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Abstract
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Article Information:
Power System Short-Term Load Forecasting Based on Fuzzy Neural Network
Chao Ge, Lei Wang and Hong Wang
Corresponding Author: Chao Ge
Submitted: January 08, 2013
Accepted: February 18, 2013
Published: September 10, 2013 |
Abstract:
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Short-Term Load Forecasting (STLF) is an important operational function in both regulated power systems. This study is concerned with the problem of STLF. Considering the factors such as temperature, date type, weather status, etc, which influence the STLF, a model is set up by dynamic recurrent fuzzy neural network. The fuzzy inference function is realized easily by using a product operation in the network. Introducing local recurrent units to hidden layer, the proposed method can overcome the limit of the traditional BP algorithm. The actual simulation is given to demonstrate the effectiveness of the proposed methods.
Key words: Fuzzy neural network, dynamic recurrent, load forecasting, , , ,
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Abstract
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Cite this Reference:
Chao Ge, Lei Wang and Hong Wang, . Power System Short-Term Load Forecasting Based on Fuzzy Neural Network. Research Journal of Applied Sciences, Engineering and Technology, (16): 2972-2975.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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