AI-powered IoT in the energy industry : digital technology and sustainable energy systems

AI-Powered IoT in the Energy Industry: Digital Technology and Sustainable Energy Systems looks at opportunities to employ cutting-edge applications of artificial intelligence (AI), the Internet of Things (IoT), and Machine Learning (ML) in designing and modeling energy and renewable energy systems. The book's main objectives are to demonstrate how big data can help with energy efficiency and demand reduction, increase the usage of renewable energy sources, and assist in transitioning from a centralized system to a distributed, efficient, and embedded energy system. Contributions cover the fundamentals of the renewable energy sector, including solar, wind, biomass, and hydrogen, as well as building services and power generation systems. Chapters also examine renewable energy property prediction methods and discuss AI and IoT prediction models for biomass thermal properties. ​Covers renewable energy sector fundamentals; Explains the application of big data in distributed energy domains; Discusses AI and IoT prediction methods and models. Preface Acknowledgments Contents About the Editors Chapter 1: AI and Intermittency Management of Renewable Energy 1.1 Introduction 1.2 Renewable Energy Sources 1.2.1 Wind Energy 1.2.2 Solar Energy 1.2.3 Geothermal Energy 1.2.4 Hydro Energy 1.2.5 Ocean Energy 1.2.6 Bioenergy 1.2.7 Hydrogen Energy 1.3 Artificial Intelligence 1.3.1 Artificial Intelligence in Wind Energy 1.3.1.1 AI Application in Wind Mills 1.3.1.1.1 Detecting Signs of Imminent Damage in Advance 1.3.1.1.2 One Windmill Should Not Hamper Any Others 1.3.1.2 Artificial Intelligence in Solar Energy 1.3.1.2.1 AI Technology 1.3.1.2.2 Smart Grid Centralized Control Centers 1.3.1.2.3 Improved Integration of Microgrids, Safety, and Reliability 1.3.1.2.4 Expand the Market 1.3.1.2.5 Intelligent Energy Storage 1.3.1.3 Artificial Intelligence in Geothermal Energy 1.3.1.3.1 AI Use Geothermal Energy Cases 1.3.1.4 Artificial Intelligence in Hydro Energy 1.3.1.4.1 AI for Performance Optimization 1.3.1.4.2 AI for the Forecasting of Plant Parameters 1.3.1.4.3 AI in Monitoring and Control of Hydropower Plants 1.3.1.4.4 AI for Accuracy Evaluation and Capability Assessment 1.3.1.5 Artificial Intelligence in Ocean Energy 1.3.1.6 Artificial Intelligence in Bioenergy 1.3.1.7 Artificial Intelligence in Hydrogen Energy References Chapter 2: AI and ML Toward Sustainable Solar Energy 2.1 Introduction 2.1.1 Involving AI for Managing Sustainable Energy 2.2 Factors Which Influence the Efficacy of Solar Energy 2.2.1 Construct Smart Control Centers 2.2.2 Coordinated Microgrids 2.2.3 Security and Reliability 2.2.4 Market Expansion 2.2.5 Smart Storage Units 2.3 Machine Learning in Solar Energy Production 2.3.1 Shrewd Foundation Plan of Solar-Oriented Energy Frameworks 2.3.2 Smart Support of Solar Energy Plants 2.3.3 Solar Energy Creation Determination 2.3.4 Enhanced Transmission and Dispersion Organizations 2.3.5 Understanding the Solar-Oriented Energy Market 2.4 Enormous Information Blast and Improvement of AI Models 2.5 Applications of ML in Sustainable Energy 2.5.1 Solar Energy 2.5.2 Wind Energy 2.6 The Future of Renewable Energy in the Perception of AI and ML 2.7 Conclusion References Chapter 3: Energy Intelligence: The Smart Grid Perspective 3.1 Introduction 3.1.1 History of Energy Grids 3.1.2 Power Sources 3.1.3 Power Demand 3.1.4 Power Supply 3.1.5 Renewable Energy Sources and Green Energy 3.2 Energy Systems and Smart Grids 3.2.1 Differentiation Between Current and Futuristic Smart Grids 3.2.2 Communication Infrastructure of Smart Grids 3.2.2.1 Wide-Area Network (Core Tier) 3.2.2.2 Neighborhood Area Network (Distribution Tier) 3.2.2.3 Premise Network (Access Tier) 3.2.2.4 Smart Microgrids 3.2.2.5 Communication Technologies: Wired or Wireless? 