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

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3 Author(s)
T. Niimura ; Dept. of Electr. & Comput. Eng., British Columbia Univ., Vancouver, BC, Canada ; M. Dhaliwal ; K. Ozawa

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

Published in:

IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th

Date of Conference:

25-28 July 2001