Scalable smartification of commercial buildings HVAC systems using the internet of Things and Machine Learning

Mohammad Najah Mahdi; Taofiq Adeola Bakare; Abdul Rahim Ahmad; Adamu Muhammad Buhari; Khalid Sheikhidris Mohamed

doi:10.1007/978-3-030-85990-9_15

Scalable smartification of commercial buildings HVAC systems using the internet of Things and Machine Learning

Mohammad Najah Mahdi, Taofiq Adeola Bakare, Abdul Rahim Ahmad, Adamu Muhammad Buhari, Khalid Sheikhidris Mohamed

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research

1 Citation (Scopus)

Abstract

Most 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.

Original language	English
Title of host publication	Proceedings of International Conference on Emerging Technologies and Intelligent Systems - ICETIS 2021
Editors	Mostafa Al-Emran, Mohammed A. Al-Sharafi, Mohammed N. Al-Kabi, Khaled Shaalan
Place of Publication	Cham Switzerland
Publisher	Springer
Pages	165-174
Number of pages	10
Volume	2
ISBN (Electronic)	9783030859909
ISBN (Print)	9783030859893
DOIs	https://doi.org/10.1007/978-3-030-85990-9_15
Publication status	Published - 2022
Externally published	Yes
Event	International Conference on Emerging Technologies and Intelligent Systems 2021 - Al Buraimi, Oman Duration: 25 Jun 2021 → 26 Jun 2021 https://link.springer.com/book/10.1007/978-3-030-85990-9 (Proceedings)

Publication series

Name	Lecture Notes in Networks and Systems
Publisher	Springer
Volume	322
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	International Conference on Emerging Technologies and Intelligent Systems 2021
Abbreviated title	ICETIS 2021
Country/Territory	Oman
City	Al Buraimi
Period	25/06/21 → 26/06/21
Internet address	https://link.springer.com/book/10.1007/978-3-030-85990-9 (Proceedings)

Keywords

Artificial intelligence
Energy consumption
Heat ventilation and air conditioning
Internet of energy
Internet of things
Machine learning

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-030-85990-9_15

Cite this

Mahdi, M. N., Bakare, T. A., Ahmad, A. R., Buhari, A. M., & Mohamed, K. S. (2022). Scalable smartification of commercial buildings HVAC systems using the internet of Things and Machine Learning. In M. Al-Emran, M. A. Al-Sharafi, M. N. Al-Kabi, & K. Shaalan (Eds.), Proceedings of International Conference on Emerging Technologies and Intelligent Systems - ICETIS 2021 (Vol. 2, pp. 165-174). (Lecture Notes in Networks and Systems; Vol. 322). Springer. https://doi.org/10.1007/978-3-030-85990-9_15

Mahdi, Mohammad Najah ; Bakare, Taofiq Adeola ; Ahmad, Abdul Rahim et al. / Scalable smartification of commercial buildings HVAC systems using the internet of Things and Machine Learning. Proceedings of International Conference on Emerging Technologies and Intelligent Systems - ICETIS 2021. editor / Mostafa Al-Emran ; Mohammed A. Al-Sharafi ; Mohammed N. Al-Kabi ; Khaled Shaalan. Vol. 2 Cham Switzerland : Springer, 2022. pp. 165-174 (Lecture Notes in Networks and Systems).

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title = "Scalable smartification of commercial buildings HVAC systems using the internet of Things and Machine Learning",

abstract = "Most 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{\textquoteright}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.",

keywords = "Artificial intelligence, Energy consumption, Heat ventilation and air conditioning, Internet of energy, Internet of things, Machine learning",

author = "Mahdi, {Mohammad Najah} and Bakare, {Taofiq Adeola} and Ahmad, {Abdul Rahim} and Buhari, {Adamu Muhammad} and Mohamed, {Khalid Sheikhidris}",

note = "Funding Information: This work was supported by the Universiti Tenaga Nasional (UNITEN) internal grant no RJO10517844/005 (2020). Many thanks to the Innovation & Research Management Center (iRMC), UNITEN who provided their assistance and expertise during the research. Funding Information: Acknowledgements This work was supported by the Universiti Tenaga Nasional (UNITEN) internal grant no RJO10517844/005 (2020). Many thanks to the Innovation & Research Management Center (iRMC), UNITEN who provided their assistance and expertise during the research. Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; International Conference on Emerging Technologies and Intelligent Systems 2021, ICETIS 2021 ; Conference date: 25-06-2021 Through 26-06-2021",

