Monthly Load Forecasting Based on Optimum Grey Model

Abstract:

Due to the variety and the randomicity of its influencing factors, the monthly load forecasting is a difficult problem for a long time. In order to improve the forecast accuracy, the paper proposes a new load forecast model based on improved GM (1, 1).First, the GM (1, 1) is used to forecast the load data, which takes the longitude historical data as original series, the increment trend of load was forecasted and takes the crosswise historical data as original series, the fluctuation trend of load was forecasted. On this basis the optimum method is led in. An optimal integrated forecasting model is built up. The case calculation results show that the proposed method can remarkably improve the accuracy of monthly load forecasting, and decrease the error. The integrated model this paper describes for short-term load forecasting is available and accurate.

• Periodical: Advanced Materials Research (Volumes 230-232)
• Pages: 1226-1230
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.230-232.1226
• Online since: May 2011
• Authors: Ting Wang, Xi Miao Jia
• Keywords: GM(1,1), Monthly Load Forecast, Optimum Method, Trend and Fluctuations

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