An Improved Combined Forecasting Method for Electric Power Load Based on Autoregressive Integrated Moving Average Model

Author

Jin, Xin ; Dong, Yao ; Wu, Jie ; Wang, Jujie

Author_Institution

Dept. of Modern Phys., Univ. of Sci. & Technol. of China, Hefei, China

Volume

2

fYear

2010

fDate

7-8 Aug. 2010

Firstpage

476

Lastpage

480

Abstract

Daily power load forecasting is an essential function in electrical power system operation and planning. The accuracy peak power load forecasting can ensure secure operation of the electric utility grid and have the least cost. Therefore, a good deal of forecasting methods have been proposed and studied in this domain. In this paper, Autoregressive Integrated Moving Average (ARIMA) model is developed to forecast short-term power load of New South Wales in Australia, then rectify residual errors using method of weighted mean. This combined method makes accuracy higher than the single ARIMA model.

Keywords

autoregressive moving average processes; load forecasting; autoregressive integrated moving average model; combined forecasting method; electric power load forecasting; electric utility grid; electrical power system operation; electrical power system planning; peak power load forecasting; weighted mean method; Data models; Forecasting; Load forecasting; Load modeling; Predictive models; Time series analysis; Autoregressive Integrated Moving Average (ARIMA); electric power load forecasting; method of weighted mean; residual errors;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Science and Management Engineering (ISME), 2010 International Conference of

Conference_Location

Xi´an

Print_ISBN

978-1-4244-7669-5

Electronic_ISBN

978-1-4244-7670-1

Type

conf

DOI

10.1109/ISME.2010.124

Filename

5573785