Abstract
This paper presents a study of the influence of the accuracy of hourly load forecasting on the energy planning and operation of electric generation utilities. First, a k Nearest Neighbours (kNN) classification technique is proposed for hourly load forecasting. Then, obtained prediction errors are compared with those obtained results by using a M5’. Second, the obtained kNN-based load forecast is used to compute the optimal on/off status and generation scheduling of the units. Finally, the influence of forecasting errors on both the status and generation level of the units over the scheduling period is studied.
Keywords
- Nearest neighbours
- load forecasting
- optimal energy production
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Troncoso Lora, A., Riquelme, J.C., Martínez Ramos, J.L., Riquelme Santos, J.M., Gómez Expósito, A. (2003). Influence of kNN-Based Load Forecasting Errors on Optimal Energy Production. In: Pires, F.M., Abreu, S. (eds) Progress in Artificial Intelligence. EPIA 2003. Lecture Notes in Computer Science(), vol 2902. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24580-3_26
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DOI: https://doi.org/10.1007/978-3-540-24580-3_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20589-0
Online ISBN: 978-3-540-24580-3
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