One of the most controversial issues in the mid-term load forecasting literature is the treatment of weather. Because of the difficulty in obtaining precise weather forecasts for a few weeks ahead, researchers have, so far, implemented three approaches: a) excluding weather from load forecasting models altogether, b) assuming future weather to be perfectly known and c) including weather forecasts in their load forecasting models. This article provides the first systematic comparison of how the different treatments of weather affect load forecasting performance. We incorporate air temperature into short- and mid-term load forecasting models, comparing time-series methods and feed-forward neural networks. Our results indicate that models including future temperature always significantly outperform models excluding temperature, at all-time horizons. However, when future temperature is replaced with its prediction, these results become weaker.

Incorporating air temperature into mid-term electricity load forecasting models using time-series regressions and neural networks / Bashiri Behmiri, N.; Fezzi, C.; Ravazzolo, F.. - In: ENERGY. - ISSN 0360-5442. - 278:(2023), p. 127831. [10.1016/j.energy.2023.127831]

Incorporating air temperature into mid-term electricity load forecasting models using time-series regressions and neural networks

Bashiri Behmiri N.
;
Fezzi C.;
2023-01-01

Abstract

One of the most controversial issues in the mid-term load forecasting literature is the treatment of weather. Because of the difficulty in obtaining precise weather forecasts for a few weeks ahead, researchers have, so far, implemented three approaches: a) excluding weather from load forecasting models altogether, b) assuming future weather to be perfectly known and c) including weather forecasts in their load forecasting models. This article provides the first systematic comparison of how the different treatments of weather affect load forecasting performance. We incorporate air temperature into short- and mid-term load forecasting models, comparing time-series methods and feed-forward neural networks. Our results indicate that models including future temperature always significantly outperform models excluding temperature, at all-time horizons. However, when future temperature is replaced with its prediction, these results become weaker.
2023
Bashiri Behmiri, N.; Fezzi, C.; Ravazzolo, F.
Incorporating air temperature into mid-term electricity load forecasting models using time-series regressions and neural networks / Bashiri Behmiri, N.; Fezzi, C.; Ravazzolo, F.. - In: ENERGY. - ISSN 0360-5442. - 278:(2023), p. 127831. [10.1016/j.energy.2023.127831]
File in questo prodotto:
File Dimensione Formato  
Behmiri et al 2023 Energy.pdf

Solo gestori archivio

Tipologia: Versione editoriale (Publisher’s layout)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 3.6 MB
Formato Adobe PDF
3.6 MB Adobe PDF   Visualizza/Apri
1-s2.0-S0360544223012252-main.pdf

accesso aperto

Tipologia: Versione editoriale (Publisher’s layout)
Licenza: Creative commons
Dimensione 3.6 MB
Formato Adobe PDF
3.6 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/401109
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 14
  • ???jsp.display-item.citation.isi??? 11
social impact