A reinforcement learning-based ensemble forecasting framework for renewable energy forecasting

Zhiyuan Wu, Guohua Fang, Jian Ye, David Z. Zhu and Xianfeng Huang

Renewable Energy, 2025, vol. 244, issue C

Abstract: The randomness and intermittency of wind and photovoltaic power generation can negatively affect the stability of power systems. Therefore, accurate forecasting of these energy outputs is crucial for effective power system management. Among various forecasting methods, ensemble forecasting has gained attention for its superior performance and reliability. However, traditional ensemble methods, such as weighted averaging and stacking, use fixed model combinations that fail to adapt to varying wind and radiation conditions, thereby limiting their accuracy. To overcome this limitation, this study proposes a novel ensemble forecasting framework based on reinforcement learning. The framework uses a deep Q-network to dynamically select the appropriate base model for different wind and radiation conditions. The learning process is supported by a model control module, a basic forecasting module, and a performance evaluation module. Independent experiments conducted across 14 regions in China validate the framework's effectiveness, showing a significant improvement in forecasting accuracy. The framework achieved an average improvement of 12.18 % in mean absolute scaled error over base models and 4.84 % over other ensemble methods. Additionally, this study analyzes the impact of different reinforcement learning models and sample sizes on the framework's performance.

Keywords: Wind power forecasting; Photovoltaic power forecasting; Reinforcement learning; Ensemble forecasting; Power system management (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960148125003544
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:244:y:2025:i:c:s0960148125003544

DOI: 10.1016/j.renene.2025.122692

Access Statistics for this article

Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides

More articles in Renewable Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().