A Combination Method for Mid-Long Term Power Load Simulation and Prediction

Article Preview

Abstract:

In view of the singleness of effect evaluation indexes in combination forecasting, this paper considered both benefit factors and cost factors. The entropy method and the variance-covariance method are adopted to determine the relative importance of these indicators and to get the weight of each single model. This improved combination forecasting model took both prediction accuracy and curve similarity into consideration, which is more accurate and reliable.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

498-501

Citation:

Online since:

August 2013

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] Niu Dongxiao, Cao Shuhua, Lu Jianchang et al. Load forecasting technology and its application[M]. Beijing: China Electric Power Press, 2009: 2-3.

Google Scholar

[2] Li Jianwei, Zhao Faqi, Liu Fenglin. Forecast Combining Approach of Mid-long Term Power Load[J]. Proceeding of the CSU-EPSA. 2011(8): 133-136.

Google Scholar

[3] Li Jinchao, Niu Dongxiao, Li Jinying et al. Combination forecasting of medium-long term power load based on entropy weight[J]. East China Electric Power, 2005(8): 26-29.

DOI: 10.1109/chicc.2006.4347372

Google Scholar

[4] Yuan Hongjun, Yang Guiyuan. The combination forecast model based on the biggest-smalledt approach degree[J]. Operation research and management science. 2010, 4: 116-119.

Google Scholar

[5] Jing Yaping, Zhang Xin, Luo Yan. Forecasting of urban water demand based on combining Grey and BP neural network with Markov chain model[J]. Journal of Northwest A&F University(Nat. Sci. ED. ). 2011(7): 230-233.

Google Scholar