Please use this identifier to cite or link to this item:
|Title:||Power-demand forecasting using a neural network with an adaptive learning algorithm|
|Authors:||Dash, P.K. |
|Source:||Dash, P.K., Liew, A.C., Ramakrishna, G. (1995-11). Power-demand forecasting using a neural network with an adaptive learning algorithm. IEE Proceedings: Generation, Transmission and Distribution 142 (6) : 560-568. ScholarBank@NUS Repository. https://doi.org/10.1049/ip-gtd:19952245|
|Abstract:||An artificial neural network with an adaptive-Kalman-filter-based learning algorithm is presented for forecasting weather-sensitive loads. The proposed model can differentiate between weekday and weekend loads. This neural-network model has been implemented using real load data. The results reveal the efficiency and accuracy of the proposed approach in terms of short learning time, rapid convergence and the adaptive nature of the learning algorithm.|
|Source Title:||IEE Proceedings: Generation, Transmission and Distribution|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Feb 15, 2018
WEB OF SCIENCETM
checked on Jan 31, 2018
checked on Feb 12, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.