Previsão de carga de curto prazo utilizando redes neurais artificiais

Abstract

In electrical power systems, one of the main challenges faced by system operators (ISO) is ensuring a reliable balance between energy supply and demand. For this, a proper planning of the system operation is necessary. Short-Term Load Forecasting (PCCP) is essential in this process, as it assists in planning daily operations, including matching demand and supply, setting future electricity prices and defining generation reserves. As smart grid technologies and intermittent renewable energy sources have increased significantly in electricity markets, the task of forecasting the load has increased in complexity and poses a challenge for ISOs. Nonlinear models based on machine learning techniques have become quite popular in recent years, as has the use of hybrid models focused on specific problems. This work focuses on the development of artificial neural network models to solve the PCCP problem, among them the MultiLayer Perceptron (MLP) networks with different numbers of layers and the Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) recurrent networks in its unidirectional and bidirectional versions. In addition to the data provided by ISO, predicted temperature information obtained from the Global Ensemble Forecast System (GEFS) model was used to try to generate more accurate load forecasts. In general terms, the results show that the recurrent models produce greater accuracy and more reliable results than the other models, including the models used by the Brazilian ISO. This is emphasized with the application of the Diebold-Mariano in a paired comparison test between models.