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

被引：19

作者：

Niimura, T ^{[1
]}

Ko, HS ^{[1
]}

Ozawa, K ^{[1
]}

机构：

[1] Univ British Columbia, Dept Elect & Comp Engn, Vancouver, BC V6T 1Z4, Canada

来源：

PROCEEDING OF THE 2002 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-3 | 2002年

关键词：

electricity market; price; time series; autoregression; neural network; fuzzy numbers;

D O I：

10.1109/IJCNN.2002.1007714

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a fuzzy regression model to estimate uncertain electricity market prices in 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 fuzzy model to consider the possible ranges of market prices. The neural network finds the crisp value for AR model and then the low and high ranges of 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.

引用

页码：1362 / 1366

页数：3

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