Pronóstico de Demanda de Electricidad con Redes Neuronales Artificiales para Salta, Argentina

Franco Zanek

doi:10.56124/encriptar.v6i12.001

Authors

Franco Zanek Universidad Nacional de Salta, Argentina

DOI:

https://doi.org/10.56124/encriptar.v6i12.001

Keywords:

Artificial Neural Network, Electric Load Curve, Hourly Prediction

Abstract

Demand forecasts for electricity demand are extremely important for energy providers and other participants in the generation, transmission, distribution, and electricity markets. These forecasts are essential for the operation and planning of the electrical system, as they allow providers to optimize their operations and make informed decisions about future investments in generation and transmission infrastructure. Additionally, these forecasts also enable providers to anticipate potential imbalances between energy supply and demand, helping them avoid blackouts and maintain the stability of the electrical system. This article presents models based on Artificial Neural Networks (ANNs) to enable assertive prediction of hourly electricity demand over the course of a year. Given the high seasonality of electricity demand in general, the methodology proposes the development of a model for each month of the year. This study is carried out using data collected from different sources for the Salta Capital region in the province of Salta, Argentina. Based on the results obtained, we can say that the proposed methodology improves the mean squared error by 3% compared to previous works. These results are of particular importance for energy providers and other participants in electricity markets as they provide them with a more precise and reliable tool for decision-making in the operation and planning of the electrical system.

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