Ultra-Short-Term Load Dynamic Forecasting Method Considering Abnormal Data Reconstruction Based on Model Incremental Training

Guangyu Chen (), Yijie Wu, Li Yang, Ke Xu, Gang Lin, Yangfei Zhang and Yuzhuo Zhang
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Guangyu Chen: School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China
Yijie Wu: School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China
Li Yang: State Grid Fujian Electric Power Company Limited, Fuzhou 350001, China
Ke Xu: School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China
Gang Lin: State Grid Fujian Electric Power Company Quanzhou Power Supply Company, Quanzhou 362000, China
Yangfei Zhang: School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China
Yuzhuo Zhang: School of Electric Power Engineering, Nanjing Institute of Technology, Nanjing 211167, China

Energies, 2022, vol. 15, issue 19, 1-21

Abstract: In order to reduce the influence of abnormal data on load forecasting effects and further improve the training efficiency of forecasting models when adding new samples to historical data set, an ultra-short-term load dynamic forecasting method considering abnormal data reconstruction based on model incremental training is proposed in this paper. Firstly, aiming at the abnormal data in ultra-short-term load forecasting, a load abnormal data processing method based on isolation forests and conditional adversarial generative network (IF-CGAN) is proposed. The isolation forest algorithm is used to accurately eliminate the abnormal data points, and a conditional generative adversarial network (CGAN) is constructed to interpolate the abnormal points. The load-influencing factors are taken as the condition constraints of the CGAN, and the weighted loss function is introduced to improve the reconstruction accuracy of abnormal data. Secondly, aiming at the problem of low model training efficiency caused by the new samples in the historical data set, a model incremental training method based on a bidirectional long short-term memory network (Bi-LSTM) is proposed. The historical data are used to train the Bi-LSTM, and the transfer learning is introduced to process the incremental data set to realize the adaptive and rapid adjustment of the model weight and improve the model training efficiency. Finally, the real power grid load data of a region in eastern China are used for simulation analysis. The calculation results show that the proposed method can reconstruct the abnormal data more accurately and improve the accuracy and efficiency of ultra-short-term load forecasting.

Keywords: ultra-short-term load forecasting; abnormal data reconstruction; isolation forests; conditional generation adversarial network; bi-directional long short-term memory network; transfer learning (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
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