Forecasting Methods for Renewable Power Generation

Jai Govind Singh, Rupendra Kumar Pachauri, Sasidharan Sreedharan

• Publisher: Wiley-Scrivener
• Year: 2025
• Print ISBN: 9781394249435
• eBook ISBN: 9781394249459
• Edition: 1
• Topic: Technology & Engineering
• Subtopic: Renewable Power Resources
• Index: Technology & Engineering

Forecasting Methods for Renewable Power Generation is an essential resource for both professionals and students, providing in-depth insights into vital forecasting techniques that enhance grid stability, optimize resource management, and enable effective electricity pricing strategies. It is a must-have reference for anyone involved in the clean energy sector.

Forecasting techniques in renewable power generation, demand response, and electricity pricing are vital for grid stability, optimal resource allocation, efficient energy management, and cost-effective electricity supply. They enable grid operators and market participants to make informed decisions, mitigate risks, and enhance the overall reliability and sustainability of the electrical grid. Electricity prices can vary significantly based on supply and demand dynamics. By forecasting expected demand and the availability of generation resources, market operators can optimize electricity pricing strategies. This alignment of prices with anticipated supply-demand balance incentivizes the efficient use of electricity and promotes market efficiency. Accurate forecasting helps prevent price spikes, reduces market uncertainties, and supports the development of effective energy trading strategies.

This book presents these topics and trends in an encyclopedic format, serving as a go-to reference for engineers, scientists, or students interested in the subject. The book is divided into three easy-to-navigate sections that thoroughly examine the AI and machine learning-based algorithms and pseudocode considered in this study. This is the most comprehensive and up-to-date encyclopedia of forecasting in renewable power generation, demand response, and electricity pricing ever written, and is a must-have for any library.

Table of contents

• Cover
• Series Page
• Title Page
• Copyright Page
• Contents
• Preface
• Chapter 1 Solar Power Forecasting Using Hybrid Deep Learning Networks Combined with Variational Mode Decomposition
• Chapter 2 Location Analysis and Environmental Validation for Installation of Hybrid Solar-Wind Energy Generation System in Hilly Areas of Uttarakhand: Study Toward Forecasting
• Chapter 3 Harnessing Wind Energy: Ontological Frameworks for Optimizing Wind Turbine Lifecycle Management and Performance
• Chapter 4 Statistical Forecasting Model for Solar Power Generation Under Different Environmental Conditions
• Chapter 5 Understanding Forecasting Models for Renewable Energy Generation and Market Operation
• Chapter 6 Machine Learning Techniques for Demand Forecasting in the Electricity Sector
• Chapter 7 Evaluation and Performance Metrics for Forecasting Renewable Power Generation, Demand, and Electricity Price
• Chapter 8 Forecasting Electricity Prices Using NNAR Approach: An Emerging Nation Experience
• Chapter 9 Machine Learning–Enabled Solar Photovoltaic Energy Forecasting for Modern-Day Grid Integration: A Virtual Power Plant Perspective
• Chapter 10 Scenario Analysis and Practical Approach of Deep Learning and Machine Learning Techniques in the Renewable Energy Sector
• Chapter 11 Application of Artificial Intelligence and Machine Learning in Assessing Solar Energy Potential
• Chapter 12 Revolutionizing Solar PV Forecasting with Machine Learning Techniques
• Chapter 13 Machine Learning–Based Prediction of Electrical Load in the Context of Variable Weather Conditions
• Chapter 14 Recent Advancement in Renewable Energy with Artificial Intelligence and Machine Learning
• About the Editors
• Index
• Also of Interest
• EULA