Short-Term Wind and Photovoltaic Power Correlation Probability Interval Prediction Method Based on Similar Day Clustering and WOA-BiLSTM-Copula Algorithm
System Analysis & Operation|更新时间:2025-09-30
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Short-Term Wind and Photovoltaic Power Correlation Probability Interval Prediction Method Based on Similar Day Clustering and WOA-BiLSTM-Copula Algorithm
“In the field of constructing new power systems, experts have proposed a short-term wind solar power correlation probability interval prediction model based on WOA BiLSTM Copula algorithm, which effectively improves the prediction accuracy.”
Southern Power System TechnologyVol. 19, Issue 8, Pages: 44-52(2025)
作者机构:
中国南方电网电力调度控制中心,广州 510663
作者简介:
基金信息:
the National Natural Science Foundation of China(41875118);the Youth Program of National Natural Science Foundation of China(41805047)
WANG Lingzi,SHEN Haibo,DENG Liyuan,et al.Short-Term Wind and Photovoltaic Power Correlation Probability Interval Prediction Method Based on Similar Day Clustering and WOA-BiLSTM-Copula Algorithm[J].Southern Power System Technology,2025,19(08):44-52.
WANG Lingzi,SHEN Haibo,DENG Liyuan,et al.Short-Term Wind and Photovoltaic Power Correlation Probability Interval Prediction Method Based on Similar Day Clustering and WOA-BiLSTM-Copula Algorithm[J].Southern Power System Technology,2025,19(08):44-52. DOI: 10.13648/j.cnki.issn1674-0629.2025.08.005.
Short-Term Wind and Photovoltaic Power Correlation Probability Interval Prediction Method Based on Similar Day Clustering and WOA-BiLSTM-Copula Algorithm