Multi-agent system for energy consumption optimisation in higher education institutions

Ahmad Al-Daraiseh; Eyas El-Qawasmeh; Nazaraf Shah

doi:10.1016/j.jcss.2014.12.010

Multi-agent system for energy consumption optimisation in higher education institutions

Ahmad Al-Daraiseh, Eyas El-Qawasmeh, Nazaraf Shah

Research output: Contribution to journal › Article › peer-review

26 Citations (Scopus)

Abstract

Global warming is one of the most serious issues faced by today’s world. The increase in world population and adoption of modern lifestyle have dramatically increased the demand for energy. Over the last decade Higher Educational Institution (HEI) buildings have seen massive increase in energy consumption due to increased use of IT equipments, longer occupancy and increased use of Heating Ventilation and Air Conditioning (HVAC) systems. Current Building Management Systems (BMS) fail to optimize energy consumption of HVAC systems in commercial and educational buildings. In this paper we present an intelligent agent based system to optimize energy consumption of HVAC system in HEI buildings. The system employs artificial intelligence techniques to predict the demand of the system and optimize energy consumption of the HVAC system. The experimental results have shown that the deployment of the system has resulted in 3% reduction in energy consumption of HVAC.

Original language	English
Pages (from-to)	958–965
Journal	Journal of Computer and System Sciences
Volume	81
Issue number	6
DOIs	https://doi.org/10.1016/j.jcss.2014.12.010
Publication status	Published - 2015

Bibliographical note

This article is not yet available on the repository. The article is in press. Full citation details will be given when the article has been published

Keywords

Energy management
Energy optimisation
Energy conservation
Sensor network
HVAC control
Energy efficiency

UN SDGs

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

Access to Document

10.1016/j.jcss.2014.12.010

Cite this

@article{b823a94cbdc64137a47312286907a308,

title = "Multi-agent system for energy consumption optimisation in higher education institutions",

abstract = "Global warming is one of the most serious issues faced by today{\textquoteright}s world. The increase in world population and adoption of modern lifestyle have dramatically increased the demand for energy. Over the last decade Higher Educational Institution (HEI) buildings have seen massive increase in energy consumption due to increased use of IT equipments, longer occupancy and increased use of Heating Ventilation and Air Conditioning (HVAC) systems. Current Building Management Systems (BMS) fail to optimize energy consumption of HVAC systems in commercial and educational buildings. In this paper we present an intelligent agent based system to optimize energy consumption of HVAC system in HEI buildings. The system employs artificial intelligence techniques to predict the demand of the system and optimize energy consumption of the HVAC system. The experimental results have shown that the deployment of the system has resulted in 3\% reduction in energy consumption of HVAC.",

keywords = "Energy management, Energy optimisation, Energy conservation, Sensor network, HVAC control, Energy efficiency",

author = "Ahmad Al-Daraiseh and Eyas El-Qawasmeh and Nazaraf Shah",

note = "This article is not yet available on the repository. The article is in press. Full citation details will be given when the article has been published",

year = "2015",

doi = "10.1016/j.jcss.2014.12.010",

language = "English",

volume = "81",

pages = "958–965",

journal = "Journal of Computer and System Sciences",

issn = "0022-0000",

publisher = "Elsevier",

number = "6",

}

TY - JOUR

T1 - Multi-agent system for energy consumption optimisation in higher education institutions

AU - Al-Daraiseh, Ahmad

AU - El-Qawasmeh, Eyas

AU - Shah, Nazaraf

N1 - This article is not yet available on the repository. The article is in press. Full citation details will be given when the article has been published

PY - 2015

Y1 - 2015

N2 - Global warming is one of the most serious issues faced by today’s world. The increase in world population and adoption of modern lifestyle have dramatically increased the demand for energy. Over the last decade Higher Educational Institution (HEI) buildings have seen massive increase in energy consumption due to increased use of IT equipments, longer occupancy and increased use of Heating Ventilation and Air Conditioning (HVAC) systems. Current Building Management Systems (BMS) fail to optimize energy consumption of HVAC systems in commercial and educational buildings. In this paper we present an intelligent agent based system to optimize energy consumption of HVAC system in HEI buildings. The system employs artificial intelligence techniques to predict the demand of the system and optimize energy consumption of the HVAC system. The experimental results have shown that the deployment of the system has resulted in 3% reduction in energy consumption of HVAC.

AB - Global warming is one of the most serious issues faced by today’s world. The increase in world population and adoption of modern lifestyle have dramatically increased the demand for energy. Over the last decade Higher Educational Institution (HEI) buildings have seen massive increase in energy consumption due to increased use of IT equipments, longer occupancy and increased use of Heating Ventilation and Air Conditioning (HVAC) systems. Current Building Management Systems (BMS) fail to optimize energy consumption of HVAC systems in commercial and educational buildings. In this paper we present an intelligent agent based system to optimize energy consumption of HVAC system in HEI buildings. The system employs artificial intelligence techniques to predict the demand of the system and optimize energy consumption of the HVAC system. The experimental results have shown that the deployment of the system has resulted in 3% reduction in energy consumption of HVAC.

KW - Energy management

KW - Energy optimisation

KW - Energy conservation

KW - Sensor network

KW - HVAC control

KW - Energy efficiency

UR - https://www.scopus.com/pages/publications/84928286254

U2 - 10.1016/j.jcss.2014.12.010

DO - 10.1016/j.jcss.2014.12.010

M3 - Article

SN - 0022-0000

SN - 1090-2724

VL - 81

SP - 958

EP - 965

JO - Journal of Computer and System Sciences

JF - Journal of Computer and System Sciences

IS - 6

ER -

Multi-agent system for energy consumption optimisation in higher education institutions

Abstract

Bibliographical note

Keywords

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this