Forecasting Renewable Energy Generation Using Random Forest Analysis

Chien-Hsin Wu (), Yao-Ting Tseng (), Wen-Fang Lo () and Yu-Hsiang Huang ()
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Chien-Hsin Wu: Tamkang University
Yao-Ting Tseng: Tamkang University
Wen-Fang Lo: Tamkang University
Yu-Hsiang Huang: Tamkang University

A chapter in Technology Management for Intelligent, Open and Responsible Organizations and Ecosystems, 2026, pp 341-346 from Springer

Abstract: Abstract Renewable energy forecasting is critical for sustainable energy development and grid stability. This study applies a Random Forest model to analyse the contribution of different renewable energy sources to total energy generation in Taiwan. The results indicate that geothermal and solar energy have the highest impact, while offshore wind, onshore wind, and conventional hydropower contribute less significantly. This analysis aligns with previous research and highlights the necessity of optimizing renewable energy strategies. By leveraging machine learning techniques, policymakers can gain deeper insights into renewable energy trends and make data-driven decisions to enhance energy security and efficiency. The study also suggests that further improvements in wind energy forecasting could contribute to better grid stability. Future research could explore hybrid machine learning approaches to refine predictive accuracy and model robustness.

Keywords: Renewable Energy; Forecasting; Random Forest; Energy Policy; Machine Learning (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-032-23282-3_41

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DOI: 10.1007/978-3-032-23282-3_41

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