AI-Based Forecasting of Solar Photovoltaics Power Generation

Author:   Elham Shirazi (Assistant Professor, University of Twente, The Netherlands) ,  Wilfried van Sark (Professor, Utrecht University, Copernicus Institute of Sustainable Development, The Netherlands)
Publisher:   Institution of Engineering and Technology
ISBN:  

9781837240197


Pages:   312
Publication Date:   01 February 2026
Format:   Hardback
Availability:   Not yet available   Availability explained
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AI-Based Forecasting of Solar Photovoltaics Power Generation


Overview

The widespread deployment of photovoltaics (PV) technology has emerged as a key element in the global shift toward a carbon-neutral and sustainable energy system. 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, serving 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.

Full Product Details

Author:   Elham Shirazi (Assistant Professor, University of Twente, The Netherlands) ,  Wilfried van Sark (Professor, Utrecht University, Copernicus Institute of Sustainable Development, The Netherlands)
Publisher:   Institution of Engineering and Technology
Imprint:   Institution of Engineering and Technology
ISBN:  

9781837240197


ISBN 10:   1837240191
Pages:   312
Publication Date:   01 February 2026
Audience:   College/higher education ,  Professional and scholarly ,  Tertiary & Higher Education ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Forthcoming
Availability:   Not yet available   Availability explained
This item is yet to be released. You can pre-order this item and we will dispatch it to you upon its release.

Table of Contents

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

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Author Information

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.

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