Citas bibligráficas
García, L., (2021). Modelamiento del pronóstico de la demanda eléctrica diaria del sistema eléctrico interconectado nacional utilizando técnicas de MACHINE LEARNING [Tesis, Universidad Nacional de Ingeniería]. http://hdl.handle.net/20.500.14076/21832
García, L., Modelamiento del pronóstico de la demanda eléctrica diaria del sistema eléctrico interconectado nacional utilizando técnicas de MACHINE LEARNING [Tesis]. PE: Universidad Nacional de Ingeniería; 2021. http://hdl.handle.net/20.500.14076/21832
@misc{renati/266886,
title = "Modelamiento del pronóstico de la demanda eléctrica diaria del sistema eléctrico interconectado nacional utilizando técnicas de MACHINE LEARNING",
author = "García Fernández, Leonardo Brain",
publisher = "Universidad Nacional de Ingeniería",
year = "2021"
}
This thesis develops a computational model to forecast the Peruvian electricity demand in the short term, based on the performance comparison between two methodologies that use the neural networks of the Adaptive Resonance Theory (ARTMAP Fuzzy) and the Neuro-Fuzzy model (ANFIS), techniques belonging to Machine Learning (ML) and applied to the National Interconnected Electric System (SEIN). In addition, the thesis’ content proposes the pre-processing methodology of historical data set as an alternative to improve performance in the results presented. The different scenarios developed in the demand forecast include the years 2019 and 2020, where neural network methodologies seek the best option in terms of absolute mean percentage error (MAPE). Finally, the results of the proposed comparison raises the suggestion to update procedure 03 (PR03), methodology proposed by the Coordinator of the Operation of the Interconnected Electric System (COES).
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