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Scalable Smartification of Commercial Buildings HVAC Systems Using the Internet of Things and Machine Learning

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dc.contributor.authorMahdi M.N.en_US
dc.contributor.authorBakare T.A.en_US
dc.contributor.authorAhmad A.R.en_US
dc.contributor.authorBuhari A.M.en_US
dc.contributor.authorMohamed K.S.en_US
dc.contributor.authorid56727803900en_US
dc.contributor.authorid57232790600en_US
dc.contributor.authorid35589598800en_US
dc.contributor.authorid56525158000en_US
dc.contributor.authorid57216259938en_US
dc.date.accessioned2023-05-29T09:42:06Z
dc.date.available2023-05-29T09:42:06Z
dc.date.issued2022
dc.description.abstractMost of the commercial buildings in Malaysia are still equipped with the legacy control for their Heat ventilation and air conditioning system (HVAC), which several studies claimed to have contributed to energy consumption in buildings. A significant amount of this energy is consumed by the building Heat Ventilation and Air Conditioning units. This is mostly due to the lack of smart and remote functionalities in the legacy HVAC systems to control the chillers and the Air handling units. This massive energy consumption is an antithesis to what governments all over the world are aiming for. However, scalability and deployment of low-cost resource-limited hardware embedded with control algorithms used to save energy in commercial building�s Heat Ventilation and Air Conditioning (HVAC) units is a difficult engineering task. But the unprecedented advancement and perverseness of information technology services over the past two decades has led to an ever more connected world. This project will leverage the concept of the Internet of Energy to make the systems smarter and more decentralized for flexible energy usage. Modern-day devices are increasingly linked to the internet, creating what is now referred to as the internet of things (IoT). The IoT paradigm has provided technologists with the ability to remotely control devices, and with the recent progress in Machine learning (ML) and Artificial Intelligence (AI), devices are trained to make smart decisions that can independently influence human to machine interactions. � 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/978-3-030-85990-9_15
dc.identifier.epage174
dc.identifier.scopus2-s2.0-85121821578
dc.identifier.spage165
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85121821578&doi=10.1007%2f978-3-030-85990-9_15&partnerID=40&md5=089ab749d309808fe5a7e5dc89b402c8
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/27283
dc.identifier.volume322
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.sourceScopus
dc.sourcetitleLecture Notes in Networks and Systems
dc.titleScalable Smartification of Commercial Buildings HVAC Systems Using the Internet of Things and Machine Learningen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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