Short-term load forecasting using neural networks and global climate models: An application to a large-scale electrical power system
Published in Applied Energy , 2023
Recommended citation: Morais, L. B. S., Aquila, G., de Faria, V. A. D., Lima, L. M. M., Lima, J. W. M., & de Queiroz, A. R. (2023). Short-term load forecasting using neural networks and global climate models: An application to a large-scale electrical power system. Applied Energy, 348, 121439. https://doi.org/10.1016/j.apenergy.2023.121439
A real study case is presented for the Brazilian interconnected power system and the results generated are compared with the forecasts from the Brazilian Independent System Operator model. In general terms, results show that the bidirectional versions of long-short term memory and gated recurrent unit produce better and more reliable predictions than the other models. From the obtained results, the recurrent neural networks reach Nash-Sutcliffe values up to 0.98, and mean absolute percentile error values of 1.18%, superior than the results obtained by the Independent System Operator models (0.94 and 2.01% respectively). The better performance of the neural network models is confirmed under the Diebold-Mariano pairwise comparison test.