Fuzzy regression models to represent electricity market data in deregulated power industry

TitleFuzzy regression models to represent electricity market data in deregulated power industry
Publication TypeConference Paper
Year of Publication2001
AuthorsNiimura, T., M. Dhaliwal, and K. Ozawa
Conference NameIFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th
Pagination2556 -2561 vol.5
Date Publishedjul.
KeywordsCalifornia Power Exchange data, data analysis, data region, demand volume, deregulated power industry, electrical power market, electricity demand, electricity market data, electricity price, electricity supply industry, flexible model, fuzzy logic, fuzzy regression models, fuzzy set theory, possibility regression, possibility theory, power exchange market, power industry deregulation, power market data, power system economics, price data, regression analysis, regression model, statistical analysis, TSK-fuzzy model

The authors present a flexible model that represents the relation of electricity price and demand in an electrical power market. Power market data are first analyzed by regression analysis. The price data show an upward trend as the demand volume increases. We have divided the regression model into two regions: low demand and high demand. Two curves are smoothly connected by a TSK-fuzzy model, noting the fact that the "low" demand and "high" demand regions are not distinct but overlapping. The fuzzy model is further extended to encompass the data region indicating the degree of possibility. California Power Exchange data are analyzed as an example


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