Artificial Intelligence for Renewable Energy Systems

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Algorithms
Analysis
Applications
Artificial Intelligence
Auto correlation
Biodiesel
Bioenergy
Case Study
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Classification
Clustering
Clustering using ML
Condition Monitoring
Correlation
Data Science
Decision Making
Deep feature
Deep Learning
Designing
Efficiency
Electrical Engineering
Energy Consumption
Energy Management
Energy System
Environment
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Error predication
Estimation
Experimental Results
Feature Extraction
Feature Selection
Forecasting Global condition
Green Building
Hybrid Energy System
Hybrid power plants
Intelligence Data Machine Learning
Modelling
Optimization
Power Plant
Practical implementation
Real time Regression
Renewable Energy
SCADA
Solar Cell
Solar Energy
Solar Radiation
Weather forecasting
Wind Energy
Wind Speed.

Product details

  • ISBN 9781119761693
  • Weight: 454g
  • Dimensions: 10 x 10mm
  • Publication Date: 04 Mar 2022
  • Publisher: John Wiley & Sons Inc
  • Publication City/Country: US
  • Product Form: Hardback
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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.

Ajay Kumar Vyas, PhD is an assistant professor at Adani Institute of Infrastructure Engineering, Ahmedabad, India. He has authored several research papers in peer-reviewed international journals and conferences, three books, and two Indian patents.

S. Balamurugan, PhD SMIEEE, ACM Distinguished Speaker, received his PhD from Anna University, India. He has published 57 books, 300+ international journals/conferences, and 100 patents. He is the Director of the Albert Einstein Engineering and Research Labs. He is also the Vice-Chairman of the Renewable Energy Society of India (RESI). He is serving as a research consultant to many companies, startups, SMEs, and MSMEs. He has received numerous awards for research at national and international levels.

Kamal Kant Hiran, PhD is an assistant professor at the School of Engineering, Sir Padampat Singhania University (SPSU), Udaipur, Rajasthan, India, as well as a research fellow at the Aalborg University, Copenhagen, Denmark. He has published more than 35 scientific research papers in SCI/Scopus/Web of Science and IEEE Transactions Journal, conferences, two Indian patents, one Australian patent granted, and nine books.

Harsh S. Dhiman, PhD is an assistant professor in the Department of Electrical Engineering at Adani Institute of Infrastructure Engineering, Ahmedabad, India. He has published 12 SCI-indexed journal articles and two books, and his research interests include hybrid operation of wind farms, hybrid wind forecasting techniques, and anomaly detection in wind turbines.