3.2.3 Distributed Approaches 3.2.4 Data Collection, Storage, and Processing 3.2.4.1 Data Characteristics 3.2.4.2 Data Sources 3.2.4.3 Data Processing 3.2.4.3.1 Data Storage 3.2.4.3.2 Data Privacy and Security 3.2.5 Data Analysis 3.2.5.1 Data Visualization 3.2.5.2 Big Data Tools for Analytics 3.2.5.2.1 Batch Processing Tools 3.2.5.2.2 Real-Time Processing Tools 3.2.5.2.3 Hybrid Processing Tools 3.2.6 IoT-Enabled Smart Grid Information System 3.3 Energy Intelligence 3.3.1 Objectives of Energy Intelligence 3.3.2 Data Forecasting: The Key Component 3.3.2.1 Insights from Smart Grids 3.3.2.2 Modeling Intelligent Power Grids 3.3.2.2.1 Demand Forecasting Through Artificial Neural Networks 3.3.2.2.2 Markov Processes 3.3.2.2.3 AI-Powered Simulators 3.3.2.2.4 Random Fuse Networks 3.3.2.2.5 Biosystems and Meta-heuristic Algorithms 3.3.2.3 Making Cities Energy Intelligent 3.4 Role of Energy Intelligence in Modeling Smarter Future 3.4.1 Making Smart Grids Smarter 3.4.2 G2V and V2G for Electric Vehicles 3.4.2.1 Self-Learning System and Grid 3.4.2.2 Complete Automation 3.4.3 Internet of Energy 3.5 Conclusion References Chapter 4: IoT Infrastructure to Energize Electromobility 4.1 Introduction 4.2 Electromobility and the Internet of Things in Buildings: Building Automation Includes Electromobility 4.2.1 The Internet of Things Provides Us with This Capability 4.2.2 Data as an Extremely Powerful Tool 4.2.3 Enhancing User Comfort with Enhanced Technical Features 4.2.4 Dynamic Information Exchange Between the Vehicle and the Building 4.3 Typical Locations with Electric Vehicles and Charging Stations by 2030 4.4 IoT Infrastructure Plays a Crucial Role in Connecting Our Devices 4.4.1 Information and Communication Technology (ICT) Infrastructure 4.4.2 IoT Technology Services 4.4.3 IoT Cloud Computing and Fog Computing 4.4.4 Analysis of Big Data and IoT 4.4.5 Internet of Things Security 4.5 Stuttgart Network Load Analysis and Forecast 4.5.1 Energy and the Internet of Things 4.5.2 Energy Generation and IoT 4.5.3 Intelligent Cities 4.5.4 Renewable Sources of Energy 4.5.5 Built-In Intelligence 4.5.6 Making Industry More Energy-Efficient 4.5.7 Intelligent Transport 4.6 The Challenges of Implementing IoT 4.6.1 Total Energy Consumption 4.6.2 Internet of Things Integration with Subsystems 4.6.3 Personal Information of Users 4.6.4 Securing Information 4.6.5 Standards Are Interconnected 4.6.6 Design of the Architecture 4.7 Future Prospects 4.7.1 Blockchain and the Internet of Things 4.7.2 Social Networking and the Environment 4.8 Conclusions References Chapter 5: Internet of Things Toward Leveraging Renewable Energy 5.1 Introduction 5.2 Technologies of IoT 5.2.1 Hardware 5.2.2 Software 5.2.3 Platform 5.2.4 Communications 5.2.4.1 Bluetooth 5.2.4.2 Wi-Fi 5.2.4.3 RFID 5.2.4.4 Wireless Sensor Networks (WSN) 5.3 General Architecture of IoT 5.3.1 Application Layer 5.3.2 Network Layer 5.3.3 Perception 5.4 Computing IoT Data 5.4.1 Cloud Computing 5.4.2 Fog Computing 5.5 Applications of IoT in Various Fields 5.5.1 Healthcare Systems 5.5.2 Agriculture 5.5.3 Smart City 5.6 IoT in Leveraging Renewable Energy 5.6.1 Renewable Energy 5.6.2 Solar Energy 5.6.3 IoT in Energy Sector 5.6.4 IoT to Improve Overall Production 5.7 Smart Grids for Implementation of Renewable