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

Author

Niimura, T. ; Dhaliwal, Maninder ; Ozawa, Kazuhiro

Author_Institution

Dept. of Electr. & Comput. Eng., British Columbia Univ., Vancouver, BC, Canada

fYear

2001

fDate

25-28 July 2001

Firstpage

2556

Abstract

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

Keywords

data analysis; electricity supply industry; fuzzy logic; fuzzy set theory; possibility theory; power system economics; statistical analysis; California Power Exchange data; TSK-fuzzy model; data region; demand volume; deregulated power industry; electrical power market; electricity demand; electricity market data; electricity price; flexible model; fuzzy regression models; possibility regression; power exchange market; power industry deregulation; power market data; price data; regression analysis; regression model; Data analysis; Economic forecasting; Electricity supply industry; Electricity supply industry deregulation; Polynomials; Power industry; Power markets; Predictive models; Regression analysis; Supply and demand;

fLanguage

English

Publisher

ieee

Conference_Titel

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

Conference_Location

Vancouver, BC

Print_ISBN

0-7803-7078-3

Type

conf

DOI

10.1109/NAFIPS.2001.943625

Filename

943625