Deregulated electricity market data representation by fuzzy regression models

TitleDeregulated electricity market data representation by fuzzy regression models
Publication TypeJournal Article
Year of Publication2001
AuthorsNiimura, T., and T. Nakashima
JournalSystems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on
Pagination320 -326
Date Publishedaug.
KeywordsCalifornia Power Exchange market data, data cluster, deregulated electricity market data representation, electricity demand, electricity price, electricity supply industry, fuzzy numbers, fuzzy regression models, fuzzy set theory, fuzzy set-based model, high demand, low demand, possibility distribution, price data nonlinear trend, regression curves, statistical analysis, Takagi-Sugeno-Kang fuzzy model, volatile data region

In this paper, the authors present a fuzzy set-based model that represents the relation of electricity demand and price in a recently deregulated electricity market. A simple regression analysis shows the price data's nonlinear trend as the demand volume increases. We have divided the data cluster into two overlapping regions: low demand and high demand. Regression curves, obtained for the two clusters, are smoothly connected by a Takagi-Sugeno-Kang (TSK)-fuzzy model. The fuzzy model is further expanded to encompass the volatile data region by introducing fuzzy numbers in regression parameters. The developed model can indicate the possibility distribution of electricity prices for a given demand value. The model also has the flexibility of narrowing its focus by modifying the fuzzy numbers. California Power Exchange market data are analyzed as a numerical example


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