Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/62317
Title: Improved neural network approach for weather sensitive short-term load forecasting
Authors: Dash, P.K. 
Liew, A.C. 
Rahman, S.
Satpathy, J.K.
Ramakrishna, G.
Issue Date: Sep-1994
Source: Dash, P.K.,Liew, A.C.,Rahman, S.,Satpathy, J.K.,Ramakrishna, G. (1994-09). Improved neural network approach for weather sensitive short-term load forecasting. International Journal of Engineering Intelligent Systems for Electrical Engineering and Communications 2 (3) : 185-199. ScholarBank@NUS Repository.
Abstract: This paper presents feedforward neural networks with an improved Kalman filter based learning algorithm for forecasting weather sensitive loads. Several parameters like the sigmoid slope, Kalman gain and learning rate are adaptively tuned to provide fast convergence during learning and subsequent predictions. With these modifications, the accuracy of the forecasted load increases considerably in comparison to the earlier neural network based techniques. Extensive studies have been performed for hourly load predictions and dally average load predictions on a typical one - year utility data and forecast results for autumn, winter, spring and summer days are given to validate the effectiveness and efficacy of the above approach.
Source Title: International Journal of Engineering Intelligent Systems for Electrical Engineering and Communications
URI: http://scholarbank.nus.edu.sg/handle/10635/62317
ISSN: 09691170
Appears in Collections:Staff Publications

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