Solar Power Forecasting

Using Time Series and Machine Learning
1. Auflage 2026
ISBN: 978-1-032-51695-0
Verlag: Taylor & Francis

Buch, Englisch, 206 Seiten, Format (B × H): 156 mm x 234 mm

Reihe: Operations Research Series

This book takes an approach that leverages methods using time series analysis, machine learning, and stochastic models to effectively forecast solar power. The goal of this book is not only to produce an accurate forecast but also to make it conducive to being used for decision-making.

Solar Power Forecasting: Using Time Series and Machine Learning combines traditional forecasting with recent advances in machine learning and data science. It uses a decision-making-oriented approach and provides probabilistic forecasts and methods as well as explains the analytical underpinnings of accuracy metrics in detail. As it illustrates through examples of how forecasting can be used in planning and operations, the book also delivers a systems-level approach.

This comprehensive resource covers various aspects of solar forecasting, including data science methods, computational techniques, and mathematical foundations. It serves as a valuable tool for practitioners, students, and experienced researchers, both in the solar power industry and in the broader field of forecasting.

Color figures can be found on Routledge.com/9781032515328

Zielgruppe

Professional Practice & Development and Undergraduate Advanced

Autoren/Hrsg.

Gautam, Natarajan

Fachgebiete

• Mathematik | Informatik Mathematik Operations Research
• Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
• Technische Wissenschaften Energietechnik | Elektrotechnik Elektrotechnik
• Technische Wissenschaften Technik Allgemein Industrial Engineering

Inhaltsverzeichnis

1. Introduction. 2. Forecasting. 3. Short-Term Solar Forecasts. 4. Day-Ahead Solar Forecasts. 5. Day-Ahead Planning. 6. Distributional Forecasts.

Über Autor(innen)

N. Gautam has been a Professor in the Department of Electrical Engineering and Computer Science at Syracuse University, NY, USA, since January 2022. Before that, he was a faculty member at Texas A&M for over 16 years and at Penn State for 8 years. In addition, Dr. Gautam has been an Amazon Scholar since Fall 2019. His research focuses on optimization and control of stochastic systems with applications in computer-communication networks, renewable energy systems, real-time logistics, and smart manufacturing. Dr. Gautam is a Fellow of the Institute for Industrial and Systems Engineers (IISE).