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A neural network based technique for short-term forecasting of anomalous load periods

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5 Author(s)
R. Lamedica ; Dept. of Electr. Eng., Rome Univ., Italy ; A. Prudenzi ; M. Sforna ; M. Caciotta
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The paper illustrates a part of the research activity conducted by the authors in the field of electric short term load forecasting (STLF) based on artificial neural network (ANN) architectures. Previous experiences with basic ANN architectures have shown that, even though these architectures provide results comparable with those obtained by human operators for most normal days, they evidence some accuracy deficiencies when applied to “anomalous” load conditions occurring during holidays and long weekends. For these periods a specific procedure based upon a combined (unsupervised/supervised) approach has been proposed. The unsupervised stage provides a preventive classification of the historical load data by means of a Kohonen's self-organizing map (SOM). The supervised stage, performing the proper forecasting activity, is obtained by using a multi-layer perceptron with a backpropagation learning algorithm similar to the ones mentioned above. The unconventional use of information deriving from the classification stage permits the proposed procedure to obtain a relevant enhancement of the forecast accuracy for anomalous load situations

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IEEE Transactions on Power Systems  (Volume:11 ,  Issue: 4 )