Predicción de demanda eléctrica utilizando redes neuronales artificiales para CNEL EP – unidad de negocios agencia Quevedo - Mora Silva, Alvaro Leonel (2021)
The purpose of this research is to perform the projection of the demand for electrical energy by using an Artificial Neural Networks (ANN) model. To develop this projection, historical consumption data (maximum monthly demand) provided by the electricity company CNEL EP – UNIDAD DE NEGOCIOS AGENCIA QUEVEDO are used from the period 2003 - 2008. The projection of electricity demand is essential to guarantee mainly the continuity of the electricity supply generating reliability in consumers. In this project, a model of ANNs is tested, which has been developed with greater frequency over time. The data processing is done through 3 layers that are part of the ANN, these layers consist of: input layer, hidden layer and output layer, which through mathematical procedures delivers continuous values that represent the result. The development of the results is divided into stages, which consist of: ANN training with 85% of the historical data, ANN test under the evaluation standard Mean Absolute Percentage Error (MAPE) between 15% of the Values not used in the training versus the projection for historical data, this stage allows demonstrating the reliability of the model based on the percentage error, and thus making a projection with a longer time horizon. Keywords: ANN, backpropagation, electrical demand, gradient decrease, MAPE