Monthly Electricity Forecast Based on X-12-ARIMA Seasonal Decomposition and Annual Electricity Correction

doi:10.3969/j.issn.1000－7229.2017.01.010

Abstract

Abstract: Monthly electricity forecast is the basis for the planning department of power grid corporation to arrange the operation and purchase plan. This paper presents a method for forecasting monthly electricity consumption considering various economic factors. First, we adopt the X-12-ARIMA model to decompose the economic data and monthly electricity data into seasonal part, and adopt stepwise regression analysis to study the correlation and regression model between the economic factors and electricity consumption and get the primary forecast results. Then, we use polynomial fitting to forecast the annual electricity, and use the result of annual electricity to adjust the monthly electricity consumption. Finally, we adopt autoregressive integrated moving average (ARIMA) model to correct the seasonal forecast for the months which are obviously influenced by meteorological factors and holidays, and obtain the monthly electricity forecasting model with good precision. In this paper, monthly electricity consumption from May 2014 to April 2015 in Guangdong Province are forecasted by economic data and monthly electricity consumption from March 2009 to April 2014 to obtain the average prediction accuracy of 97.78%, which verifies the effectiveness of the prediction model.

Key words: X-12-ARIMA, monthly electricity consumption, forecasting, correction, autoregressive integrated moving average (ARIMA)

CLC Number:

TM 714

ZHANG Qiang, WANG Yi, LI Dingrui, ZHU Wenjun. Monthly Electricity Forecast Based on X-12-ARIMA Seasonal Decomposition and Annual Electricity Correction[J]. Electric Power Construction, 2017, 38(1): 76-.

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