基于信息熵变权区间组合和边界逼近的短期光伏功率区间预测

杨国清; 李建基; 王德意; 张凯; 刘菁

doi:10.19912/j.0254-0096.tynxb.2021-1042

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太阳能学报 ›› 2023, Vol. 44 ›› Issue (2) : 381-390. DOI: 10.19912/j.0254-0096.tynxb.2021-1042

基于信息熵变权区间组合和边界逼近的短期光伏功率区间预测

杨国清^1,2, 李建基¹, 王德意^1,2, 张凯¹, 刘菁¹

作者信息 +

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

Yang Guoqing^1,2, Li Jianji¹, Wang Deyi^1,2, Zhang Kai¹, Liu Jing¹

Author information +

文章历史 +

摘要

针对现有的区间预测在满足高覆盖率的同时区间宽度存在过宽的问题,提出一种基于信息熵变权区间组合和边界逼近的短期光伏功率区间预测方法。首先,对历史天气数据特征进行特征重组,并基于套索交叉的递归特征消除（LassoCV-RFE）算法对重组后的特征进行筛选。然后,采用动态贝叶斯网络模型和基于卷积长短期记忆网络的改进分位数回归模型（CNN-LSTM-QH）分别预测光伏出力的置信区间,根据信息熵进行区间变权组合。最后,结合区间覆盖率和区间宽度指标,构建边界逼近函数和惩罚边界,对两个预测结果加权组合后的区间进行边界逼近。仿真结果表明：相比于一般的单一模型方法,所提方法能在95%、90%和85%的置信水平下分别减小21.86%、16.67%和14.93%的平均区间宽度,同时区间覆盖率也能满足对应的置信度要求。

Abstract

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.

导出引用

杨国清, 李建基, 王德意, 张凯, 刘菁. 基于信息熵变权区间组合和边界逼近的短期光伏功率区间预测[J]. 太阳能学报. 2023, 44(2): 381-390 https://doi.org/10.19912/j.0254-0096.tynxb.2021-1042

Yang Guoqing, Li Jianji, Wang Deyi, Zhang Kai, Liu Jing. SHORT-TERM PHOTOVOLTAIC POWER INTERVAL REDICTION BASED ON INFORMATION ENTROPY VARIABLE WEIGHT INTERVAL COMBINATION AND BOUNDARY APPROXIMATION[J]. Acta Energiae Solaris Sinica. 2023, 44(2): 381-390 https://doi.org/10.19912/j.0254-0096.tynxb.2021-1042

中图分类号： TM615

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