A double deep reinforcement learning-based adaptive framework for decision-optimal wind power interval prediction

Chenghan Li, Ye Guo and Yinliang Xu

Energy, 2025, vol. 329, issue C

Abstract: Prediction intervals (PI) effectively quantify forecasting uncertainty and serve as inputs for subsequent decision-making tasks. While it is traditionally assumed that reducing prediction errors will correspondingly reduce decision errors, this assumption is not invariably valid. This paper introduces an adaptive decision-optimal framework for optimal interval forecasting in wind power, designed to alleviate the economic dispatch challenges posed by wind power uncertainty within power systems. Methodologically, this framework employs a close-loop method based on the Double Deep Q-Network algorithm, where the forecasting module leverages a pre-trained model with Bi-Directional Long Short-Term Attention to enhance extracting features of historical data and increase quantile forecast precision. Then, Double Deep Q-Network can select decision-optimal quantile proportions. The validity of the framework is demonstrated through experiments utilizing real-world wind power data from the Belgian Elia company, validated across IEEE 6-bus and 30-bus cases. The method decreased average operation cost and risk by 0.36%/4.38% in the IEEE 6-bus and 0.76%/6.29% in the IEEE 30-bus compared to benchmarks. This framework offers a robust solution for wind power probability forecasting and the decision of power systems, thereby enhancing the stability and economic efficiency of power system operations.

Keywords: Quantile prediction; Long short-term memory; Attention mechanism; Reinforcement learning; Double Deep Q-Network (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544225023035
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:energy:v:329:y:2025:i:c:s0360544225023035

DOI: 10.1016/j.energy.2025.136661

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

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