Artificial Intelligence for Renewable Energy Systems
- Length: 272 pages
- Edition: 1
- Language: English
- Publisher: Wiley-Scrivener
- Publication Date: 2022-03-15
- ISBN-10: 1119761697
- ISBN-13: 9781119761693
- Sales Rank: #0 (See Top 100 Books)
ARTIFICIAL INTELLIGENCE FOR RENEWABLE ENERGY SYSTEMS
Renewable energy systems, including solar, wind, biodiesel, hybrid energy, and other relevant types, have numerous advantages compared to their conventional counterparts. This book presents the application of machine learning and deep learning techniques for renewable energy system modeling, forecasting, and optimization for efficient system design.
Due to the importance of renewable energy in today’s world, this book was designed to enhance the reader’s knowledge based on current developments in the field. For instance, the extraction and selection of machine learning algorithms for renewable energy systems, forecasting of wind and solar radiation are featured in the book. Also highlighted are intelligent data, renewable energy informatics systems based on supervisory control and data acquisition (SCADA); and intelligent condition monitoring of solar and wind energy systems. Moreover, an AI-based system for real-time decision-making for renewable energy systems is presented; and also demonstrated is the prediction of energy consumption in green buildings using machine learning. The chapter authors also provide both experimental and real datasets with great potential in the renewable energy sector, which apply machine learning (ML) and deep learning (DL) algorithms that will be helpful for economic and environmental forecasting of the renewable energy business.
Audience
The primary target audience includes research scholars, industry engineers, and graduate students working in renewable energy, electrical engineering, machine learning, information & communication technology.
Cover
Table of Contents
Title Page
Copyright
Preface
1 Analysis of Six-Phase Grid Connected Synchronous Generator in Wind Power Generation
1.1 Introduction
1.2 Analytical Modeling of Six-Phase Synchronous Machine
1.3 Linearization of Machine Equations for Stability Analysis
1.4 Dynamic Performance Results
1.5 Stability Analysis Results
1.6 Conclusions
References
Appendix
Symbols Meaning
2 Artificial Intelligence as a Tool for Conservation and Efficient Utilization of Renewable Resource
2.1 Introduction
2.2 AI in Water Energy
2.3 AI in Solar Energy
2.4 AI in Wind Energy
2.5 AI in Geothermal Energy
2.6 Conclusion
References
3 Artificial Intelligence–Based Energy-Efficient Clustering and Routing in IoT-Assisted Wireless Sensor Network
3.1 Introduction
3.2 Related Study
3.3 Clustering in WSN
3.4 Research Methodology
3.5 Conclusion
References
4 Artificial Intelligence for Modeling and Optimization of the Biogas Production
4.1 Introduction
4.2 Artificial Neural Network
4.3 Evolutionary Algorithms
4.4 Conclusion
References
5 Battery State-of-Charge Modeling for Solar PV Array Using Polynomial Regression
5.1 Introduction
5.2 Dynamic Battery Modeling
5.3 Results and Discussion
5.4 Conclusion
References
6 Deep Learning Algorithms for Wind Forecasting: An Overview
Nomenclature
6.1 Introduction
6.2 Models for Wind Forecasting
6.3 The Deep Learning Paradigm
6.4 Deep Learning Approaches for Wind Forecasting
6.5 Research Challenges
6.6 Conclusion
References
7 Deep Feature Selection for Wind Forecasting-I
7.1 Introduction
7.2 Wind Forecasting System Overview
7.3 Current Forecasting and Prediction Methods
7.4 Deep Learning–Based Wind Forecasting
7.5 Case Study
References
8 Deep Feature Selection for Wind Forecasting-II
8.1 Introduction
8.2 Literature Review
8.3 Long Short-Term Memory Networks
8.4 Gated Recurrent Unit
8.5 Bidirectional Long Short-Term Memory Networks
8.6 Results and Discussion
8.7 Conclusion and Future Work
References
9 Data Falsification Detection in AMI: A Secure Perspective Analysis
9.1 Introduction
9.2 Advanced Metering Infrastructure
9.3 AMI Attack Scenario
9.4 Data Falsification Attacks
9.5 Data Falsification Detection
9.6 Conclusion
References
10 Forecasting of Electricity Consumption for G20 Members Using Various Machine Learning Techniques
10.1 Introduction
10.2 Dataset Preparation
10.3 Results and Discussions
10.4 Conclusion
Acknowledgement
References
11 Use of Artificial Intelligence (AI) in the Optimization of Production of Biodiesel Energy
11.1 Introduction
11.2 Indian Perspective of Renewable Biofuels
11.3 Opportunities
11.4 Relevance of Biodiesel in India Context
11.5 Proposed Model
11.6 Conclusion
References
Index
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