DocumentCode :
2515796
Title :
Day-ahead electricity price forecasting using artificial intelligence
Author :
Zhang, Jun ; Cheng, Chuntian
Author_Institution :
Dept. of Civil & Hydraulic Eng., Dalian Univ. of Technol., Dalian
fYear :
2008
fDate :
6-7 Oct. 2008
Firstpage :
1
Lastpage :
5
Abstract :
Accurate day-ahead electricity price forecasting (DEPF) has significant meanings in deregulated electrical power market due to its profitable function for all the participants to make reasonable decisions during the market business activities. However, the DEPF with satisfactory precision is difficult to be gained because of the violent volatility of electricity price caused by many factors. In this study, a multilayer perceptron artificial neural networks model is constructed for the DEPF in spot market of Nord Pool which is one of the most successful electrical power markets in the world. The major influencing factors are chosen by statistical methods called auto correlation function (ACF) and cross correlation function (CCF), and the standard error back-propagation algorithm is improved by using self-adaptive learning rate and self-adaptive momentum coefficient algorithm to make the training process more efficient both in global optimization and time saving. The most suitable structure of the network is determined by a trial-and-error experiment minimizing MAPE and MSRE of the network and several commonly used error indicators are employed to evaluate the goodness of fit performance of the model. The case study indicates that the DEPF of the proposed model is more reasonable and accurate, comparing with that of traditional ARIMA model.
Keywords :
backpropagation; correlation methods; multilayer perceptrons; power engineering computing; power markets; pricing; statistical analysis; artificial intelligence; autocorrelation function; cross correlation function; day-ahead electricity price forecasting; deregulated electrical power market; multilayer perceptron artificial neural network; self-adaptive learning rate; standard error back-propagation algorithm; statistical method; Artificial intelligence; Artificial neural networks; Autocorrelation; Economic forecasting; Electricity supply industry deregulation; Load forecasting; Multilayer perceptrons; Optimization methods; Power markets; Statistical analysis; ARIMA; Nord Pool; artificial neural networks; electricity price forecasting; self-adaptive algorithm;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electric Power Conference, 2008. EPEC 2008. IEEE Canada
Conference_Location :
Vancouver, BC
Print_ISBN :
978-1-4244-2894-6
Electronic_ISBN :
978-1-4244-2895-3
Type :
conf
DOI :
10.1109/EPC.2008.4763317
Filename :
4763317
Link To Document :
بازگشت