Short-Term Load Forecasting Based on the CEEMDAN-Sample Entropy-BPNN-Transformer

被引:26
|
作者
Huang, Shichao [1 ]
Zhang, Jing [1 ]
He, Yu [1 ]
Fu, Xiaofan [1 ]
Fan, Luqin [1 ]
Yao, Gang [2 ]
Wen, Yongjun [3 ]
机构
[1] Guizhou Univ, Coll Elect Engn, Guiyang 550025, Peoples R China
[2] Guizhou Power Grid Co, Guiyang 550001, Peoples R China
[3] Pujiang Guangyuan Power Construct Co Ltd, Jinhua 322200, Zhejiang, Peoples R China
基金
中国国家自然科学基金;
关键词
BPNN; CEEMDAN; load forecasting; sample entropy; transformer; MODEL;
D O I
10.3390/en15103659
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Aiming at the problem that power load data are stochastic and that it is difficult to obtain accurate forecasting results by a single algorithm, in this paper, a combined forecasting method for short-term power load was proposed based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)-sample entropy (SE), the BP neural network (BPNN), and the Transformer model. Firstly, the power load data were decomposed into several power load subsequences with obvious complexity differences by using the CEEMDAN-SE. Then, BPNN and Transformer model were used to forecast the subsequences with low complexity and the subsequences with high complexity, respectively. Finally, the forecasting results of each subsequence were superimposed to obtain the final forecasting result. The simulation was taken from our proposed model and six forecasting models by using the load dataset from a certain area of Spain. The results showed that the MAPE of our proposed CEEMDAN-SE-BPNN-Transformer model was 1.1317%, while the RMSE was 304.40, which was better than the selected six forecasting models.
引用
收藏
页数:14
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