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Forecasting Renewable Energy Generation Using Random Forest Analysis

In: Technology Management for Intelligent, Open and Responsible Organizations and Ecosystems

Author

Listed:
  • Chien-Hsin Wu

    (Tamkang University)

  • Yao-Ting Tseng

    (Tamkang University)

  • Wen-Fang Lo

    (Tamkang University)

  • Yu-Hsiang Huang

    (Tamkang University)

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.

Suggested Citation

  • Chien-Hsin Wu & Yao-Ting Tseng & Wen-Fang Lo & Yu-Hsiang Huang, 2026. "Forecasting Renewable Energy Generation Using Random Forest Analysis," Springer Proceedings in Business and Economics, in: Fabiano Armellini & Syrine Njah & Elaine Mosconi & Breno Nunes (ed.), Technology Management for Intelligent, Open and Responsible Organizations and Ecosystems, pages 341-346, Springer.
    • Handle: RePEc:spr:prbchp:978-3-032-23282-3_41
    DOI: 10.1007/978-3-032-23282-3_41
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