BMF fuzzy neural network with genetic algorithm for forecasting electric load
| dc.contributor | 國立臺灣師範大學電機工程學系 | zh_tw |
| dc.contributor.author | Y.-S. Lee | en_US |
| dc.contributor.author | C.-H. Kao | en_US |
| dc.contributor.author | W.-Y. Wang | en_US |
| dc.date.accessioned | 2014-10-30T09:28:23Z | |
| dc.date.available | 2014-10-30T09:28:23Z | |
| dc.date.issued | 2005-01-01 | zh_TW |
| dc.description.abstract | Electricity is widely applied in many aspects of modern life. Precise forecasting of electricity consumption may not only reduce operational and maintenance cost for power companies but also enhance the reliability of power supply systems, as well as avoid shortage of supply that causes damage and inconvenience to customers. In this paper, load forecasting is facilitated by a so-called BMF fuzzy neural network, which features a structure adjusted by genetic algorithm. The purpose is to obtain better control points and weights, so as to ensure sound performance. Seven networks are constructed in correspondence with the seven different electrical loading models from Monday to Sunday. Results of the simulation reflect the forecasted loading in winter and summer months | en_US |
| dc.description.uri | http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1619955 | zh_TW |
| dc.identifier | ntnulib_tp_E0604_02_060 | zh_TW |
| dc.identifier.uri | http://rportal.lib.ntnu.edu.tw/handle/20.500.12235/32037 | |
| dc.language | en | zh_TW |
| dc.relation | IEEE Power Electronics and Drives Systems( 2005 PEDS), Kuala Lumpur,pp. 1662-1667 | en_US |
| dc.subject.other | Fuzzy neural network | en_US |
| dc.subject.other | genetic algorithm | en_US |
| dc.subject.other | load forecasting | en_US |
| dc.title | BMF fuzzy neural network with genetic algorithm for forecasting electric load | en_US |