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Load forecasting performance enhancement when facing anomalous events

Fidalgo, J.N. ; Lopes, J.A.P.

IEEE transactions on power systems, 2005-02, Vol.20 (1), p.408-415 [Revista revisada por pares]

New York: IEEE

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Título:
Load forecasting performance enhancement when facing anomalous events
Autor: Fidalgo, J.N. ; Lopes, J.A.P.
Materias: Artificial neural networks ; Computer bugs ; Degradation ; Demand forecasting ; Economic forecasting ; Load forecasting ; neural networks ; power distribution ; Power system management ; Power system planning ; Power systems ; Shape
Es parte de: IEEE transactions on power systems, 2005-02, Vol.20 (1), p.408-415
Notas: ObjectType-Article-2
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Descripción: The application of artificial neural networks or other techniques in load forecasting usually outputs quality results in normal conditions. However, in real-world practice, a remarkable number of abnormalities may arise. Among them, the most common are the historical data bugs (due to SCADA or recording failure), anomalous behavior (like holidays or atypical days), sudden scale or shape changes following switching operations, and consumption habits modifications in the face of energy price amendments. Each of these items is a potential factor of forecasting performance degradation. This work describes the procedures implemented to avoid the performance degradation under such conditions. The proposed techniques are illustrated with real data examples of current, active, and reactive power forecasting at the primary substation level.
Editor: New York: IEEE
Idioma: Inglés

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Load forecasting performance enhancement when facing anomalous events

Fidalgo, J.N. ; Lopes, J.A.P.

New York: IEEE

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