朱涛,杨欢红,肖峰,李光,李广一,朱伟星,叶婧元
ZHU Tao,YANG Huanhong,XIAO Feng,LI Guang,LI Guangyi,ZHU Weixing,YE Jingyuan

• 1:上海华电奉贤热电有限公司
• 2:上海电力大学

摘要(Abstract)：

光伏发电在电力系统中占比不断提高，实现准确的光伏出力概率预测能够有效辅助电网调控运行。为了提高概率预测精度，提出了一种基于改进灰狼算法和TCN-QRF（时间卷积神经网络-分位数随机森林）的光伏出力概率预测方法。首先将完成预处理的光伏出力时间序列数据集转换为监督学习数据集；然后使用TCN提取光伏出力时序特征作为QRF的输入，构建TCN-QRF模型；最后，基于非线性收敛因子和高斯突变策略改进灰狼算法，使用改进灰狼算法完成TCN-QRF超参数的高效选择，实现了更精准的光伏出力概率预测。
As the share of photovoltaic(PV) power generation grows increasingly within electric power systems, the accurate probabilistic forecasting of PV output can be help for grid regulation and operation. To enhance forecasting precision, a probabilistic forecasting method for ultra-short-term photovoltaic output using the improved grey wolf optimization(GWO), temporal convolutional neural networks and quantile random forests(TCN-QRF) is proposed.Firstly, the preprocessed time series dataset for PV output is converted into a supervised learning dataset. Then, the TCN is used to extract the temporal features of PV output as the input to the QRF, constructing the TCN-QRF model. Finally, the GWO is improved using the nonlinear convergence factor and Gaussian mutation strategy. The improved GWO efficiently selects hyperparameters for TCN-QRF, enabling a more precise probabilistic forecasting of PV output.

关键词(KeyWords)： 改进灰狼算法;概率预测;光伏出力;TCN-QRF
improved GWO;probabilistic forecasting;PV output;TCN-QRF

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金（52177100）;; 中国华电集团公司科技项目（CHDKJ23-02-233）

作者(Author): 朱涛,杨欢红,肖峰,李光,李广一,朱伟星,叶婧元
ZHU Tao,YANG Huanhong,XIAO Feng,LI Guang,LI Guangyi,ZHU Weixing,YE Jingyuan

DOI: 10.19585/j.zjdl.202408010

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