Please use this identifier to cite or link to this item: https://doi.org/10.1049/ip-gtd:19952245
Title: Power-demand forecasting using a neural network with an adaptive learning algorithm
Authors: Dash, P.K. 
Liew, A.C. 
Ramakrishna, G.
Issue Date: Nov-1995
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
URI: http://scholarbank.nus.edu.sg/handle/10635/62642
ISSN: 13502360
DOI: 10.1049/ip-gtd:19952245
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