A day-ahead electricity price prediction based on a fuzzy-neuro autoregressive model in a deregulated electricity market

Abstract

Presents a fuzzy regression model to estimate uncertain electricity market prices in a deregulated industry environment. The price of electricity in a deregulated market is very volatile in time. Therefore, it is difficult to estimate an accurate market price using historically observed data. In the proposed method, uncertain market prices are estimated by an autoregressive model using a neural network, and the time series model is extended to a fuzzy model to consider the possible ranges of market prices. The neural network finds the crisp value for the AR model and then the low and high ranges of the fuzzy model are found by linear programming. Therefore, the proposed model can represent the possible ranges of a day-ahead market price. For a numerical example, the model is applied to California Power Exchange market data.