year = "2022",

doi = "10.1007/978-3-030-85990-9_15",

language = "English",

isbn = "9783030859893",

volume = "2",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer",

pages = "165--174",

editor = "Mostafa Al-Emran and Al-Sharafi, {Mohammed A.} and Al-Kabi, {Mohammed N.} and Khaled Shaalan",

booktitle = "Proceedings of International Conference on Emerging Technologies and Intelligent Systems - ICETIS 2021",

url = "https://link.springer.com/book/10.1007/978-3-030-85990-9",

}

Mahdi, MN, Bakare, TA, Ahmad, AR, Buhari, AM & Mohamed, KS 2022, Scalable smartification of commercial buildings HVAC systems using the internet of Things and Machine Learning. in M Al-Emran, MA Al-Sharafi, MN Al-Kabi & K Shaalan (eds), Proceedings of International Conference on Emerging Technologies and Intelligent Systems - ICETIS 2021. vol. 2, Lecture Notes in Networks and Systems, vol. 322, Springer, Cham Switzerland, pp. 165-174, International Conference on Emerging Technologies and Intelligent Systems 2021, Al Buraimi, Oman, 25/06/21. https://doi.org/10.1007/978-3-030-85990-9_15

Scalable smartification of commercial buildings HVAC systems using the internet of Things and Machine Learning. / Mahdi, Mohammad Najah; Bakare, Taofiq Adeola; Ahmad, Abdul Rahim et al.
Proceedings of International Conference on Emerging Technologies and Intelligent Systems - ICETIS 2021. ed. / Mostafa Al-Emran; Mohammed A. Al-Sharafi; Mohammed N. Al-Kabi; Khaled Shaalan. Vol. 2 Cham Switzerland: Springer, 2022. p. 165-174 (Lecture Notes in Networks and Systems; Vol. 322).

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research

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T1 - Scalable smartification of commercial buildings HVAC systems using the internet of Things and Machine Learning

AU - Mahdi, Mohammad Najah

AU - Bakare, Taofiq Adeola

AU - Ahmad, Abdul Rahim

AU - Buhari, Adamu Muhammad

AU - Mohamed, Khalid Sheikhidris

N1 - Funding Information: This work was supported by the Universiti Tenaga Nasional (UNITEN) internal grant no RJO10517844/005 (2020). Many thanks to the Innovation & Research Management Center (iRMC), UNITEN who provided their assistance and expertise during the research. Funding Information: Acknowledgements This work was supported by the Universiti Tenaga Nasional (UNITEN) internal grant no RJO10517844/005 (2020). Many thanks to the Innovation & Research Management Center (iRMC), UNITEN who provided their assistance and expertise during the research. Publisher Copyright: © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

PY - 2022

Y1 - 2022

N2 - Most 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.

AB - Most 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.

KW - Artificial intelligence

KW - Energy consumption

KW - Heat ventilation and air conditioning

KW - Internet of energy

KW - Internet of things

KW - Machine learning

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U2 - 10.1007/978-3-030-85990-9_15

DO - 10.1007/978-3-030-85990-9_15

M3 - Conference Paper

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SN - 9783030859893

VL - 2

T3 - Lecture Notes in Networks and Systems

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EP - 174

BT - Proceedings of International Conference on Emerging Technologies and Intelligent Systems - ICETIS 2021

A2 - Al-Emran, Mostafa

A2 - Al-Sharafi, Mohammed A.

A2 - Al-Kabi, Mohammed N.

A2 - Shaalan, Khaled

PB - Springer

CY - Cham Switzerland

T2 - International Conference on Emerging Technologies and Intelligent Systems 2021

Y2 - 25 June 2021 through 26 June 2021

ER -

Mahdi MN, Bakare TA, Ahmad AR, Buhari AM, Mohamed KS. Scalable smartification of commercial buildings HVAC systems using the internet of Things and Machine Learning. In Al-Emran M, Al-Sharafi MA, Al-Kabi MN, Shaalan K, editors, Proceedings of International Conference on Emerging Technologies and Intelligent Systems - ICETIS 2021. Vol. 2. Cham Switzerland: Springer. 2022. p. 165-174. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-030-85990-9_15

Scalable smartification of commercial buildings HVAC systems using the internet of Things and Machine Learning

Abstract

Publication series

Conference

Keywords

UN SDGs

Access to Document

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Cite this