
Artificial Intelligence for Energy Management
- 448 pages
- English
- PDF
- Available on iOS & Android
Artificial Intelligence for Energy Management
About this book
Harness the future of sustainable energy with this essential volume, which provides a comprehensive guide to integrating artificial intelligence for efficient energy storage and management systems.
To achieve a clean and sustainable energy future, renewable energy sources such as solar, hydropower, and wind must develop dependable and effective energy storage technologies. The growing need for intelligent energy storage systems is greater than ever, despite substantial advancements in sophisticated energy storage technology, especially for large-scale energy storage. This book aims to provide the most recent developments in the integration of artificial intelligence for energy storage and management systems by introducing energy systems, power generation, and power needs to reduce expenses associated with generation, power loss, and environmental impacts. It explores state-of-the-art methods and solutions, such as intelligent wind and solar energy systems, founded on current technology, offering a strong foundation to satisfy the requirements of both developed and developing nations. An extensive overview of the many kinds of storage options is included. Additionally, it examines how utilizing diverse storage types can enhance the administration of a power supply system while also considering the more significant opportunities that result from integrating multiple storage devices into a system. Artificial Intelligence for Energy Management is a collection of expert contributions encompassing new techniques, methods, algorithms, practical solutions, and models for renewable energy storage based on artificial intelligence.
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Information
Table of contents
- Cover
- Series Page
- Title Page
- Copyright Page
- Contents
- Preface
- Chapter 1 Introduction to Next-Generation Energy Management and Need for AI Solutions
- Chapter 2 Overview of Innovative Next Generation Energy Storage Technologies
- Chapter 3 Battery Energy Storage Systems with AI
- Chapter 4 AI-Powered Strategies for Optimal Battery Health and Environmental Resilience for Sodium Ion Batteries
- Chapter 5 Design and Development of an Adaptive Battery Management System for E-Vehicles
- Chapter 6 Remaining Useful Life (RUL) Prediction for EV Batteries
- Chapter 7 Analysis of Si, SiC, and GaN MOSFETs for Electric Vehicle Power Electronics System
- Chapter 8 An Efficient Control Strategy for Hybrid Electrical Vehicles Using Optimized Deep Learning Techniques
- Chapter 9 Machine Learning and Deep Learning Methods for Energy Management Systems
- Chapter 10 Ensuring Grid-Connected Stability for Single-Stage PV System Using Active Compensation for Reduced DC-Link Capacitance
- Chapter 11 Optimizing Microgrid Scheduling with Renewables and Demand Response through the Enhanced Crayfish Optimization Algorithm
- Chapter 12 Relative Investigation of Swarm Optimized Load Frequency Controller
- Chapter 13 Economic Aspects and Life Cycle Assessment in Energy Storage Systems
- Chapter 14 Energy Monitoring System Using Arduino and Blynk: Design and Simulation
- Chapter 15 Smart Home Energy Management System
- Chapter 16 A Study to Analyze the Vulnerabilities and Threats Faced by the Power Sector
- Chapter 17 Integrated Hybrid Energy Management to Reduce Standby Mode Power Consumption
- Chapter 18 Enhanced Reliability of Electrical Power Transmission in IEEE 24 DC Bus System Using Hybrid Optimization
- Chapter 19 Impact of Renewable Energy Sources on Power System Inertia
- Chapter 20 Empowering India Toward Sustainability: An In-Depth Review of Wind Energy Utilization
- About the Editors
- Index
- Also of Interest
- EULA