The proliferation of urban sensing, IoT, and big data in buildings, cities, and urban areas provides unprecedented opportunities for a deeper understanding of occupant behavior, transportation, and energy and water usage patterns. However, utilizing the existing data sources and modeling methods in building science to model urban scale occupant behaviors can be pretty challenging. Therefore, technological progress is needed to unlock its full potential. In order to fulfill the latter task, this workshop focuses on the methodologies for big urban and building data collection, analytics, modeling, and real-world technology deployment. The workshop aims to open discussion on the current challenges of big data in smart buildings and cities.

The 1st ACM international workshop on big data and machine learning for smart buildings and cities

Carlucci S
2021-01-01

Abstract

The proliferation of urban sensing, IoT, and big data in buildings, cities, and urban areas provides unprecedented opportunities for a deeper understanding of occupant behavior, transportation, and energy and water usage patterns. However, utilizing the existing data sources and modeling methods in building science to model urban scale occupant behaviors can be pretty challenging. Therefore, technological progress is needed to unlock its full potential. In order to fulfill the latter task, this workshop focuses on the methodologies for big urban and building data collection, analytics, modeling, and real-world technology deployment. The workshop aims to open discussion on the current challenges of big data in smart buildings and cities.
2021
AA.VV.
BuildSys '21: Proceedings of the 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation
9781450391146
8th ACM International Conference on Systems for Energy-Efficient Built Environments, BuildSys 2021
Coimbra, Portogallo
17/11/2021
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11383/2177371
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