ELECTRICITY MARKET PRICE FORECASTING BY GRID COMPUTING OPTIMIZING ARTIFICIAL NEURAL NETWORKS

T. Niimura, K. Ozawa () and N. Sakamoto
Additional contact information
T. Niimura: Faculty of Economics, Hosei University, Tokyo
K. Ozawa: Faculty of Economics, Hosei University, Tokyo
N. Sakamoto: Faculty of Economics, Hosei University, Tokyo

Portuguese Journal of Management Studies, 2007, vol. XII, issue 2, 133-143

Abstract: This paper presents a grid computing approach to parallel-process a neural network time-series model for forecasting electricity market prices. A grid computing environment introduced in a university computing laboratory provides access to otherwise underused computing resources. The grid computing of the neural network model not only processes several times faster than a single iterative process, but also provides chances of improving forecasting accuracy. Results of numerical tests using real market data on twenty grid-connected PCs are reported.

Keywords: Grid computing; electricity market; prices; forecasting; neural networks. (search for similar items in EconPapers)
Date: 2007
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://ejms.iseg.ulisboa.pt/files/2007-Electricit ... _neural_networks.pdf (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pjm:journl:v:xii:y:2007:i:2:p:133-143

Access Statistics for this article

Portuguese Journal of Management Studies is currently edited by Luís Mota de Castro, Tiago Cardão-Pito, Mark Crathorne

More articles in Portuguese Journal of Management Studies from ISEG, Universidade de Lisboa Contact information at EDIRC.
Bibliographic data for series maintained by Luís Mota de Castro, Tiago Cardão-Pito, Mark Crathorne ().