基于Q学习组合模型的光伏电站发电功率预测方法研究

唐仲杰; 寇文珍; 喻雪

doi:doi:10.19912/j.0254-0096.tynxb.2024-1153

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基于Q学习组合模型的光伏电站发电功率预测方法研究

更新时间：2025-10-22

- 基于Q学习组合模型的光伏电站发电功率预测方法研究
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- 太阳能学报 2025年46卷第9期页码：618-625
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- DOI：doi:10.19912/j.0254-0096.tynxb.2024-1153
  中图分类号： TM615
- 网络出版：2025-10-14，
  
  纸质出版：2025
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唐仲杰, 寇文珍, 喻雪. 基于Q学习组合模型的光伏电站发电功率预测方法研究[J]. 太阳能学报, 2025,46(9):618-625.
唐仲杰, 寇文珍, 喻雪. 基于Q学习组合模型的光伏电站发电功率预测方法研究[J]. 太阳能学报, 2025,46(9):618-625. DOI： doi:10.19912/j.0254-0096.tynxb.2024-1153.

DOI：

摘要

针对单一模型在预测光伏电站发电功率易受自身固有局限影响导致的预测精度不理想以及传统组合模型权重恒定非最优而未充分挖掘其预测潜力的问题

提出基于Q学习组合模型的预测方法。首先

通过Pearson相关系数选取与发电功率有强相关性的气象特征和时间序列作为输入特征;其次

分别采用极限梯度提升和长短期记忆网络两个单一模型对发电功率进行预测;最后

采用Q学习算法对历史样本在线滚动优化实时更新每次初步预测的组合权重

从而实现最优时变权重组合。结果表明

相比于其他模型

所提方法能显著提升预测精度和效果

验证了该方法的有效性。

Abstract

Aiming at the problem that the forecast of a single model in forecasting the generation power of photovoltaic power plants is not ideal due to its inherent limitations

and that the weight of the traditional combination model is constant and not optimal

and its forecast potential is not fully exploited

a prediction method based on the Q-learning combination model is proposed. Firstly

meteorological features and time series with strong correlation with power generation are selected as input features through Pearson correlation coefficient; Secondly

two single models namely XGBoost（eXtreme Gradient Boosting） and LSTM（Long Short Term Memory） are used to forecast the generation power; Finally

the Q-learning algorithm is used to perform online rolling optimization on historical samples and update the combination weights of each preliminary forecast in real-time

thereby achieving the optimal time-varying weight combination. The experimental results show that compared to other models

the method can significantly improve forecast accuracy and effectiveness

verifying the effectiveness of this method.

关键词

Keywords

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