Yayın: The local power demand estimation based on artificial neural network technique
| dc.contributor.author | Kilic, O | |
| dc.contributor.author | Attar, F | |
| dc.contributor.author | Yumurtaci, R | |
| dc.contributor.author | Tanrioven, M | |
| dc.date.accessioned | 2026-06-27T12:56:22Z | |
| dc.date.issued | 1998 | |
| dc.description.abstract | The demand to electrical energy increases day by day. It is very important to reflect this increasing demand accurately to power plant planning. ANN technique can be effectively used in loan forecasting. In this paper, ANN load forecasting is performed by using some non-linear input parameters such as temperature, humidity, rain conditions. Real electrical date obtained for the national grid and meteorological parameters are used in the presented application. | en |
| dc.identifier.endpage | 991 | |
| dc.identifier.isbn | 0-7803-3879-0 | |
| dc.identifier.issn | 0843-932X | |
| dc.identifier.startpage | 988 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14981/47904 | |
| dc.identifier.wos | 000075341900213 | |
| dc.language.iso | eng | |
| dc.publisher | IEEE | |
| dc.relation.conference | 9th Mediterranean Electrotechnical Conference (Melecon 98) | |
| dc.relation.ispartof | MELECON '98 - 9TH MEDITERRANEAN ELECTROTECHNICAL CONFERENCE, VOLS 1 AND 2 | |
| dc.subject | artificial neural network | |
| dc.subject | backpropagation algorithms | |
| dc.subject | learning rate | |
| dc.subject | load forecasting | |
| dc.subject | neuron | |
| dc.subject | Automation & Control Systems | |
| dc.subject | Computer Science | |
| dc.subject | Engineering | |
| dc.subject | Optics | |
| dc.subject | Telecommunications | |
| dc.title | The local power demand estimation based on artificial neural network technique | |
| dc.type | Proceedings Paper | |
| dspace.entity.type | Publication | |
| local.import.source | WOS |