Application of fuzzy neural network in monthly electric load forecasting

Pao Yue-kong Library Electronic Theses Database

Application of fuzzy neural network in monthly electric load forecasting

 

Author: Kwan, Yiu-leung
Title: Application of fuzzy neural network in monthly electric load forecasting
Degree: M.Sc.
Year: 1997
Subject: Electric power consumption -- Forecasting
Electric power consumption -- China -- Hong Kong -- Forecasting
Electric power-plants -- Load
Fuzzy systems
Neural networks (Computer science)
Hong Kong Polytechnic University -- Dissertations
Department: Multi-disciplinary Studies
Pages: viii, 188 leaves : ill. ; 30 cm
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b1250486
URI: http://theses.lib.polyu.edu.hk/handle/200/3104
Abstract: Load forecasting plays one of the most critical roles in modern power system control centers. The accuracy of forecasting influences the decision making in unit commitment, fuel allocation, maintenance scheduling and off-line network analysis. Accurate load forecasting is a potential source of great savings for electric utilities. Since the mid-sixties, much research has been devoted to the development of accurate and efficient load forecasting methods. Many approaches used in time series prediction have been applied to power system load forecasting, such as linear regression, exponential smoothing, Box-Jenkins model, stochastic process, and the state space methods. Some important issues, however, remain in the field of electric load forecasting due to these problems contains many complications and uncertainties. Power system load demand is influenced by many factors, such as weather, economic and social activities. Different electricity markets, such as domestic, industrial, commercial, street lighting and export to China, etc. may be affected by these factors to different extent. By analysis of only historical load data, it is difficult to obtain accurate load demand from forecasting. The relation between load demand and the independent variables is complex and it is not always possible to fit the load curve using statistical models. It is difficult to model the relationships between system load and factors that influence it. Also, another difficulty lies in estimating and adjusting the model parameters. These parameters are estimated from historical data and they may become obsolete or may not be able to reflect load pattern changes for recent periods. In addition, load forecasting models rely heavily on the particular utility environment in which the models are developed, therefore, they are not sufficiently general to be transferred easily from one company to another. Fuzzy neural network (FNN) is a potentially powerful technique for solving the above problems. The FNN is actually a neural network based fuzzy logic system. This system takes advantages of both neural network and fuzzy system, and overcome the difficulties of each system since neural networks favor numerical mathematical analysis, hardware implementation, distributed parallel processing and self-learning mechanism while fuzzy systems has been for dealing with difficulties arising from uncertainty, imprecision, and noise. The study presented in this dissertation is devoted to the development of system load forecasting methods based on fuzzy neural networks. The concepts and methodology formulated have been tested by using the Hong Kong's electricity consumption together with the corresponding weather data from the Census and Statistics Department of Hong Kong. The testing results shown that the proposed approaches have superior qualities in dealing with difficulties encountered in the traditional techniques, e.g. sudden change in the load pattern. In addition, the numerical simulation results shown that this new approach can provide forecasts with similar accuracy as the traditional statistical methods and artificial neural network based method. This dissertation also includes a comparison of the fuzzy neural network approach and other established techniques in this field. It is demonstrated that the former provides a parallel representation of the later.

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