• CSCD核心库收录期刊
  • 中文核心期刊
  • 中国科技核心期刊

ELECTRIC POWER CONSTRUCTION ›› 2022, Vol. 43 ›› Issue (9): 117-124.doi: 10.12204/j.issn.1000-7229.2022.09.012

• Research and Application of Key Technologies for Distribution Network Planning and Operation Optimization under New Energy Power Systems ·Hosted by Professor WANG Shouxiang and Dr. ZHAO Qianyu· • Previous Articles     Next Articles

Residential Daily Power Load Forecasting Based on Threshold ARMA Model Considering the Influence of Temperature

SUN Yuqin, WANG Yawen, ZHU Wei(), LI Yan   

  1. College of Mathematics and Physics, Shanghai University of Electric Power, Shanghai 200090, China
  • Received:2021-11-25 Online:2022-09-01 Published:2022-08-31
  • Contact: ZHU Wei E-mail:zhuwei_ok@126.com
  • Supported by:
    National Natural Science Foundation of China(11871377);National Natural Science Foundation of China(12071274)


Due to the influence of the abrupt change-point of temperature, the load sequence has a threshold effect, which leads to poor load forecasting effects of traditional linear time series models. This paper uses the abrupt change-point of the temperature as the threshold and establishes a threshold autoregressive moving average model with temperature as the exogenous variable (TARMAX). The forecasting accuracy is improved. In this paper, the Markov Chain Monte Carlo (MCMC) method is firstly applied to search for the abrupt change-point of the temperature, and the model parameters are obtained. Then, the method of random search variables is used to quickly select the optimal model, which effectively reduces the amount of calculation for selecting the time series model. Finally, the residential daily power load in different seasons is forecasted. The example shows that, compared with the linear time series models, the long short-term memory network (LSTM), and the multi-layer perceptron (MLP), the TARMAX model improves the forecasting accuracy of the power load.

Key words: residential daily power load forecasting, threshold autoregressive moving average (TARMA) model, abrupt change-point of temperature, threshold, exogenous variable

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