Title :
A day-ahead electricity price prediction based on a fuzzy-neuro autoregressive model in a deregulated electricity market
Author :
Niimura, T. ; Ko, Hee-Sang ; Ozawa, K.
Author_Institution :
Dept. of Electr. & Comput. Eng., British Columbia Univ., Vancouver, BC, Canada
fDate :
6/24/1905 12:00:00 AM
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
Keywords :
autoregressive processes; feedforward neural nets; forecasting theory; fuzzy set theory; multilayer perceptrons; parameter estimation; power system economics; time series; California Power Exchange market data; crisp value; day-ahead electricity price prediction; deregulated electricity market; fuzzy model; fuzzy-neuro autoregressive model; linear programming; neural network; time series model; uncertain electricity market prices; Costs; Electricity supply industry; Electricity supply industry deregulation; Fuzzy neural networks; Linear programming; Multi-layer neural network; Neural networks; Power generation; Power markets; Predictive models;
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7278-6
DOI :
10.1109/IJCNN.2002.1007714
