Journal Article

A comprehensive review of machine learning applications in forecasting solar PV and wind turbine power output

Benitez, Ian B., Singh, Jai Govind

2025

Overview

With climate change driving the global push toward sustainable energy, the reliability of power systems increasingly depends on accurate forecasting methods. This study examined the role of machine learning (ML) in forecasting solar PV power output (SPVPO) and wind turbine power output (WTPO) and identified the challenges posed by the intermittent nature of these renewable energy sources. This study examined the current techniques, challenges, and future directions in ML-based forecasting of SPVPO and WTPO and proposed a standardized framework. Using the Mann–Whitney and Kruskal–Wallis tests, the results highlight the significant impact of key meteorological and operational variables on enhancing forecasting accuracy, as measured by MAPE and R-squared. Key features for SPVPO forecasting include solar irradiance, ambient temperature, and prior SPVPO, while wind speed, turbine speed, and prior wind power output are crucial for WTPO forecasting. Moreover, ensemble models, support vector machines, Gaussian processes, hybrid artificial neural networks, and decomposition-based hybrid models exhibit promising forecasting accuracy and reliability. Challenges such as data availability, complexity-interpretability trade-offs, and integration difficulties with energy management systems present opportunities for innovative solutions. These include exploring advanced data processing and calibration techniques, leveraging Big Data and IoT advancements, formulating advanced machine learning (ML) techniques, and employing probabilistic approaches with desirable accuracy and robustness in forecasting solar photovoltaic power output (SPVPO) and wind turbine power output (WTPO). Additionally, expanding research to ensure model generalizability across diverse climate conditions and forecasting horizons is crucial for enhancing the reliability and efficiency of renewable energy forecasting using machine learning techniques.

• Publisher: Springer Berlin Heidelberg,Springer Nature B.V,SpringerOpen
• Subject: Accuracy / Alternative energy sources / Ambient temperature / Artificial Intelligence / Artificial neural networks / Big Data / Climate change / Core principles and forecasting accuracy / Data processing / Decision making / Decision trees / Deep learning / Electrical Engineering / Electrical Machines and Networks / Energy industry / Energy management systems / Engineering / Feature selection / Forecasting / Forecasting techniques / Gaussian process / Humidity / Information Systems and Communication Service / Irradiance / Machine learning / Machine learning-based forecasting techniques / Mechanical Engineering / Neural networks / Optimization / Photovoltaic cells / Power Electronics / R&D / Renewable energy sources / Renewable resources / Research & development / Review / Solar and energy forecasting models / Solar PV power output forecasting / Support vector machines / System reliability / Weather forecasting / Wind power / Wind speed / Wind turbine power output forecasting / Wind turbines