:
2. Sustainable Resource Management:
3. Environmental Monitoring:
4. Smart Cities:

The bad news: Most of the data generated is not used in AI, yet!

While it seems we are making great strides, and enormous volumes of IOT data collected in a wide variety of devices, surprisingly, much of it is not currently used in any AI applications. Therein lies the opportunity.

When this is prioritized for Greentech - use cases

Use cases and applications

The integration of IoT, AI, and big data can significantly contribute to a more sustainable future, offering solutions to reduce carbon footprints, optimize resource usage, and protect the environment. Let's look at a few specific industries and use cases.

1. Renewable Energy Integration: IoT devices in renewable energy systems, such as solar panels and wind turbines, are generating vast amounts of data. This data is used for optimizing energy production, improving efficiency, and reducing costs. It is estimated that IoT applications in the energy sector could save up to $200 billion annually by 2030 through improved efficiency and reduced operational costs (IOT World Congress). Example: In 2023, IoT-enabled smart grids have significantly improved the integration and management of renewable energy sources, facilitating real-time monitoring and control of energy distribution networks.
2. Sustainable Resource Management: IoT sensors used in agriculture, water management, and land use optimization generate significant amounts of data. For instance, smart irrigation systems use soil moisture sensors and weather data to optimize water usage, which can lead to water savings of up to 30% (SpringerOpen). Example: Precision agriculture utilizes IoT sensors to monitor crop health, soil conditions, and weather patterns, enabling farmers to make data-driven decisions that enhance productivity and sustainability.
3. Environmental Monitoring: Environmental monitoring applications of IoT include air quality sensors, water quality monitoring systems, and wildlife tracking devices. These systems generate real-time data that is crucial for environmental conservation and disaster response. The global market for environmental monitoring is projected to reach $21.3 billion by 2025, driven by IoT technologies (Statista). Example: Cities deploying IoT-based air quality monitoring networks can track pollution levels in real-time, helping authorities to take timely actions to reduce pollution and protect public health.
4. Smart Cities: IoT applications in smart cities generate extensive data on traffic management, energy usage, waste management, and public safety. It is estimated that by 2025, there will be over 30 billion IoT-connected devices globally, many of which will be deployed in smart city initiatives (IOT World Congress) (Statista). Example: Smart city projects in cities like Barcelona and Singapore utilize IoT to manage urban infrastructure efficiently, reducing energy consumption, optimizing waste collection, and improving the overall quality of life for residents.

5. Smart Grids: Smart grids utilize sensors and smart appliances to optimize electricity production and distribution, reducing carbon footprints and improving efficiency. A study by Navigant Research projects that smart grid investments could reach $13.8 billion annually by 2024.

6. Smart Meters: Real-time data from smart meters helps consumers and providers optimize energy usage, leading to significant energy savings. The deployment of smart meters is expected to grow at a CAGR of 8.8%, reaching 198.53 million units by 2023.

Each of these industry use cases and scenarios, have numerous applications. Each deserves its own article, and I will cover each in detail, however, here is a preview:

Air Quality:

IoT and AI monitor air pollution, providing real-time data for better decision-making and pollution management. According to WHO, air pollution causes 7 million premature deaths annually, and better monitoring could significantly reduce this number. The global air quality monitoring system market is expected to grow from $4.25 billion in 2020 to $6.55 billion by 2025, at a CAGR of 9.0%, driven by the increasing adoption of IoT and AI technologies. There are many sources of data and use cases such as:

• Real-time Monitoring: IoT-enabled air quality sensors provide real-time data on pollutants such as particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), and carbon monoxide (CO).
• Wide Coverage: Networks of IoT sensors can cover large urban and rural areas, providing comprehensive air quality maps and identifying pollution hotspots.
• Data Integration: IoT devices can integrate data from various sources, including weather stations, traffic cameras, and industrial emissions monitors, to provide a holistic view of air quality.
• Predictive Analytics: AI algorithms analyze historical and real-time data to predict air quality trends, allowing authorities to take proactive measures to reduce pollution.
• Anomaly Detection: AI can identify unusual patterns in air quality data, helping to detect pollution incidents and illegal emissions quickly.
• Source Attribution: AI models can trace pollution sources, distinguishing between different contributors like traffic, industrial activities, and natural events.
• Decision Support: AI provides actionable insights and recommendations for policymakers to implement effective air quality management strategies.

Case Studies

1. SmartAQnet: SmartAQnet in Germany utilizes a dense network of IoT sensors to provide real-time air quality data and uses AI to predict pollution levels. This system has helped reduce peak pollution levels in cities like Augsburg by 20% .
2. Google Project Air View: Google, in collaboration with Aclima, uses AI and IoT to map air quality at a street level in various cities. The project has provided detailed pollution maps that help city planners and policymakers address local air quality issues effectively .
3. Breeze Technologies: Breeze Technologies uses IoT sensors and AI analytics to monitor and improve air quality in urban areas. Their platform has been deployed in cities like Hamburg, leading to a 15% reduction in NO2 levels .
4. London Air Quality Network (LAQN): LAQN integrates IoT sensors and AI to monitor air quality across London. The system provides real-time data and forecasts, helping authorities implement measures that have resulted in a 25% reduction in PM2.5 levels over five years .

Wildlife Protection:

IoT devices, such as sensors, cameras, and GPS trackers, play a crucial role in monitoring wildlife and their habitats. Here are some applications and their impacts:

• Image Recognition: AI models are trained to identify species from camera trap images, significantly speeding up the data analysis process. For example, Microsoft’s AI for Earth program helps researchers analyze millions of photos to identify and count species.
• Predictive Analytics: AI can predict poaching hotspots and potential threats by analyzing patterns in historical data, enabling proactive measures. The SMART (Spatial Monitoring and Reporting Tool) system uses AI to integrate data from patrols and predict poaching activities.
• Habitat Mapping: AI is used to analyze satellite imagery and map wildlife habitats, tracking changes over time and identifying areas that need protection.
• Reduction in Poaching: Projects using IoT and AI have reported significant reductions in poaching. For instance, the use of smart collars and drones in Kenya’s Tsavo National Park has contributed to a 50% reduction in elephant poaching.
• Increased Data Accuracy: AI-powered image recognition can achieve accuracy rates of over 90% in identifying species from camera trap images, compared to much lower rates with manual analysis.
• Real-Time Monitoring: IoT networks enable real-time monitoring of wildlife and environmental conditions, providing timely data that is crucial for effective conservation management.

Case Studies

1. SMART Conservation Software: Used in over 800 conservation sites across 55 countries, SMART combines IoT and AI to improve wildlife law enforcement and site-based conservation activities.
2. ZSL’s Instant Detect 2.0: The Zoological Society of London developed this system, which uses satellite-enabled sensors to provide real-time alerts of illegal activities in remote locations, helping rangers respond quickly.
3. Wildbook: An AI-powered platform that uses computer vision to identify and track individual animals from photos and videos, aiding in population studies and anti-poaching efforts.

Top Players in Greentech

1. Siemens: In smart grid technology.
2. Schneider Electric: For energy management solutions.
3. Tesla: Known for electric vehicles (EVs) and battery storage solutions.
4. NextEra Energy: A leader in renewable energy generation, particularly wind and solar power.
5. Siemens Gamesa: A major player in wind turbine manufacturing and renewable energy solutions.
6. Vestas: Specializes in wind energy solutions and services.
7. ?rsted: Focuses on offshore wind energy and sustainable energy solutions.
8. Enel Green Power: A global leader in renewable energy production.
9. SunPower Corporation: Known for high-efficiency solar panels and solar power solutions.

?