Abstract
Obtaining an accurate forecast of the energy demand is fundamental to support the several decision processes of the electricity service agents in a country. For market operators, a greater precision in the short-term load forecasting implies a more efficient programming of the electricity generation resources, which means a reduction in costs. In the long term, it constitutes a main indicator for the generation of investment signals for future installed capacity. This research proposes a prognostic model for the demand of electrical energy in Bogota, Colombia at hourly level in a full week, through Artificial Neural Network.
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Viloria, A., Pichon, A.R., Hernandez-P, H., Bilbao, O.R., Lezama, O.B.P., Vargas, J. (2020). Forecast of the Demand for Hourly Electric Energy by Artificial Neural Networks. In: Gunjan, V., Senatore, S., Kumar, A., Gao, XZ., Merugu, S. (eds) Advances in Cybernetics, Cognition, and Machine Learning for Communication Technologies. Lecture Notes in Electrical Engineering, vol 643. Springer, Singapore. https://doi.org/10.1007/978-981-15-3125-5_46
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DOI: https://doi.org/10.1007/978-981-15-3125-5_46
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