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Une approche basée sur les données pour l'intégration des conditions microclimatiques pour la prévision de la performance énergétique des bâtiments.

A data-driven approach towards integration of microclimate conditions for predicting building energy performance.

Numéro : pap. 3722

Auteurs : WU L., CHINDE V., SHARMA H., et al.

Résumé

Energy consumption in buildings is a major part of the overall energy usage in the United States and across the world. Energy performance of buildings is primarily affected by the heat exchange between the building outer skin and the surrounding environment. Building energy simulation (BES) tools are capable of predicting energy usage with variable degree of accuracy using the building geometry, construction and weather data. In this regard, it is a common practice in BES tools to include boundary conditions of the building shell based on the local weather station. However, to account for accurate building energy consumption, especially in urban environments with lots of anthropogenic heat source it is necessary to consider the microclimate around the building. These conditions are influenced by the immediate environment such as surrounding buildings, hard surfaces and trees. However, deployment of sensors to monitor microclimate of a building can be quite expensive and hence, not scalable. Therefore, a model to predict the microclimate information is essential to provide a more reasonable weather information for the BES tools, and hence, to predict energy consumption in buildings more accurately. In this work, we propose a scalable, computationally inexpensive data-driven approach for predicting microclimate information (e.g., temperature) under multiple weather conditions. We demonstrate that such a framework can be implemented based on machine learning techniques such as
spatiotemporal pattern network (STPN) and neural networks (NNs).

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 : A data-driven approach towards integration of microclimate conditions for predicting building energy performance.
  • Identifiant de la fiche : 30025078
  • 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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