Intelligent Data Analytics for Solar Energy Prediction and Forecasting

Grab the future of renewable power with Intelligent Data Analytics for Solar Energy Prediction and Forecasting by Amit Kumar Yadav, Hasmat Malik, and Majed A. Alotaibi — an essential resource for engineers, data scientists, policy makers, and utility operators focused on reliable solar energy integration.

This book opens with clear, practical explanations of core concepts—solar irradiance, photovoltaic (PV) behavior, and time-series characteristics—then advances to state-of-the-art machine learning and statistical models used in solar forecasting. Readers will find step-by-step walkthroughs of predictive techniques including regression, neural networks, ensemble learning, and hybrid approaches, plus guidance on feature engineering, error metrics, and model selection tailored for PV systems and distributed generation.

What sets this volume apart is its emphasis on real-world applicability: illustrative case studies, regional data interpretation, and strategies for improving grid stability and energy trading. Whether you’re optimizing short-term dispatch or planning multi-day generation schedules, the book translates complex algorithms into actionable insights that reduce forecasting error and increase operational confidence.

Key benefits:
– Gain hands-on understanding of predictive models for solar energy forecasting.
– Learn how to preprocess meteorological and satellite data for better accuracy.
– Apply scalable approaches for grid integration and demand-response planning.
– Suitable for practitioners across India, the Middle East, Europe, North America, and beyond.

If you need a single authoritative guide that bridges advanced data analytics with practical solar forecasting needs, this title delivers. Enhance forecasting accuracy, streamline operations, and future-proof your renewable energy projects—order your copy today and start turning data into dependable solar power.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.