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

TitleA day-ahead electricity price prediction based on a fuzzy-neuro autoregressive model in a deregulated electricity market
Publication TypeConference Paper
Year of Publication2002
AuthorsNiimura, T., H. - S. Ko, and K. Ozawa
Conference NameNeural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Pagination1362 -1366
Keywordsautoregressive processes, California Power Exchange market data, crisp value, day-ahead electricity price prediction, deregulated electricity market, feedforward neural nets, forecasting theory, fuzzy model, fuzzy set theory, fuzzy-neuro autoregressive model, linear programming, multilayer perceptrons, neural network, parameter estimation, power system economics, time series, time series model, uncertain electricity market prices
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

URLhttp://dx.doi.org/10.1109/IJCNN.2002.1007714
DOI10.1109/IJCNN.2002.1007714

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