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An adaptive modular artificial neural network hourly load forecaster and its implementation at electric utilities

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4 Author(s)
A. Khotanzad ; Dept. of Electr. Eng., Southern Methodist Univ., Dallas, TX, USA ; Rey-Chue Hwang ; A. Abaye ; D. Maratukulam

This paper describes a modular artificial neural network (ANN) based hourly load forecaster which has already been implemented at 20 electric utilities across the US and is being used on-line by several of them. The behavior or the load and its correlation with parameters affecting it (e.g. weather variables) are decomposed into three distinct trends of weekly, daily, and hourly. Each trend is modeled by a separate module containing several multi-layer feed-forward ANNs trained by the back-propagation learning rule. The forecasts produced by each module are then combined by adaptive filters to arrive at the final forecast. During the forecasting phase, the parameters of the ANNs within each module are adaptively changed in response to the system's latest forecast accuracy. The performance of the forecaster has been tested on data from these 20 utilities with excellent results. The on-line performance of the system has also been quite satisfactory and superior to other forecasting packages used by the utilities. Moreover, the forecaster is robust, easy to use, and produces accurate results in the case of rapid weather changes

Published in:

IEEE Transactions on Power Systems  (Volume:10 ,  Issue: 3 )