Next Day Price Forecasting in Deregulated Market by Combination of Artificial Neural Network and ARIMA Time Series Models

被引：0

作者：

Areekul, Phatchakorn ^{[1
]}

Senjyu, Tomonobu ^{[1
]}

Urasaki, Naomitsu ^{[1
]}

Yona, Atsushi ^{[1
]}

机构：

[1] Univ Ryukyus, Fac Engn, Dept Elect & Elect Engn, Okinawa 9030213, Japan

来源：

ICIEA 2010: PROCEEDINGS OF THE 5TH IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS, VOL 3 | 2010年

关键词：

Electricity price forecasting; neural network; ARIMA; combination methodology; back-propagation;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Electricity price forecasting is becoming increasingly relevant to power producers and consumers in the new competitive electric power markets, when planning bidding strategies in order to maximize their benefits and utilities, respectively. This paper proposed a method to predict hourly electricity prices for next-day electricity markets by combination methodology of ARIMA and ANN models. The proposed method is examined on the Australian National Electricity Market (NEM), New South Wales regional in year 2006. Comparison of forecasting performance with the proposed ARIMA, ANN and combination (ARIMA-ANN) models are presented. Empirical results indicate that an ARIMA-ANN model can improve the price forecasting accuracy.

引用

页码：299 / 304

页数：6

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