Výsledky vyhledávání - "building automation"

Periodical

It’s in the Data How to Better Understand Your Building.

Autor: VANDERSLUIS, BENJAMIN¹

Publikováno v: Facility Management Journal (FMJ). Jan/Feb2022, Vol. 32 Issue 1, p60-62. 3p.

Akademický článek

Understanding the opportunities and challenges of building automation and control systems to support facility management – an extensive literature review

Autor: van Roosmale, Sophie, Audenaert, Amaryllis, Meysman, Jasmine

Publikováno v: Facilities, 2024, Vol. 42, Issue 7/8, pp. 677-693.

Externí odkaz: http://www.emeraldinsight.com/doi/10.1108/F-05-2023-0042

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Akademický článek

On Building Automation System security

Autor: Christopher Morales-Gonzalez, Matthew Harper, Michael Cash, Lan Luo, Zhen Ling, Qun Z. Sun, Xinwen Fu

Publikováno v: High-Confidence Computing, Vol 4, Iss 3, Pp 100236- (2024)

Building Automation Systems (BASs) are seeing increased usage in modern society due to the plethora of benefits they provide such as automation for climate control, HVAC systems, entry systems, and lighting controls. Many BASs in use are outdated and

Externí odkaz: https://doaj.org/article/4d8742814fa4497c9f7b382405301cef

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Akademický článek

Applicability of different wireless networks adopted for time-critical control tasks in buildings: A comprehensive comparison study

Autor: Li, Xinyue ^{a, d}, Li, Hangxin ^a, Cao, Jiannong ^b, Wang, Shengwei ^{a, c, ⁎}

Publikováno v: In Journal of Building Engineering 1 December 2024 98

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Akademický článek

Encrypted fully model-free event-triggered HVAC control

Autor: Feng, Zhenan, Nekouei, Ehsan ^⁎

Publikováno v: In Journal of Building Engineering 1 December 2024 98

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Akademický článek

Building automation and control systems for office buildings: Technical insights for effective facility management - A literature review

Autor: van Roosmale, S. ^{a, ⁎}, Hellinckx, P. ^b, Meysman, J. ^a, Verbeke, S. ^{a, c, d}, Audenaert, A. ^a

Publikováno v: In Journal of Building Engineering 15 November 2024 97

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Akademický článek

Modeling and Prediction of Occupancy in Buildings Based on Sensor Data Using Deep Learning Methods

Autor: Georgiana Cretu, Iulia Stamatescu, Grigore Stamatescu

Publikováno v: IEEE Access, Vol 12, Pp 102994-103003 (2024)

Accurate modelling and prediction of indoor occupancy can lead to efficient optimization and control of building energy consumption. This research uses indirect ambient sensor measurements and heterogeneous data types, together with state of the art

Externí odkaz: https://doaj.org/article/f10fbce0294e4505832b2a4c4b0ef303

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