Comparison of electric load forecasting algorithms

Pao Yue-kong Library Electronic Theses Database

Comparison of electric load forecasting algorithms

 

Author: Wong, Chung-yu
Title: Comparison of electric load forecasting algorithms
Degree: M.Sc.
Year: 1999
Subject: Electric power-plants -- Load -- Forecasting
Electric power-plants -- Load -- Data processing
Electric power-plants -- Load -- Mathematical models
Electric power consumption -- Forecasting
Hong Kong Polytechnic University -- Dissertations
Department: Multi-disciplinary Studies
Dept. of Electrical Engineering
Pages: vii, 80 leaves : ill. (some col.) ; 30 cm
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b1485426
URI: http://theses.lib.polyu.edu.hk/handle/200/597
Abstract: An intelligent approaches to electric load forecasting which combines the techniques of Box-Jenkins (BJ) modeling with the evolutionary programming (EP) and neural network (NN) algorithms are presented. Box-Jenkins models have been shown to produce better forecasts than any other time series model especially when the data series contains both seasonal and nonseasonal patterns. However, many statisticians and analysts have been reluctant to use it because the modeling procedures are complicated which requires a great deal of experience and a thorough understanding on the theoretical features of the autocorrealation functions and the partial autocorrelation functions. In general, BJ modeling procedures consist of a four-stage cycle: model identification, parameter estimation, diagnostic checking, and validation forecasts. In this dissertation, the complex BJ model identification problem is addressed by using EP. EP is a new adaptive search technique which simulates the natural evolutionary process to reach the fittest individuals after repeated mutation, competition and selection procedures. It has the ability in determining the global minimum and thus leading to optimal model selection. By using this approach, it is attempted to simplify the BJ identification procedures, and at the same time, achieving the optimal load forecasting model. At the estimation stage, instead of using the traditional gradient search approach like nonlinear least square, the EP and NN algorithms are adopted separately for selecting the coefficients of the tentative model. NN is another well-known intelligent algorithm which has proved to be successful in pattern recognition. However, it has seldom been applied on parameter estimation, so the result of this dissertation can serve as a good reference for future research work. The performance of these approaches is investigated by using the monthly electricity consumption of Hong Kong at the validation forecasting stage. It is found that the forecast error are generally better than that of the traditional approach as obtained by using the SPSS commercial software package.

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