Deregulated electricity market data representation by fuzzy regression models

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.