NDLI: Application of Neural Network Model Based on Combination of Fuzzy Classification and Input Selection in Short Term Load Forecasting

Content Provider	IEEE Xplore Digital Library
Author	Yu-Jun He You-Chan Zhu Dong-Xing Duan Wei Sun
Copyright Year	2006
Description	Author affiliation: Dept. of Electron. & Commun. Eng., North China Electr. Power Univ., Baoding (Yu-Jun He)
Abstract	In power system, short term load forecasting (STLF) is important for optimum operation planning of power generation facilities, as it affects both system reliability and fuel consumption. Computational intelligent technique for STLF has become more and more important in electric engineering since it is a useful tool for efficient planning. So the study of STLF system requires an efficient computational tool such as computational intelligence technique. In this paper, we applied the use of computational intelligent methods to short term load forecasting systems. With power systems growth and the increase in their complexity, many factors have become influential to the electric power generation and consumption. First, we use entropy theory to select relevant ones from all load influential factors. Next, considering the features of power load and reduced influential factors, we use fuzzy classification rules to divide the past load data into different network property. Then the representative historical load data samples were selected as the training set for neural network, which have the same weather characteristic as the certain forecasting day. Finally, Elman recurrent neural network (ERNN) forecasting model is constructed which is a kind of globally feed forward locally recurrent network model with distinguished dynamical characteristics. And the effectiveness of the model has been tested using practical daily load data. The simulation results show that the presented intelligent technique for load forecasting can give satisfactory results
Sponsorship	IEEE Syst., Man and Cybernetics Hebei Univ.
Starting Page	3152
Ending Page	3156
File Size	245103
Page Count	5
File Format	PDF
ISBN	1424400619
DOI	10.1109/ICMLC.2006.258409
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2006-08-13
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neural networks Predictive models Fuzzy neural networks Load forecasting Power system planning Power engineering computing Computational intelligence Competitive intelligence Weather forecasting Power system modeling power system Intelligent technique fuzzy classification entropy short term load forecasting
Content Type	Text
Resource Type	Article

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