Publication
Electricity market price forecasting by grid computing optimizing artificial neural networks
| dc.contributor.author | Niimura, T. | |
| dc.contributor.author | Ozawa, K. | |
| dc.contributor.author | Sakamoto, N. | |
| dc.date.accessioned | 2015-11-03T11:21:07Z | |
| dc.date.available | 2015-11-03T11:21:07Z | |
| dc.date.issued | 2007 | |
| dc.description.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. | pt_PT |
| dc.identifier.citation | Niimura, T., K. Ozawa e N. Sakamoto (2007). "Electricity market price forecasting by grid computing optimizing artificial neural networks". Portuguese Journal of Management Studies, XII(2):133-144 | pt_PT |
| dc.identifier.uri | http://hdl.handle.net/10400.5/10011 | |
| dc.language.iso | eng | pt_PT |
| dc.publisher | Instituto Superior de Economia e Gestão | pt_PT |
| dc.subject | Grid computing | pt_PT |
| dc.subject | electricity market | pt_PT |
| dc.subject | prices | pt_PT |
| dc.subject | forecasting | pt_PT |
| dc.subject | neural networks | pt_PT |
| dc.title | Electricity market price forecasting by grid computing optimizing artificial neural networks | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.conferencePlace | Lisboa | pt_PT |
| oaire.citation.endPage | 144 | pt_PT |
| oaire.citation.issue | 2 | pt_PT |
| oaire.citation.startPage | 133 | pt_PT |
| oaire.citation.title | Portuguese Journal of Management Studies | pt_PT |
| oaire.citation.volume | XII | pt_PT |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | article | pt_PT |