Title :
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
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;
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
DOI :
10.1109/ISME.2010.124
