??AI+IoT=AIoT, Artificial Intelligence of Things applied to Energy??& Utilities Sectors?
??Fabio Bottacci
Senior Business Advisor / Venture Partner | Executive Director Business Development / Startups GTM Strategy & Execution | Industrial IoT + AI GenAI Expert & Evaluator @Horizon Europe Funding | HBR Advisory Council Member
Hello everyone,
This Friday, the newsletter will focus on Artificial Intelligence + Industrial IoT = AIoT actual applications and solutions, which - combined together - deliver optimized tangible results, my key point since 2016, by the way??:
??Two IoT Analytics research reports on the topics of industrial Artificial Intelligence (AI) and the Artificial Intelligence of Things (AIoT) estimate that the AIoT market will reach $102.2 billion by 2026. Source: https://bit.ly/424ati0
??Four trends are driving the technology:
(1) The availability of new software tools
(2) The development of simplified AI solutions
(3) The infusion of AI into legacy applications,
(4) Advances in AI hardware.
??So, Industrial IoT "on steroids" is when IIoT retrieved real-time data are used not only for equipment Condition-Based Monitoring [CBM] applications, but also exponentially leveraged by Artificial Intelligence [AI] / Deep Learning [DL] advanced algorithms, to predict critical assets operational status.
??AIoT solutions enable time-sensitive decisions, and - most important of all - actions, which in same cases can be even automated/autonomous ones.
Data is only valuable as the decisions/actions it enables
??Applied agnostically to any industry/vertical, AIoT allows to "predict" future operational events, in some cases with more than 95% of accuracy, and with days/weeks in advance.
??This not only from an economic point of view - for example, with planned cost-effective interventions, such as Predictive/Prescriptive Maintenance (PdM) - but also timely predicting human-danger situations, such as early wildfires detection, dangerous equipment explosion, etc.
I hope you enjoy it, and - as usual - let my know your comments/thoughts!
Ciao, Fabio
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CASE STUDY
ENEL : Largest Production Deployment of AI and IoT [AIoT] Applications | Via BakerHughesC3.ai
Overview
To increase efficiency, develop new services, and spread a digital culture across the organization, Enel is executing an enterprise-wide digitalization strategy.
Central to achieving the Fortune 100 company’s goals is the large-scale deployment of the C3 AI Suite and applications.
Enel operates the world’s largest enterprise IoT system with 20 million smart meters across Italy and Spain.
About Enel
Project Highlights
The Solution
The teams worked together to replace traditional non-technical loss identification processes with the C3 Fraud Detection application. The new application uses advanced AI capabilities to prioritize potential cases of non-technical loss at service points, based on a blend of the magnitude of energy recovery and likelihood of fraud.
The system integrates and correlates 10 trillion rows of data from seven Enel source systems and 22 data integrations into a unified, federated cloud image in near real-time, running on Amazon Web Services . Using analytics and more than 500 advanced machine learning features, C3 Fraud Detection continuously updates probability of fraud for each customer meter.
To improve grid reliability and reduce the occurrence of faults, Enel deployed the C3 Predictive Maintenance application for 5 control centers. The application uses AI to analyze real-time network sensor data, smart meter data, asset maintenance records, and weather data to predict feeder failure.
The system provides a holistic view of Enel’s operating assets by integrating data from 8 disparate systems - SCADA, Grid Topology, Weather, Power Quality, Maintenance, Workforce, Work Management, and Inventory - and presenting relevant, actionable insights. Key innovations in this project include a time-based view of Enel’s as-operated network state using an advanced graph network approach, and the use of an advanced machine learning framework that continuously learns to improve prediction performance.
"Talking about digitalization, new technology, the move to the cloud, and the adoption of platforms, our experience with C3.ai has been a wonderful example.”
Fabio Veronese - Head of Infrastructure & Networks Digital Hub, Enel
Operational Impact
Process Optimization - Predictive Maintenance
Results
??4 Production Applications Deployed at Scale
??50M+ Sensors Monitored
??€200M+ Annual Recurring Economic Value
??2X Performance Increase in Identifying Unbilled Energy
??€6.72B Potential Per Year In Economic Benefit Once Fully Implemented
Deployed Technologies
ENEL Solution Architecture
Source: https://bit.ly/3ZPvdYU
#industrial #iot ?#iiot #platform #ai ?#ml ?#cloud #energy #utilities #frauddetection #predictivemaintenance #pdm #failureprediction
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PERSPECTIVES
How will AI [AIoT] shape the future of clean energy? | Via?World Economic Forum ?/?GreenBiz Group ?| Feb, 2023?
In February was the foremost utility trade show in the U.S. descended on the San Diego Convention Center Corporation — DISTRIBUTECH International .
AI-powered technologies [AIoT] were scattered across the expo hall, touting new applications for energy and data management.
AI is in the air.?I’m not able to open a social media app or newsletter without being reminded of the AI revolution. For the first time, it seems, the masses are understanding how AI and machine learning will change everything.
How will AI change the future of clean energy?
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领英推荐
Source:?https://bit.ly/3L3sXcz
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PERSPECTIVES
Green [Industrial] IoT for Energy Efficiency and Environmental Sustainability | Via?InfoQ ?| Oct, 2022
The future challenge of IoT is to develop processes and policies that make sustainable use of IoT to reduce the greenhouse effect and carbon emissions, and further optimize?IoT greenhouse footprint.
