Paper Title:
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.

  Info
Periodical
Advanced Materials Research (Volumes 230-232)
Edited by
Ran Chen and Wenli Yao
Pages
1226-1230
DOI
10.4028/www.scientific.net/AMR.230-232.1226
Citation
T. Wang, X. M. Jia, "Monthly Load Forecasting Based on Optimum Grey Model", Advanced Materials Research, Vols. 230-232, pp. 1226-1230, 2011
Online since
May 2011
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Price
$32.00
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