摘要
针对光伏功率序列波动性强、随机性高的问题,文中提出一种基于Q-Learning的多模态自适应光伏功率优化组合预测模型。首先,采用鲸鱼优化算法的变分模态分解方法,将原始光伏功率序列分解成不同子模态,并通过集成特征筛选模型,确定各子模态序列最敏感的气象因素。然后,构建反向传播神经网络、双向长短期记忆网络、门控循环单元网络和时间卷积网络4种基础预测模型。考虑到不同模型对不同频率特征的子序列预测能力不同,利用Q-Learning算法自适应选择各模态对应的最优基础模型组合方式。最后,将不同子模态的预测结果叠加重构,得到最终预测结果,并利用高分辨率光伏气象功率数据集进行验证。结果证明,文中所提出的基于Q-Learning的多模态自适应光伏功率优化组合预测模型,相较于单一模型的预测误差平均绝对误差下降了16.18%,均方误差下降了17.00%。
To address the challenges of high volatility and stochasticity in photovoltaic(PV)power series,a multi-modal adaptive PV power optimization forecasting model based on Q-Learning is proposed.The original PV power series are first decomposed into different submodalities using the variational mode decomposition algorithm optimized by the whale optimization algorithm.An integrated feature selection model is then employed to identify the most sensitive meteorological features for each submodal series.Four basic forecasting models:back propagation neural network,bidirectional long shortterm memory,gated recurrent unit and temporal convolutional network,are constructed to predict the power sub-series.Given that different models exhibit varying forecasting abilities for sub-series with different frequency characteristics,Q-Learning is utilized to adaptively select the optimal combination of forecasting models for each modality.The final forecasting result is obtained by superimposing and reconstructing the forecasts of the different submodalities.The proposed model is validated using a high-resolution PV meteorological power dataset.The results demonstrate that the proposed multi-modal adaptive photovoltaic power optimization and combination forecasting based on Q-Learning achieved a 16.18%reduction in mean absolute error and a 17.00%reduction in mean squared error compared to the single model.
作者
隗知初
杨苹
周钱雨凡
陈文皓
万思洋
崔嘉雁
WEI Zhichu;YANG Ping;ZHOU Qianyufan;CHEN Wenhao;WAN Siyang;CUI Jiayan(School of Electric Power Engineering,South China University of Technology,Guangzhou 510640,China;Guangdong Key Laboratory of Green Energy Technology,South China University of Technology,Guangzhou 510640,China)
出处
《电力工程技术》
北大核心
2026年第1期115-124,163,共11页
Electric Power Engineering Technology
基金
国家重点研发计划资助项目(2023YFB4203102)
广东省海洋经济发展(海洋六大产业)专项资金项目(GDNRC[2023]27)。
关键词
鲸鱼优化算法
变分模态分解
Q-LEARNING
功率预测
组合模型
光伏发电
whale optimization algorithm
variational mode decomposition
Q-Learning
power forecasting
combination model
photovoltaic(PV)power generation