??IoT can be combined with technologies like AI, machine learning, computer vision, cloud computing, nanotechnology, and big data to develop environmentally sustainable solutions for a better and fulfilled life.
??Greening of IoT focuses on manufacturing energy efficient IoT hardware as well as upon green software development, while Greening by IoT involves IoT as an enabler to create a sustainable environment.
??The green IoT life cycle spans the entire IoT product lifecycle: green design, green production, green deployment, and green recycling.
??Green RFID, Green Wireless sensing networks, Green M2M, Green Data center and Green Cloud Computing are key enablers of green IoT.
Source:?https://bit.ly/3gv2wPG
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INSIGHTS
7?[Industrial] IoT?Applications?That Will Transform The?Electricity?Sector | Via?IoT Now ?| May, 2022
Over the coming decade the electricity sector must face increasing demand, increasing regulation, and fundamental changes in the way that electricity grids work on a day-to-day basis.
New fluctuating energy sources must be integrated into existing grids, and many of today’s customers may take on a second role as suppliers.
The deployment of new technology is critical to enabling the changes that must happen, and a wide variety of technology-based solutions are being deployed in the electricity industry right now.
7 ways IoT technology help
??Smart metering
??Monitoring?distribution?substations, transformers, and?feeders
??Remote monitoring?and control of network assets
??Drone inspections for transmission and distribution assets
??Remote monitoring for a range of renewables energy generation assets
??Deploying connected inductive sensors throughout a grid to monitor the efficiency of grid operations
??Fleet management solutions for maintenance field forces
Source:?https://bit.ly/37mgqPV
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INSIGHTS
Analyst?Insights?on [Industrial]?IoT?in the?Energy?Sector | Via?IoT Now ??| Mar, 2022
Latest insights from the Energy sector on the key issues facing the market and how IoT is addressing them.
Energy generation and distribution includes electricity, smart grid and renewables, oil and gas exploration and production.
Key issues in the sector:
??Assuring sustainable supplies of energy for the coming decades
??Mitigating the effects of climate change.?
??Sustainability of energy resources
How?IoT?is addressing these issues:
??Analytics?for?demand forecasting
??Asset monitoring
??Automated vehicle?tracking?and guidance
??Connectivity
??Customer engagement
??Demand response
??Digital twins
??Drones
??Edge computing
??Lone?worker?protection
??Predictive maintenance?
??Security?and?cybersecurity
??Smart metering
??Usage based?billing?and?fraud detection
Source:?https://bit.ly/3IvovOV
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Bio:?Fabio Bottacci ?is a relationship builder, creative problem solver and strategic thinker. Senior industrial executive, he acquired a solid background in large multinational organizations across Brazil, US, and Western Europe. He is known for his ability to deliver results despite ambiguity and obstacles, to build bridges between people and to manage conflict and negotiations.
He began his career at?Accenture Italia , strategy practice, while attending MBA courses. He then moved to Brazil, where he consistently proved, during more than 20 years of professional experience, strong client's network, industry knowledge and business development expertise in the oil and gas, automotive and energy/utilities verticals.
Since 2015, he has been the founder & CEO of?VINCI Digital - Industrial IoT Strategic Advisory , being recognized internationally as a thought leader by well-known organization, such as the?Wor ld Economic Forum ,?IoT Solution World Congress ,?BNDES , etc., and helping startups, SME, and big corporations to thrive within the actual digital transformation environment, by developing new business models, and delivering actual results/ROI in months, not years.
Negócios :: Tech :: UX
1 年Cirineu Carvalho Fernandes negócios grandes!
Founder @ Ajay Techma Systems | with Passion for Achieving Business Excellence by Adoption of Technology and Digital Transformation
1 年Thanks for sharing Almost 30 years back, I had carried out studies for improvements in Energy Management ( Mainly Steam & Electricity) to carry out Energy Audits, With this knowledge base later developed software solution to monitor energy losses and optimize usage. CMMS, had a module for Condition Based Monitoring to keep track of vibrations mainly pumps, by regular measurements based on preventive maintenance schedule drawn. This will update to preventive maintenance history & show trend to indicate due plan action. Today these can be integrated by using Analytics for optimizing equipment capacity.
CEO @ MapOmega. Military Engineer, Full Stack Dev, Industry 4.0, Six Sigma Black Belt
1 年Nos meus tempos de Seis Sigma Black Belt, o Define e Measure eram as duas etapas críticas dos Projetos, que durante entre 6 e 8 meses. Todo o Projeto demandava um planejamento minucioso nestas etapas, caso contrário todo o esfor?o de meses estava perdido, pela falta dos dados corretos a serem analisados na etapa seguinte. Agora, com os sensores sendo commodities de baixo custo, estas duas etapas est?o soterradas sob uma avalanche de dados, num volume inimaginado. Todo o problema de análise mudou de uma falta, para um excesso de dados. O famoso “dilúvio de dados”. Neste processo, a inteligência Artificial reúne o conjunto de ferramentas para triturar e organizar esses dados, numa velocidade jamais imaginada quando come?amos nos anos 90/2000. Dominar as possibilidades dessas ferramentas, para extrair delas informa??es relevantes à tomada de decis?es, visando otimiza??o, seguran?a e lucratividade s?o os novos desafios das empresas dos anos 20. Felizmente, tudo avan?a e nos proporciona novos e vastos campos de aprendizado contínuo. Resta saber quando as empresas ir?o se dar conta que investir em tecnologia é menos arriscado do que investir em caminhos já batidos, que n?o v?o mais para lugar algum.