Energy Distribution 5.8 Context Awareness 5.9 Test Bed 5.10 Literature Survey 5.11 Energy Sector Challenges 5.12 Conclusion References Chapter 6: IOT Contribution in Construct of Green Energy 6.1 Energy and the Internet of Things 6.1.1 Introduction 6.1.2 Research Methods 6.2 Internet of Things (IoT) for Business 6.3 Self-Powered Internet of Things Devices Using Energy Harvesting 6.3.1 Combustion of Energy 6.3.2 The Harvesting of Solar Energy 6.4 System Using IoT to Harvest Energy 6.4.1 Alternative Energy Sources 6.4.2 Using IOT to Improve Wind Turbine Reliability 6.4.3 Internet of Things in Power 6.5 Technology and AI 6.5.1 Artificial Intelligence as a Global Development Tool 6.6 AI for a Sustainable Future 6.7 Sensor-Based Energy Management in Wireless Sensor Networks 6.7.1 Overview 6.7.2 Modeling and Managing Energy-Efficient Sensors 6.7.3 Organization for Sensing Adaptive 6.7.4 Hierarchical Analysis Methods 6.8 Conclusion References Chapter 7: Building Sustainable Changing Infrastructure – Smart Solutions 7.1 Introduction 7.1.1 Environment 7.1.2 The Emergence of Sustainability 7.2 Smart Solutions 7.2.1 Constructions 7.2.2 Energy 7.2.3 Water 7.2.4 Food 7.2.5 Healthcare 7.3 Conclusion References Chapter 8: Biomass Renewable Energy: Introduction and Application of AI and IoT 8.1 Introduction 8.2 Renewable Energy 8.3 Types of Renewable Energy 8.3.1 Wind Power 8.3.2 Solar Power 8.3.3 Geothermal Energy 8.3.4 Hydro Energy 8.3.5 Tidal Energy 8.3.6 Nuclear Energy 8.3.7 Hydrogen Energy 8.4 IoT in Renewable Energy 8.4.1 Challenges of IoT Implementation in Renewable Energy 8.5 AI in Renewable Energy 8.5.1 Challenges of AI in Energy Industry 8.5.2 Subsidy Given by the Government for Generation of Renewable Energy 8.6 Future of Renewable Energy 8.7 Conclusion References Chapter 9: AI and IoT in Improving Resilience of Smart Energy Infrastructure 9.1 Introduction 9.2 Smart Energy, Smart Grid, and Smart Energy Systems 9.3 Smart Energy System 9.4 IoT and Smart Energy Systems 9.5 IoT and Energy Sectors 9.5.1 IoT and Solar Energy 9.5.2 IoT and Geothermal Energy Generation 9.5.3 IoT and Wind Energy 9.5.4 IoT in Hydropower 9.6 IoT Applications in Energy Sector 9.6.1 Supervisory Control and Data Acquisition (SCADA) 9.6.2 Energy Resource Optimization 9.6.3 Microgrids’ Empowerment 9.6.4 Advanced Metering Infrastructure (AMI) 9.6.5 Proactive Mechanism for Repair 9.6.6 Smart Meter Technology 9.6.7 Remote Monitoring of Assets 9.7 IoT Challenges in Energy Sector 9.7.1 More Energy Consumption 9.7.2 IoT Integration with System Components 9.7.3 Privacy 9.7.4 Security 9.7.5 Architecture Design 9.8 AI and Energy Sector 9.8.1 AI and Wind Energy 9.8.1.1 AI and Prediction 9.8.1.2 Artificial Intelligence (AI) in Operations and Management (O&M) 9.8.2 AI and Solar Energy 9.8.2.1 AI-Based Forecasting System 9.8.2.2 AI for Power Grids and Storage 9.8.2.3 Inspecting Solar Panels with AI-Enabled Drones 9.8.2.4 AI and Market Growth 9.9 AI in Improving Energy Sources 9.9.1 Weather Prediction 9.9.2 Grid Balancing 9.9.3 Detect Grid Faults and Failure 9.9.4 Real-Time Monitoring of Brownouts 9.9.5 Prevent Electric Grid Failures 9.10 Conclusion References Chapter 10: Empowering Renewable Energy Using Internet of Things 10.1 Introduction 10.2 Renewable Energy Subsectors 10.2.1 Solar and Wind: A Major Subsector 10.2.2 Tidal Energy 10.2.3 Wave Energy 10.2.4 Hydro Energy 10.2.5 Biomass 10.3 Integration of Renewable Energy and IoT 10.3.1 IoT in Wind and Solar Energy 10.3.2 IoT in Tidal Energy 10.3.3 IoT in Wave Energy 10.3.4 IoT in Hydro Energy 10.3.5 IoT in Biomass 