AI-Based Forecasting of Solar Photovoltaics Power Generation
Will be published approx. on 1. February 2026
Book
Hardback
312 pages
978-1-83724-019-7 (ISBN)
Description
The widespread deployment of photovoltaics (PV) systems has emerged as a key element in the global shift toward a carbon-neutral and sustainable energy systems. Driven by a combination of supportive regulatory frameworks, government incentive programs, technical developments, and increasing environmental awareness, the adoption of PV technologies has witnessed remarkable growth in recent years. However, the rapid integration of distributed PV systems into existing electricity grid infrastructure introduces new challenges, particularly concerning voltage regulation, reverse power flow, and congestion within the electricity grid. These issues are intensified when PV systems are integrated without proper strategy. In this context, solar PV power forecasting has become an essential tool for ensuring the reliable and efficient integration of solar PV systems into power systems. Artificial intelligence (AI) and machine learning (ML) offer means to forecast PV power and energy generation based on historical data of PV generation, meteorological data, and/or weather forecasts.
AI-Based Forecasting of Solar Photovoltaics Power Generation blends theoretical knowledge with practical case studies, which serves as a comprehensive and timely contribution to the rapidly evolving field of solar PV forecasting. It covers topics such as data collection and processing, solar forecasting based on statistical time-series, machine and deep learning, hybrid and probabilistic approaches, model optimization, hyperparameter tuning, and solar PV forecasting for energy system integration and control.
As solar PV systems become increasingly integrated into energy systems, a dedicated book on PV generation forecasting is incredibly useful, making this book an important resource for energy system operators, policymakers, researchers, and students seeking to improve the reliability, resiliency, and efficiency of solar PV systems and the broader systems into which they are integrated.
AI-Based Forecasting of Solar Photovoltaics Power Generation blends theoretical knowledge with practical case studies, which serves as a comprehensive and timely contribution to the rapidly evolving field of solar PV forecasting. It covers topics such as data collection and processing, solar forecasting based on statistical time-series, machine and deep learning, hybrid and probabilistic approaches, model optimization, hyperparameter tuning, and solar PV forecasting for energy system integration and control.
As solar PV systems become increasingly integrated into energy systems, a dedicated book on PV generation forecasting is incredibly useful, making this book an important resource for energy system operators, policymakers, researchers, and students seeking to improve the reliability, resiliency, and efficiency of solar PV systems and the broader systems into which they are integrated.
More details
Series
Language
English
Place of publication
Stevenage
United Kingdom
Target group
College/higher education
Professional and scholarly
Product notice
sewn/stitched
Cloth over boards
Dimensions
Height: 234 mm
Width: 156 mm
Thickness: 19 mm
Weight
603 gr
ISBN-13
978-1-83724-019-7 (9781837240197)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
Elham Shirazi is an assistant professor at the University of Twente, The Netherlands. Her multidisciplinary research focuses on applying AI/ML methods to forecasting, integration, and control of energy systems. She joined the University of Twente in 2021, following postdoctoral research at KU Leuven's and prior work at IMEC's Energy Department in Belgium. She is a member of IEA PVPS and ETIP PV, and serves on technical committees for EUPVSEC, IEEE ISGT, and ACM e-Energy.
Wilfried van Sark is a professor in photovoltaics integration at Copernicus Institute of Sustainable Development, Utrecht University, The Netherlands. He has over 40 years' experience in PV solar energy R&D. His research includes next generation PV, performance analysis of photovoltaic modules and systems, smart grids with EV and vehicle-to-grid technology, and solar forecasting. He is associate editor or board member for several related journals and a senior member of IEEE.
Wilfried van Sark is a professor in photovoltaics integration at Copernicus Institute of Sustainable Development, Utrecht University, The Netherlands. He has over 40 years' experience in PV solar energy R&D. His research includes next generation PV, performance analysis of photovoltaic modules and systems, smart grids with EV and vehicle-to-grid technology, and solar forecasting. He is associate editor or board member for several related journals and a senior member of IEEE.
Content
Chapter 1: Introduction to solar photovoltaics generation forecast
Chapter 2: Data, data collection, and pre-processing for solar photovoltaics forecast
Chapter 3: Statistical time-series based solar photovoltaics forecast
Chapter 4: Machine learning based solar photovoltaics forecast
Chapter 5: Deep learning based solar photovoltaics forecast
Chapter 6: Ensemble and hybrid approaches for solar photovoltaics forecast
Chapter 7: Probabilistic approaches for solar photovoltaics forecast
Chapter 8: Model optimisation, hyperparameter tuning, and performance evaluation of AI-based solar photovoltaics forecast
Chapter 9: Sky imager based solar photovoltaics forecast
Chapter 10: Solar photovoltaics forecasting for energy system integration and control
Chapter 2: Data, data collection, and pre-processing for solar photovoltaics forecast
Chapter 3: Statistical time-series based solar photovoltaics forecast
Chapter 4: Machine learning based solar photovoltaics forecast
Chapter 5: Deep learning based solar photovoltaics forecast
Chapter 6: Ensemble and hybrid approaches for solar photovoltaics forecast
Chapter 7: Probabilistic approaches for solar photovoltaics forecast
Chapter 8: Model optimisation, hyperparameter tuning, and performance evaluation of AI-based solar photovoltaics forecast
Chapter 9: Sky imager based solar photovoltaics forecast
Chapter 10: Solar photovoltaics forecasting for energy system integration and control