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.
