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A new recursive neural network algorithm to forecast electricity price for PJM day-ahead market

Citation

Mandal, P and Srivastava, AK and Senju, T and Negnevitsky, M, A new recursive neural network algorithm to forecast electricity price for PJM day-ahead market, International Journal of Energy Research, 34, (6) pp. 507-522. ISSN 0363-907X (2010) [Refereed Article]

Copyright Statement

Copyright 2009 John Wiley & Sons, Ltd.

DOI: doi:10.1002/er.1569

Abstract

This paper evaluates the usefulness of publicly available electricity market information in predicting the hourly prices in the PJM day-ahead electricity market using recursive neural network (RNN) technique, which is based on similar days (SD) approach. RNN is a multi-step approach based on one output node, which uses the previous prediction as input for the subsequent forecasts. Comparison of forecasting performance of the proposed RNN model is done with respect to SD method and other literatures. To evaluate the accuracy of the proposed RNN approach in forecasting short-term electricity prices, different criteria are used. Mean absolute percentage error, mean absolute error and forecast mean square error (FMSE) of reasonably small values were obtained for the PJM data, which has correlation coefficient of determination (R2) of 0.7758 between load and electricity price. Error variance, one of the important performance criteria, is also calculated in order to measure robustness of the proposed RNN model. The numerical results obtained through the simulation to forecast next 24 and 72 h electricity prices show that the forecasts generated by the proposed RNN model are significantly accurate and efficient, which confirm that the proposed algorithm performs well for shortterm price forecasting. Copyright © 2009 John Wiley & Sons, Ltd.

Item Details

Item Type:Refereed Article
Research Division:Engineering
Research Group:Other Engineering
Research Field:Engineering not elsewhere classified
Objective Division:Energy
Objective Group:Other Energy
Objective Field:Energy not elsewhere classified
Author:Mandal, P (Dr Paras Mandal)
Author:Negnevitsky, M (Professor Michael Negnevitsky)
ID Code:60955
Year Published:2010 (online first 2009)
Web of Science® Times Cited:6
Deposited By:Engineering
Deposited On:2010-02-23
Last Modified:2017-04-11
Downloads:0

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