Artificial intelligence for renewable energy systems / edited by S. Balamurugan [and three others].

Format:

Book

Contributor:

Balamurugan, S., Prof., editor.

Series:

Artificial Intelligence and Soft Computing for Industrial Transformation

Artificial intelligence and soft computing for industrial transformation

Language:

English

Subjects (All):

Renewable energy sources--Data processing.

Renewable energy sources.

Artificial intelligence--Engineering applications.

Artificial intelligence.

Physical Description:

1 online resource (270 pages)

Place of Publication:

Hoboken, New Jersey ; Beverly, Massachusetts : Scrivener Publishing : John Wiley & Sons, Inc., [2022]

Summary:

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.

Contents:

Front Matter

Analysis of Six-Phase Grid Connected Synchronous Generator in Wind Power Generation / Arif Iqbal, Girish Kumar Singh

Artificial Intelligence as a Tool for Conservation and Efficient Utilization of Renewable Resource / N Vinay, Ajay Sudhir Bale, Subhashish Tiwari, Chithra R Baby

Artificial Intelligence-Based Energy-Efficient Clustering and Routing in IoT-Assisted Wireless Sensor Network / Nitesh Chouhan

Artificial Intelligence for Modeling and Optimization of the Biogas Production / Narendra Khatri, Kamal Kishore Khatri

Battery State-of-Charge Modeling for Solar PV Array Using Polynomial Regression / Siddhi Vinayak Pandey, Jeet Patel, Harsh S Dhiman

Deep Learning Algorithms for Wind Forecasting: An Overview / M Lydia, G Edwin Prem Kumar

Deep Feature Selection for Wind Forecasting-I / C Ramakrishnan, S Sridhar, Kusumika Krori Dutta, R Karthick, C Janamejaya

Deep Feature Selection for Wind Forecasting-II / S Oswalt Manoj, JP Ananth, Balan Dhanka, Maharaja Kamatchi

Data Falsification Detection in AMI: A Secure Perspective Analysis / VV Vineeth, S Sophia

Forecasting of Electricity Consumption for G20 Members Using Various Machine Learning Techniques / Jaymin Suhagiya, Deep Raval, Siddhi Vinayak Pandey, Jeet Patel, Ayushi Gupta, Akshay Srivastava

Use of Artificial Intelligence (AI) in the Optimization of Production of Biodiesel Energy / Manvinder Singh Pahwa, Manish Dadhich, Jaskaran Singh Saini, Dinesh Kumar Saini

Index

Also of Interest

Notes:

Description based on print version record.

Includes bibliographical references and index.

Other Format:

Print version: Vyas, Ajay Kumar Artificial Intelligence for Renewable Energy Systems

ISBN:

9781119761723

1119761727

9781119761686

1119761689

9781119761716

1119761719

OCLC:

1301543087