Research Article | OPEN ACCESS
Power System Short-Term Load Forecasting Based on Fuzzy Neural Network
1Chao Ge, 2Lei Wang and 3Hong Wang
1College of Information Engineering, Hebei United University
2Department of Information Engineering, Tangshan College
3College of Qinggong, Hebei United University, Tangshan, China 063009
Research Journal of Applied Sciences, Engineering and Technology 2013 16:2972-2975
Received: January 08, 2013 | Accepted: February 18, 2013 | Published: September 10, 2013
Abstract
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.
Keywords:
Fuzzy neural network, dynamic recurrent, load forecasting,
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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