July 31, 2025Journal of Electrical Systems and Information TechnologyOpen Access

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

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Authors

IBIan B. Benitez JSJai Govind Singh

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Implication

Comprehensive review identifies challenges and solutions in forecasting solar PV and wind turbine power output using machine learning, highlighting implications for renewable energy reliability.

Key Points

Machine learning plays a crucial role in accurately forecasting solar PV and wind turbine power output.
Key meteorological variables significantly enhance forecasting accuracy, as shown through comprehensive testing methods.
Challenges in data availability and model complexity need innovative solutions for improved renewable energy forecasting.
Advanced machine learning techniques and big data integration are essential for enhancing forecasting reliability across different climates.

Cite This Study

Benitez et al. (2025) studied this question.

synapsesocial.com/papers/689a0c5fe6551bb0af8cf5d8 https://doi.org/10.1186/s43067-025-00239-4

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Implication

Comprehensive review identifies challenges and solutions in forecasting solar PV and wind turbine power output using machine learning, highlighting implications for renewable energy reliability.