Short-Term Power Load Forecasting Based on Feature Filtering and Error Compensation under Imbalanced Samples

Zheng Wan and Hui Li ()
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Zheng Wan: College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China
Hui Li: College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China

Energies, 2023, vol. 16, issue 10, 1-22

Abstract: There are many influencing factors present in different situations of power load. There is also a strong imbalance in the number of load samples. In addition to examining the problem of low training efficiency of existing algorithms, this paper proposes a short-term power load prediction method based on feature selection and error compensation under imbalanced samples. After clustering the load data, we expand some sample data to balance the sample categories and input the load data and filtered feature sequences into the improved GRU for prediction. At the same time, the errors generated during the training process are used as training data. An error correction model is constructed and trained, and the results are used for error compensation to further improve prediction accuracy. The experimental results show that the overall prediction accuracy of the model has increased by 80.24%. After expanding a few samples, the prediction accuracy of the region where the samples are located increased by 59.41%. Meanwhile, due to the improvement of the algorithms, the running time was reduced by approximately 14.92%.

Keywords: feature filtering; error compensation; imbalanced sample; improved GRU; short-term power load forecasting (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: 2023
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