Skip to main content
Journal cover image

Short-term load forecasting using neural networks and global climate models: An application to a large-scale electrical power system

Publication ,  Journal Article
Morais, LBS; Aquila, G; de Faria, VAD; Lima, LMM; Lima, JWM; de Queiroz, AR
Published in: Applied Energy
October 15, 2023

This paper focuses on the development of shallow and deep neural networks in the form of multi-layer perceptron, long-short term memory, and gated recurrent unit to model the short-term load forecasting problem. Different model architectures are tested, and global climate model information is used as input to generate more accurate forecasts. 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.

Duke Scholars

Published In

Applied Energy

DOI

ISSN

0306-2619

Publication Date

October 15, 2023

Volume

348

Related Subject Headings

  • Energy
  • 40 Engineering
  • 38 Economics
  • 33 Built environment and design
  • 14 Economics
  • 09 Engineering
 

Citation

APA
Chicago
ICMJE
MLA
NLM
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. https://doi.org/10.1016/j.apenergy.2023.121439
Morais, L. B. S., G. Aquila, V. A. D. de Faria, L. M. M. Lima, J. W. M. Lima, and A. R. de Queiroz. “Short-term load forecasting using neural networks and global climate models: An application to a large-scale electrical power system.” Applied Energy 348 (October 15, 2023). https://doi.org/10.1016/j.apenergy.2023.121439.
Morais LBS, Aquila G, de Faria VAD, Lima LMM, Lima JWM, de Queiroz AR. Short-term load forecasting using neural networks and global climate models: An application to a large-scale electrical power system. Applied Energy. 2023 Oct 15;348.
Morais, L. B. S., et al. “Short-term load forecasting using neural networks and global climate models: An application to a large-scale electrical power system.” Applied Energy, vol. 348, Oct. 2023. Scopus, doi:10.1016/j.apenergy.2023.121439.
Morais LBS, Aquila G, de Faria VAD, Lima LMM, Lima JWM, de Queiroz AR. Short-term load forecasting using neural networks and global climate models: An application to a large-scale electrical power system. Applied Energy. 2023 Oct 15;348.
Journal cover image

Published In

Applied Energy

DOI

ISSN

0306-2619

Publication Date

October 15, 2023

Volume

348

Related Subject Headings

  • Energy
  • 40 Engineering
  • 38 Economics
  • 33 Built environment and design
  • 14 Economics
  • 09 Engineering