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dc.contributorMulti-disciplinary Studiesen_US
dc.contributorDepartment of Electrical Engineeringen_US
dc.creatorTsang, Tak-yar Teddy-
dc.identifier.urihttps://theses.lib.polyu.edu.hk/handle/200/4072-
dc.languageEnglishen_US
dc.publisherHong Kong Polytechnic University-
dc.rightsAll rights reserveden_US
dc.titleElectrical load forecasting using fuzzy ARMAX modelen_US
dcterms.abstractThe 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.en_US
dcterms.extentviii, 73 leaves : ill. ; 30 cmen_US
dcterms.isPartOfPolyU Electronic Thesesen_US
dcterms.issued2000en_US
dcterms.educationalLevelAll Masteren_US
dcterms.educationalLevelM.Sc.en_US
dcterms.LCSHElectric power-plants -- Load -- Forecastingen_US
dcterms.LCSHElectric power-plants -- Load -- Data processingen_US
dcterms.LCSHFuzzy systemsen_US
dcterms.LCSHHong Kong Polytechnic University -- Dissertationsen_US
dcterms.accessRightsrestricted accessen_US

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Please use this identifier to cite or link to this item: https://theses.lib.polyu.edu.hk/handle/200/4072