Please use this identifier to cite or link to this item: https://doi.org/10.1109/59.589648
Title: A real-time short-term load forecasting system using functional link network
Authors: Dash, P.K.
Satpathy, H.P.
Liew, A.C. 
Rahman, S.
Issue Date: 1997
Source: Dash, P.K., Satpathy, H.P., Liew, A.C., Rahman, S. (1997). A real-time short-term load forecasting system using functional link network. IEEE Transactions on Power Systems 12 (2) : 675-680. ScholarBank@NUS Repository. https://doi.org/10.1109/59.589648
Abstract: This paper presents a new functional-link network based short-term electric load forecasting system for realtime implementation. The load and weather parameters are modelled as a nonlinear ARMA process and parameters of this model are obtained using the functional approximation capabilities of an auto-enhanced Functional Link net. The adaptive mechanism with a nonlinear learning rule is used to train the link network on-line. The results indicate that the functional link net based load forecasting system produces robust and more accurate load forecasts in comparison to simple adaptive neural network or statistical based approaches. Testing the algorithm with load and weather data for a period of two years reveals satisfactory performance with mean absolute percentage error (MAPE) mostly less than 2% for a 24-hour ahead forecast and less than 2.5% for a 168-hour ahead forecast. © 1996 IEEE.
Source Title: IEEE Transactions on Power Systems
URI: http://scholarbank.nus.edu.sg/handle/10635/54770
ISSN: 08858950
DOI: 10.1109/59.589648
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