Author: Tsang, Tak-yar Teddy
Title: Electrical load forecasting using fuzzy ARMAX model
Degree: M.Sc.
Year: 2000
Subject: Electric power-plants -- Load -- Forecasting
Electric power-plants -- Load -- Data processing
Fuzzy systems
Hong Kong Polytechnic University -- Dissertations
Department: Multi-disciplinary Studies
Department of Electrical Engineering
Pages: viii, 73 leaves : ill. ; 30 cm
Language: English
Abstract: The dissertation studies a new electrical load forecasting technique, fuzzy autoregressive moving average with exogenous input variables (FARMAX). The model has been tested for one day ahead hourly load forecasting of ring circuits in distribution level. The exogenous input variables can be anything that has an effect on electricity consumption. The model is composed of a few elements: the identification of the system by conventional ARMAX method, fuzzy logic and evolutionary programming. In order to achieve higher accuracy of forecasting weather information, data such as air temperature and rainfall will be taken into consideration. The input data will be passed through a fuzzy logic system to make the model more "human like". The fuzzification system is formulated as a combinatorial optimization problem. Evolutionary programming technique (branch of genetic algorithm) is used to obtain optimal fuzzy parameters for the input variables. Heuristic search method forms the other part of the fuzzification system. The method determines the best number of divisions or fuzzy sub-spaces of input variables. The final result will be obtained through defuzzification. The FARMAX model is tested by using real data in Hong Kong. The focus of the dissertation is 24-hour ahead short term loading forecasting of distribution feeder circuits in Yuen Long. The simulation is done by Matlab package and the results are verified.
Rights: All rights reserved
Access: restricted access

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