A Method for Power System Short-Term Load Forecasting Based on Radial Basis Function Neural Network

In the daily operation of the power system, short-term load forecasting is of great significance, and it has always been an important research subject. Based on the characteristics of the power system load and radial basis function (RBF) neural network nonlinear identification function, this paper u...

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Bibliographic Details
Published in2013 Fourth International Conference on Intelligent Systems Design and Engineering Applications pp. 12 - 14
Main Authors Zeng Linsuo, Li Yanling
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2013
Subjects
BIM
Biological neural networks
Computer Aided Design
Computer Aided Drafting
Forecasting
Landscape Architecture
Load forecasting
Load modeling
Parametric Design
Predictive models
Wisdom Garden
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Summary:In the daily operation of the power system, short-term load forecasting is of great significance, and it has always been an important research subject. Based on the characteristics of the power system load and radial basis function (RBF) neural network nonlinear identification function, this paper uses RBF neural network on power system short-term load forecasting, and using Matlab toolbox to build load forecasting model to predict a maximum daily load in a place. The results of error meet the actual requirements, and it shows that the RBF neural network owns the effectiveness and feasibility in the field of power system short-term load forecasting.
DOI:10.1109/ISDEA.2013.409