Journal of Electrical Engineering ›› 2022, Vol. 17 ›› Issue (1): 164-170.doi: 10.11985/2022.01.021

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Short-term Load Forecasting Model Based on Improved Bagging Algorithm and Fuzzy MP-LSTM Fusion

CAI Xinxiang1(), HAN Aoyang2, ZHOU Shengqi2, JIAN Xuehui2, ZHANG Zhisheng1()   

  1. 1. College of Electrical Engineering, Qingdao University, Qingdao 266071
    2. Qingdao Power Supply Company, State Grid Shandong Electric Power Company, Qingdao 266002
  • Received:2021-05-17 Revised:2021-10-12 Online:2022-03-25 Published:2022-05-06
  • Contact: ZHANG Zhisheng E-mail:c17806254216@163.com;slnzzs@126.com

Abstract:

In order to improve the accuracy of load forecasting, a short-term load forecasting model based on the fusion of improved Bagging algorithm and fuzzy minimum peephole long short-term memory (MP-LSTM) is proposed. Compared with the traditional long short-term memory (LSTM) model, the MP-LSTM model discards input gates and output gates, and only retains forgetting gates. The model includes a sigmoid network layer and a tanh network layer, which reduces model parameters and optimizes the model structure. By fuzzing the temperature, the influence of temperature fluctuations on the load is reduced the improved Bagging algorithm is used to integrate the MP-LSTM model to improve the accuracy of model prediction. The actual load data of a certain area is used for simulation, and compared with the traditional LSTM neural network prediction method, MP-LSTM neural network method and fuzzy MP-LSTM neural network method, and simulation results show that the proposed model has better predictions accuracy.

Key words: Short-term load forecasting, MP-LSTM, PSO, power systems, Bagging algorithm

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