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

doi:10.12204/j.issn.1000-7229.2022.09.012

Abstract

Abstract:

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

CLC Number:

TM715

SUN Yuqin, WANG Yawen, ZHU Wei, LI Yan. Residential Daily Power Load Forecasting Based on Threshold ARMA Model Considering the Influence of Temperature[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(9): 117-124.

Figures/Tables 15

Fig.1 Time series diagrams of residential power load and maximum temperature

Table 1 Correlation between temperature and residential power load

Fig.2 Scatter plot of residential daily power load-daily maximum temperature

Fig.3 Load sequence diagrams on both sides

Fig.4 Flowchart of the load forecasting

Fig.5 Sampling result of the abrupt change-point of the temperature

Fig.6 Selection results of load forecasting model

Fig.7 Forecast results of residential power load in Jun. 2019

Table 2 Forecasting accuracy of residential power load in Jun. 2019

Fig.8 Forecast results of residential power load in Sep. 2019

Fig.9 Forecast results of residential power load in Dec. 2019

Fig.10 Forecast results of residential power load in Mar. 2020

Table 3 Forecasting accuracy of residential power load in different months

Table 4 Forecasting accuracy with average temperature as the exogenous variable

Fig.11 Scatter plots of load-temperature in different regions

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