Hardcover. 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. This book conveys approaches for using AI for improved PV forecasting, which is imperative in increasing the share of clean power to achieve decarbonisation of the energy system. Chapters cover machine and deep learning, evaluation, grid integration and case studies. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

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Detalles bibliogr�ficos

T�tulo: AI-Based Forecasting of Solar Photovoltaics Power Generation (Hardcover)
Editor: Institution of Engineering and Technology, Stevenage
Fecha de publicaci�n: 2026
Idioma: Ingl�s
ISBN 10: 1837240191
ISBN 13: 9781837240197
Encuadernaci�n: Hardcover
Estado: new

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Sinopsis

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.

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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 the 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 an associate editor or board member for several related journals and a senior member of IEEE.

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