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Developing data-driven models to predict BEMS energy consumption for demand response systems

Par Conseil national de recherches du Canada

DOITrouver le DOI : https://doi.org/10.1007/978-3-319-07455-9_20
AuteurRechercher : 1; Rechercher : 1; Rechercher : 1
Affiliation
  1. Conseil national de recherches du Canada. Technologies de l'information et des communications
FormatTexte, Chapitre de livre
Conférence27th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA/AIE 2014), June 3-6, 2014, Kaohsiung, Taiwan
Sujetcooling systems; electric load forecasting; intelligent systems; learning algorithms; learning systems; models; office buildings; weather forecasting; air handling units; building energy management systems; data-driven model; demand response; energy consumption prediction; energy-saving measures; forecast information; residential building; energy utilization
Résumé
Energy consumption prediction for building energy management systems (BEMS) is one of the key factors in the success of energy saving measures in modern building operation, either residential buildings or commercial buildings. It provides a foundation for building owners to optimize not only the energy usage but also the operation to respond to the demand signals from smart grid. However, modeling energy consumption in traditional physic-modeling techniques remains a challenge. To address this issue, we present a data-mining-based methodology, as an alternative, for developing data-driven models to predict energy consumption for BEMSs. Following the methodology, we developed data-driven models for predicting energy consumption for a chiller in BEMS by using historic building operation data and weather forecast information. The models were evaluated with unseen data. The experimental results demonstrated that the data-driven models can predict energy consumption for chiller with promising accuracy.
Date de publication
Maison d’éditionSpringer International Publishing
Dans
  • Modern Advances in Applied Intelligence: 27th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2014, Kaohsiung, Taiwan, June 3-6, 2014, Proceedings, Part I (2014) : 188–197.
Série
  • Lecture Notes in Computer Science, nº 8481.
Langueanglais
Publications évaluées par des pairsOui
Numéro NPARC21272679
Exporter la noticeExporter en format RIS
Signaler une correctionSignaler une correction (s'ouvre dans un nouvel onglet)
Identificateur de l’enregistrement84fae62b-fdd8-4e41-9ed3-09c71557c297
Enregistrement créé2014-12-03
Enregistrement modifié2020-06-18
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