Abstract
Two practical techniques: Time Series (TS) and Artificial Neural Networks (ANN), for the one-step-ahead short-term peak load forecasting have been proposed and discussed in this paper. We use weather variables since it is well known that better forecasting performances can be obtained taking them into account. The order selection of TS and the number input neurons of the ANN have are based on the computation of correlation functions. Their performances are evaluated through a simulation study. An extensive test activity of the two techniques shows that have better forecasting accuracy and robustness ANN models.
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© 1997 Springer-Verlag Berlin Heidelberg
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Marín, F.J., Sandoval, F. (1997). Short-term peak load forecasting: Statistical methods versus Artificial Neural Networks. In: Mira, J., Moreno-Díaz, R., Cabestany, J. (eds) Biological and Artificial Computation: From Neuroscience to Technology. IWANN 1997. Lecture Notes in Computer Science, vol 1240. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0032594
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DOI: https://doi.org/10.1007/BFb0032594
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