An enhanced radial basis function network for short-term electricity price forecasting
This paper proposed a price forecasting system for electric market participants to reduce the risk of price volatility. Combining the Radial Basis Function Network (RBFN) and Orthogonal Experimental Design (OED), an Enhanced Radial Basis Function Network (ERBFN) has been proposed for the solving process. The Locational Marginal Price (LMP), system load, transmission flow and temperature of the PJM system were collected and the data clusters were embedded in the Excel Database according to the year, season, workday and weekend. With the OED applied to learning rates in the ERBFN, the forecasting error can be reduced during the training process to improve both accuracy and reliability. This would mean that even the "spikes" could be tracked closely. The Back-propagation Neural Network (BPN), Probability Neural Network (PNN), other algorithms, and the proposed ERBFN were all developed and compared to check the performance. Simulation results demonstrated the effectiveness of the proposed ERBFN to provide quality information in a price volatile environment.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 87 (2010)
Issue (Month): 10 (October)
|Contact details of provider:|| Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description|
|Order Information:|| Postal: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic|
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Abdou Kâ Diongue & Dominique Guegan & Bertrand Vignal, 2007.
"Forecasting electricity spot market prices with a k-factor GIGARCH process,"
Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers)
- Diongue, Abdou Kâ & Guégan, Dominique & Vignal, Bertrand, 2009. "Forecasting electricity spot market prices with a k-factor GIGARCH process," Applied Energy, Elsevier, vol. 86(4), pages 505-510, April.
- Abdou Kâ Diongue & Dominique Guégan & Bertrand Vignal, 2007. "Forecasting electricity spot market prices with a k-factor GIGARCH process," Documents de travail du Centre d'Economie de la Sorbonne b07058, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne, revised Nov 2009.
- Abdou Kâ Diongue & Dominique Guegan & Bertrand Vignal, 2009. "Forecasting electricity spot market prices with a k-factor GIGARCH process," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-00307606, HAL.
- Kosater, Peter & Mosler, Karl, 2006.
"Can Markov regime-switching models improve power-price forecasts? Evidence from German daily power prices,"
Elsevier, vol. 83(9), pages 943-958, September.
- Kosater, Peter & Mosler, Karl, 2005. "Can Markov-regime switching models improve power price forecasts? Evidence for German daily power prices," Discussion Papers in Econometrics and Statistics 1/05, University of Cologne, Institute of Econometrics and Statistics.
- Inglesi, Roula, 2010. "Aggregate electricity demand in South Africa: Conditional forecasts to 2030," Applied Energy, Elsevier, vol. 87(1), pages 197-204, January.
- Erdogdu, Erkan, 2010.
"A paper on the unsettled question of Turkish electricity market: Balancing and settlement system (Part I),"
Elsevier, vol. 87(1), pages 251-258, January.
- Erdogdu, Erkan, 2010. "A paper on the unsettled question of Turkish electricity market: Balancing and settlement system (Part I)," MPRA Paper 19090, University Library of Munich, Germany.
- Crespo Cuaresma, Jesús & Hlouskova, Jaroslava & Kossmeier, Stephan & Obersteiner, Michael, 2004. "Forecasting electricity spot-prices using linear univariate time-series models," Applied Energy, Elsevier, vol. 77(1), pages 87-106, January.
- repec:hal:journl:halshs-00307606 is not listed on IDEAS
- AlRashidi, M.R. & EL-Naggar, K.M., 2010. "Long term electric load forecasting based on particle swarm optimization," Applied Energy, Elsevier, vol. 87(1), pages 320-326, January.
When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:87:y:2010:i:10:p:3226-3234. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Zhang, Lei)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.