Neural networks ensembles for short-term load forecasting

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7 Citations (Scopus)

Abstract

This paper proposes a new approach for short-term load forecasting based on neural networks ensembling methods. A comparison between traditional statistical linear seasonal model and ANN-based models has been performed on the real-world building load data, considering the utilisation of external data such as the day of the week and building occupancy data. The selected models have been compared to the prediction of hourly demand for the electric power up to 24 hours for a testing week. Both neural networks ensembles achieved lower average and maximum errors than other models. Experiments showed how the introduction of external data had helped the forecasting. © 2011 IEEE.
Original languageEnglish
DOIs
Publication statusPublished - 2011
Externally publishedYes
EventSymposium Series on Computational Intelligence, IEEE SSCI 2011 - 2011 IEEE Symposium on Computational Intelligence Applications in Smart Grid, CIASG 2011 - , France
Duration: 1 Jan 2011 → …

Conference

ConferenceSymposium Series on Computational Intelligence, IEEE SSCI 2011 - 2011 IEEE Symposium on Computational Intelligence Applications in Smart Grid, CIASG 2011
CountryFrance
Period1/1/11 → …

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All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Computer Science Applications

Cite this

De Felice, M., & Yao, X. (2011). Neural networks ensembles for short-term load forecasting. Paper presented at Symposium Series on Computational Intelligence, IEEE SSCI 2011 - 2011 IEEE Symposium on Computational Intelligence Applications in Smart Grid, CIASG 2011, France. https://doi.org/10.1109/CIASG.2011.5953333