Abstract
In this paper we present a technique for prediction of electrical demand based on multiple models. The multiple models are composed by several local models, each one describing a region of behavior of the system, called operation regime. The multiple models approach developed in this work is applied to predict electrical load 24 hours ahead. Data of electrical load from the state of California that include an approximate period of 2 years was used as a case of study. The concept of multiple model implemented in the present work is also characterized by the combination of several techniques. Two important techniques are applied in the construction of multiple models: Regularization and the Knowledge Discovery in Data Bases (KDD) techniques. KDD is used to identify the operation regime of electrical load time series.
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Melgoza, J.J.R., Flores, J.J., Sotomane, C., Calderón, F. (2004). Extracting Temporal Patterns from Time Series Data Bases for Prediction of Electrical Demand. In: Monroy, R., Arroyo-Figueroa, G., Sucar, L.E., Sossa, H. (eds) MICAI 2004: Advances in Artificial Intelligence. MICAI 2004. Lecture Notes in Computer Science(), vol 2972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24694-7_3
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DOI: https://doi.org/10.1007/978-3-540-24694-7_3
Publisher Name: Springer, Berlin, Heidelberg
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