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

Shichao Huang, Jing Zhang, Yu He, Xiaofan Fu, Luqin Fan, Gang Yao and Yongjun Wen
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Shichao Huang: College of Electrical Engineering, Guizhou University, Guiyang 550025, China
Jing Zhang: College of Electrical Engineering, Guizhou University, Guiyang 550025, China
Yu He: College of Electrical Engineering, Guizhou University, Guiyang 550025, China
Xiaofan Fu: College of Electrical Engineering, Guizhou University, Guiyang 550025, China
Luqin Fan: College of Electrical Engineering, Guizhou University, Guiyang 550025, China
Gang Yao: Guizhou Power Grid Company, Guiyang 550001, China
Yongjun Wen: Pujiang Guangyuan Power Construction Co., Ltd., Pujiang, Jinhua 322200, China

Energies, 2022, vol. 15, issue 10, 1-14

Abstract: 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.

Keywords: BPNN; CEEMDAN; load forecasting; sample entropy; transformer (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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