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Short-term load forecasting with local ANN predictors

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2 Author(s)
Drezga, I. ; Center for Energy and the Global Environ., Virginia Polytech. Inst. & State Univ., Blacksburg, VA, USA ; Rahman, S.

A new technique for artificial neural network (ANN) based short-term load forecasting (STLF) is presented in this paper. The technique implemented active selection of training data, employing the k-nearest neighbors concept. A novel concept of pilot simulation was used to determine the number of hidden units for the ANNs. The ensemble of local ANN predictors was used to produce the final forecast, whereby the iterative forecasting procedure used a simple average of ensemble ANNs. Results obtained using data from two US utilities showed forecasting accuracy comparable to those using similar techniques. Excellent forecasts for one-hour-ahead and five-days-ahead forecasting, robust behavior for sudden and large weather changes, low maximum errors and accurate peak-load predictions are some of the findings discussed in the paper

Published in:

Power Systems, IEEE Transactions on  (Volume:14 ,  Issue: 3 )