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

摘要：

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.

关键词： electric power load forecasting residual errors method of weighted mean Autoregressive Integrated Moving Average (ARIMA)

作者: Xin Jin Jie Wu Yao Dong Jujie Wang

作者单位: Department of Modem Physics University of Science and Technology of China Hefei, China School of Mathematics & Statistics Lanzhou University Lanzhou, China

会议类型: 国际会议

会议名称: 2010 International Conference of Informationa Science and Management Engineering(2010年信息科学与管理工程国际学术会议 ISME 2010)

会议地点: 西安

会议语种:英文

页码: 1056-1060

在线出版日期: 2010-08-07（万方平台首次上网日期，不代表论文的发表时间）