Load and Demand Side Flexibility Forecasting - Archive ouverte HAL Accéder directement au contenu
Communication Dans Un Congrès Année :

Load and Demand Side Flexibility Forecasting

(1) , ,
1
Etta Grover-Silva
  • Fonction : Auteur
  • PersonId : 1035450
Johanna Le Conte
  • Fonction : Auteur
  • PersonId : 1122624

Résumé

Recent developments in energy metering technologies have allowed electric load data to be more easily accessible. Services that use this data to inform customers can raise awareness about electricity consumption and provide suggestions to encourage energy efficient behavior. Quantifying the flexibility of the demand combined with accurate predictions of the total electric load allow information services to provide suggestions to end-users on how to reduce electric consumption that are appliance and time specific. With the arrival of new electric generation technologies, such as photovoltaic or wind energy, demand side flexibility will play an important role in the optimization of the future electric system. The accurate prediction of demand flexibility at a building level, therefore can contribute to the optimization of load scheduling. This study presents an effective multi-step technique to forecast the average hourly demand flexibility for a household, using neural networks, unsupervised clustering techniques and an optimization algorithm, combined with statistical studies. The model is mainly based on the historical electric use of a building and does not require significant computational capacity, thus making it widely applicable and informative for residential customers, helping them improve their behavior to be more energy efficient in the future.
Fichier principal
Vignette du fichier
ENERGY_30007_v3.pdf (258.36 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03520220 , version 1 (10-01-2022)

Identifiants

  • HAL Id : hal-03520220 , version 1

Citer

Rocio Alvarez Merce, Etta Grover-Silva, Johanna Le Conte. Load and Demand Side Flexibility Forecasting. ENERGY 2020, Sep 2020, Lisbon, Portugal. ⟨hal-03520220⟩
34 Consultations
32 Téléchargements

Partager

Gmail Facebook Twitter LinkedIn More