南方电网技术2025，Vol.19Issue(8)：44-52,9.DOI:10.13648/j.cnki.issn1674-0629.2025.08.005
基于相似日聚类与WOA-BiLSTM-Copula算法的短期风光功率相关性概率区间预测方法
Short-Term Wind and Photovoltaic Power Correlation Probability Interval Prediction Method Based on Similar Day Clustering and WOA-BiLSTM-Copula Algorithm
摘要
Abstract
Wind and photovoltaic power generation exhibit strong randomness and volatility.Improving the prediction accuracy is of great significance for constructing new power systems.Considering the certain correlation between wind and photovoltaic output in the same region,a short-term wind and photovoltaic power correlation probability interval prediction model based on similar day clustering and whale optimization algorithm-bidirectional long short-term memory neural network-Copula(WOA-BiLSTM-Copula)algorithm is proposed.Firstly,the K-means clustering algorithm is adopted to divide the numerical weather prediction(NWP)dataset,and wind and photovoltaic joint output typical similar day scenarios with correlation are extracted based on Kendall and Spearman cor-relation coefficients.Secondly,the non-parametric kernel density estimation method is used to establish Copula model for similar day scenes,and the optimal Copula function types of wind and photovoltaic output are determined.Then,the WOA is trained to optimize the BiLSTM and perform point predictions on wind power and photovoltaic power.Finally,the Monte Carlo method is used to sample the optimal Copula function,and the correlation probability prediction interval is generated based on wind and photovoltaic point prediction values.The simulation results show that the proposed model can effectively extract the correlation characteristics of wind and photovoltaic output,and the accuracy is higher than the existing models,which verifies the effectiveness of the model.关键词
风光出力预测/相似日聚类/最优Copula函数建模/鲸鱼优化算法/双向长短时记忆神经网络/概率区间预测Key words
wind and photovoltaic power output prediction/similar day clustering/optimal Copula function modeling/whale optimiza-tion algorithm/bidirectional long short-term memory neural network/probability interval prediction分类
信息技术与安全科学引用本文复制引用
王凌梓,沈海波,邓力源,刘显茁,邓韦斯..基于相似日聚类与WOA-BiLSTM-Copula算法的短期风光功率相关性概率区间预测方法[J].南方电网技术,2025,19(8):44-52,9.基金项目
国家自然科学基金资助项目(41875118) （41875118）
国家自然科学基金青年基金资助项目(41805047).Supported by the National Natural Science Foundation of China(41875118) （41805047）
the Youth Program of National Natural Science Foundation of China(41805047). （41805047）
