Previsão de Demanda de Energia Elétrica em Microgrid Considerando Níveis Menos Agregados por Meio da Aplicação de Rede Neural Artificial GRNN Combinada com o Método Estatístico SARIMA

Abstract

The growth in electricity consumption in the world forces countries to have a well-structured planning in relation to forecasting the demand for electricity in their most diverse sectors. Several techniques are used to predict electrical loads, such as artificial intelligence models, statistical models and hybrid models. This work aims to present a model based on the combination of a statistical method, SARIMA, and an artificial neural network, the GRNN, to improve the accuracy of forecasts of demand for electricity consumption. The data set used in this work belongs to a group of buildings located in the Itaipu Technological Park and was acquired through electronic meters installed together with the transformers that serve each of these buildings, performing data collection every 15 minutes. After processing and refining the database, forecasting techniques were applied, each one using a forecast horizon of 1, 3 and 5 days, the first technique being the combination between GRNN and SARIMA and the other techniques used were the methods themselves separately, allowing the comparison of their results. The results obtained with the proposed combined model are, in general, more accurate when compared to the results of the techniques individually, as they combine the advantages of each technique and end up smoothing the negative characteristics of each other, thus causing a balance that reflects on the forecast. generated.