Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/62467
Title: New approach to daily and peak load predictions using a random vector functional-link network
Authors: Dash, P.K. 
Satpathy, H.P.
Swain, D.P.
Liew, A.C. 
Keywords: Electric load forecasting
Load prediction
Random vector functional-link network
Issue Date: 1997
Source: Dash, P.K.,Satpathy, H.P.,Swain, D.P.,Liew, A.C. (1997). New approach to daily and peak load predictions using a random vector functional-link network. International Journal of Engineering Intelligent Systems for Electrical Engineering and Communications 5 (1) : 11-19. ScholarBank@NUS Repository.
Abstract: A functional-link neural network based short-term electric load forecasting system is presented in this paper. The electric load forecasting model is assumed to consist of a load time series and a weather dependent component modelled as a functional expansion of the weather variables like temperature or humidity. The parameters of the functional link neural network model are identified using a weight adjustment algorithm based on Widrow-Hoff delta rule. The non linear weight adjustment algorithm adapts the weights every 24-hour or 168-hour producing a MAPE mostly less than 2% for a 24-hour ahead forecast and 2.5% for a 168-hour ahead forecast. The results of forecast for a period over 2 years indicate that the new model produces more accurate and robust forecasts in comparison to simple adaptive, neural network or statistical approaches.
Source Title: International Journal of Engineering Intelligent Systems for Electrical Engineering and Communications
URI: http://scholarbank.nus.edu.sg/handle/10635/62467
ISSN: 09691170
Appears in Collections:Staff Publications

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