The paper illustrates a part of the research activity conducted by 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 above mentioned. 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.

A neural network based technique for short-term forecasting of anomalous load periods / M., Caciotta; Lamedica, Regina; V., Orsolini Cencelli; A., Prudenzi; M., Sforna. - In: IEEE TRANSACTIONS ON POWER SYSTEMS. - ISSN 0885-8950. - STAMPA. - 11:4(1996), pp. 1749-1756. ((Intervento presentato al convegno 1996 IEEE/PES Winter Meeting tenutosi a BALTIMORE, MD nel JAN 21-25, 1996 [10.1109/59.544638].

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

LAMEDICA, Regina;
1996

Abstract

The paper illustrates a part of the research activity conducted by 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 above mentioned. 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.
artificial neural networks; self-organizing map; short-term load forecasting
01 Pubblicazione su rivista::01a Articolo in rivista
A neural network based technique for short-term forecasting of anomalous load periods / M., Caciotta; Lamedica, Regina; V., Orsolini Cencelli; A., Prudenzi; M., Sforna. - In: IEEE TRANSACTIONS ON POWER SYSTEMS. - ISSN 0885-8950. - STAMPA. - 11:4(1996), pp. 1749-1756. ((Intervento presentato al convegno 1996 IEEE/PES Winter Meeting tenutosi a BALTIMORE, MD nel JAN 21-25, 1996 [10.1109/59.544638].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/91148
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