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Modélisation par apprentissage automatique d’un chauffe-eau électrique pour prédire la consommation électrique.

Machine-learning model of electric water heater for electricity consumption prediction.

Numéro : pap. 3404

Auteurs : DONG J., MUNK J., CUI B., et al.

Résumé

The recent increase of smart meters in the residential sector has led to large available datasets. The electricity consumption of individual households/devices can be accessed in close to real time, and allows both the demand and supply side to extract valuable information for efficient energy management. Predicting electricity consumption should help utilities improve planning generation and demand side management, however this is not a trivial task as consumption at the individual household level varies with occupant behavior. In residential buildings, many loads have some power flexibility. One of them is water heater (WH), which accounts for up to 20% of home daily electricity use. Conventional methods for water heater power prediction, which heavily rely on physical principles, have limited applicability as their performance is subject to many physical assumptions. Recently, black-box models have gained huge interest due to their flexibility in model development and the rich availability of data in modern buildings. Black-box modelling methods can be further categorized into two types, i.e., statistical methods and supervised machine learning (ML). Since building operations are typically complicated and nonlinear, the resulting accuracy of simple statistical methods can be poor. Many ML-based, black-box approaches have the ability to characterize and forecast total energy consumption of commercial data. However, a paucity of research applying black-box methods have been tested on the hour ahead energy consumption forecasts for typical detached residential houses in the US. With the advances in smart metering, sub meter usage forecasts at the household-level is also gaining popularity for smart building control and demand response programs. This led us to develop black-box ML techniques to address the problem of residential hour and day ahead load forecasting of WH. The developed forecasting models are built using three common ML algorithms, support vector machines (SVM), Gaussian Naive Bayes, and Random Forest. Performance comparison among these ML methods was carried out. The results suggest that all models were able to correctly predict a greater proportion of the actual power consumption with prediction accuracy yields averagely between 94% ~ 96%. The SVM model performs the best, while the RF works the worst.

Documents disponibles

Format PDF

Pages : 10

Disponible

  • Prix public

    20 €

  • Prix membre*

    15 €

* meilleur tarif applicable selon le type d'adhésion (voir le détail des avantages des adhésions individuelles et collectives)

Détails

  • Titre original : Machine-learning model of electric water heater for electricity consumption prediction.
  • Identifiant de la fiche : 30024884
  • Langues : Anglais
  • Sujet : Environnement
  • Source : 2018 Purdue Conferences. 5th International High Performance Buildings Conference at Purdue.
  • Date d'édition : 09/07/2018

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