Abstract
This paper describes a time-series prediction method based on the kNN technique. The proposed methodology is applied to the 24-hour load forecasting problem. Also, based on recorded data, an alternative model is developed by means of a conventional dynamic regression technique, where the parameters are estimated by solving a least squares problem. Finally, results obtained from the application of both techniques to the Spanish transmission system are compared in terms of maximum, average and minimum forecasting errors.
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Troncoso Lora, A., Riquelme Santos, J.M., Riquelme, J.C., Gómez Expósito, A., Martínez Ramos, J.L. (2004). Time-Series Prediction: Application to the Short-Term Electric Energy Demand. In: Conejo, R., Urretavizcaya, M., Pérez-de-la-Cruz, JL. (eds) Current Topics in Artificial Intelligence. TTIA 2003. Lecture Notes in Computer Science(), vol 3040. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-25945-9_57
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DOI: https://doi.org/10.1007/978-3-540-25945-9_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22218-7
Online ISBN: 978-3-540-25945-9
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