NDLI: Genetic Programming with Rough Sets Theory for Modeling Short-term Load Forecasting

Content Provider	IEEE Xplore Digital Library
Author	Wen-chuan Wang Chun-tian Cheng Lin Qiu
Copyright Year	2008
Description	Author affiliation: Inst. of Environ. & Municipal Eng., North China Inst. of Water Conservancy & Hydroelectric Power, Zhengzhou (Lin Qiu) \|\| Inst. of hydropower Syst. & Hydroinf., Dalian Univ. of Technol., Dalian (Wen-chuan Wang; Chun-tian Cheng)
Abstract	The accurate and robust short-term load forecasting (STLF) plays a significant role in electric power operation. The accuracy of STLF is greatly related to the selected the main relevant influential factors. However, how to select appropriate influential factor is a difficult task because of the randomness and uncertainties of the load demand and its influential factors. In this paper, a novel method of genetic programming (GP) with rough sets (RS) theory is developed to model STLF to improve the accuracy and enhance the robustness of load forecasting results. RS theory is employed to process large data and eliminate redundant information in order to find relevant factors to the short-term load, which are used as sample sets to establish forecasting model by means of GP evolutional algorithm. The presented model is applied to forecast short-term load using the actual data from GuiZhou power grid in China. The forecasted results are compared with BP artificial neural Network with RS theory, and it is shown that the presented forecasting method is more accurate and efficient.
Starting Page	306
Ending Page	310
File Size	401576
Page Count	5
File Format	PDF
ISBN	9780769533049
DOI	10.1109/ICNC.2008.141
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-10-18
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Uncertainty genetic programming Weather forecasting Artificial neural networks Predictive models Power grids Load forecasting Genetic programming Rough sets Robustness rough set short-term load forecasting Load modeling
Content Type	Text
Resource Type	Article

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