10.4 Overall Benefits of IoT in Renewable Energy 10.4.1 Automation of Process to Increase Productivity 10.4.2 Enhanced Cost Efficiency 10.4.3 Excellent Grid Management 10.4.4 Smart Distribution 10.4.5 Smart Residence 10.4.6 Comparison of Renewable Energy with IoT 10.5 International Market Scenario for IoT in Renewable Energy 10.6 Challenges Faced by IoT in Renewable Energy 10.7 Conclusion References Chapter 11: Modernization of Rural Electric Infrastructure 11.1 Introduction 11.1.1 Challenges for Rural Electrification 11.2 Traditional and Modern Technology for Electric Power Systems 11.2.1 Traditional Power Grids 11.2.2 Smart Grids 11.3 Demand and Supply of Electricity in Rural Areas 11.3.1 Sources of Electricity Used by Rural People 11.3.2 Hurdles Preventing Affordability 11.3.3 Demand for Electricity 11.4 Electric Energy Storage 11.4.1 Hydroelectric Power Stations 11.4.2 Wind Mills 11.4.3 Batteries 11.4.4 Thermal Power Station 11.5 Security Concerns over Electrical Power Systems 11.6 Smart-Grid Policies 11.7 Issues on the Implementation of Modern Technologies 11.8 Renewable Energy for Sustainable Development 11.8.1 Solar Energy 11.8.2 Wind Energy 11.8.3 Hydro-Electric Energy 11.8.4 Biomass Energy 11.8.5 Advantages of Renewable Energy Sources 11.8.6 Disadvantages of Renewable Energy Sources 11.9 Recent Trends in Electrical Power Systems 11.10 Innovation for the Deployment of Modern Technologies 11.11 Future Implications in the Rural Electricity Infrastructure 11.11.1 Connecting Unelectrified Rural Homes 11.11.2 Providing the Supply of a Required Quality of Power 11.11.3 Electricity at Marginable Rates 11.11.4 Ensuring Clean and Sustainable Electricity as a Product 11.11.5 Innovations 11.12 Conclusion References Chapter 12: The Role of Artificial Intelligence in Renewable Energy 12.1 Introduction 12.2 Renewable Energy 12.2.1 Renewable Energy Source 12.2.1.1 Solar 12.2.1.2 Biomass 12.2.1.3 Hydrogen 12.2.1.4 Oceans 12.2.1.5 Geothermal 12.2.1.6 Wind 12.3 Challenges for Renewable Energy 12.3.1 Weather Unpredictability 12.3.2 Energy Storage Technology 12.4 AI and Renewable Energy Systems 12.4.1 Robotics 12.4.1.1 Flying Drones 12.4.1.2 Crawling Robots 12.4.1.3 Driving Robots 12.4.1.4 Sailing Robots 12.4.1.5 Diving Robots 12.4.2 Smart and Centralized Control Systems 12.4.3 Smart Grids and Intelligent Storage 12.4.4 Wind Flow Speed Prediction 12.4.5 Modeling of a Solar Steam Generator 12.4.6 Improve Safety and Reliability 12.5 RE Technology Organizations and AI 12.5.1 Xcel Energy 12.5.2 PowerScout 12.5.3 General Electric 12.6 Conclusion References Chapter 13: Powering the Geothermal Energy with AI, ML, and IoT 13.1 Introduction 13.2 Highlights of AI, ML, and IoT 13.3 Overview of GT Energy 13.4 Hotspots of GT Energy 13.5 Power Production 13.5.1 Dry (Direct) Steam System 13.5.2 Flashing Power System 13.5.3 Binary Cycle System 13.6 Advantages and Disadvantages 13.6.1 Benefits of GT Energy 13.6.2 Drawbacks of GT Energy 13.7 GT Reservoir Management 13.8 AI-Powered IoT 13.8.1 In the Identification of Hotspots 13.8.2 In Power Production 13.8.3 In Reservoir Management 13.9 Conclusion References Chapter 14: IoT and Sustainability Energy Systems: Risk and Opportunity 14.1 Introduction 14.1.1 Smart Technologies Are Needed 14.1.2 Scope of Applications 14.1.3 Methodology for Reviewing 14.2 Efficiency of IoT in Sustainable Energy and Environmental Management 14.2.1 The Role of IoT in Maximizing Energy Efficiency and Sustainability 14.2.2 The Internet of Things Is Transforming the Energy Industry 14.3 Conclusion References Index