SHORT-TERM PHOTOVOLTAIC POWER INTERVAL PREDICTION BASED ON INFORMATION ENTROPY VARIABLE WEIGHT INTERVAL COMBINATION AND BOUNDARY APPROXIMATION

被引：0
|
作者：
Yang G. [1 ,2 ]
Li J. [1 ]
Wang D. [1 ,2 ]
Zhang K. [1 ]
Liu J. [1 ]
机构：
[1] College of Electrical Engineering, Xi’an University of Technology, Xi’an
[2] Xi’an Key Laboratory of Smart Energy, Xi’an University of Technology, Xi’an
来源：
Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2023年 / 44卷 / 02期
关键词：
adaptive weight; boundary approximation; combined prediction; feature selection; interval prediction; photovoltaic power;
D O I：
10.19912/j.0254-0096.tynxb.2021-1042
中图分类号：
学科分类号：
摘要：
Aiming at the problem that the interval width is too wide while the existing interval prediction satisfies the high coverage rate, a short-term photovoltaic power interval prediction method was proposed based on the interval combination of information entropy variable weight and boundary approximation. Firstly, the features of historical weather data were reconstructed, and the reconstructed features were screened based on LASSOCV-RFE algorithm. Then, dynamic Bayesian network model and improved quantile regression model based on convolutional long and short-term memory network (CNN-LSTM-QH) were used to predict the confidence interval of photovoltaic output, and the interval variable weight combination was carried out according to the information entropy. Finally, combining with the interval coverage and interval width indexes, the boundary approximation function and penalty boundary were constructed, and the weighted combination of the two prediction results was used to approximate the boundary of the interval. Simulation results show that the proposed method can reduce the average interval widths of 21.86%, 16.67% and 14.93% respectively at 95%, 90% and 85% confidence levels, and the interval coverage also meets the corresponding confidence level requirements. © 2023 Science Press. All rights reserved.
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页码：381 / 390
页数：9
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