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Intelligent Energy Management Strategy for Automated Office Buildings

Author

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  • Simplice Igor Noubissie Tientcheu

    (Department of Electrical Engineering, Tshwane University of Technology, Pretoria 0001, South Africa)

  • Shyama P. Chowdhury

    (Department of Electrical Engineering, Tshwane University of Technology, Pretoria 0001, South Africa)

  • Thomas O. Olwal

    (Department of Electrical Engineering, Tshwane University of Technology, Pretoria 0001, South Africa)

Abstract

The increasing demand to reduce the high consumption of end-use energy in office buildings framed the objective of this work, which was to design an intelligent system management that could be utilized to minimize office buildings’ energy consumption from the national electricity grid. Heating, Ventilation and Air Conditioning (HVAC) and lighting are the two main consumers of electricity in office buildings. Advanced automation and control systems for buildings and their components have been developed by researchers to achieve low energy consumption in office buildings without considering integrating the load consumed and the Photovoltaic system (PV) input to the controller. This study investigated the use of PV to power the HVAC and lighting equipped with a suitable control strategy to improve energy saving within a building, especially in office buildings where there are reports of high misuse of electricity. The intelligent system was modelled using occupant activities, weather condition changes, load consumed and PV energy changes, as input to the control system of lighting and HVAC. The model was verified and tested using specialized simulation tools (Simulink ® ) and was subsequently used to investigate the impact of an integrated system on energy consumption, based on three scenarios. In addition, the direct impact on reduced energy cost was also analysed. The first scenario was tested in simulation of four offices building in a civil building in South Africa of a single occupant’s activities, weather conditions, temperature and the simulation resulted in savings of HVAC energy and lighting energy of 13% and 29%, respectively. In the second scenario, the four offices were tested in simulation due to the loads’ management plus temperature and occupancy and it resulted in a saving of 20% of HVAC energy and 29% of lighting electrical energy. The third scenario, which tested integrating PV energy (thus, the approach utilized) with the above-mentioned scenarios, resulted in, respectively, 64% and 73% of HVAC energy and lighting electrical energy saved. This saving was greater than that of the first two scenarios. The results of the system developed demonstrated that the loads’ control and the PV integration combined with the occupancy, weather and temperature control, could lead to a significant saving of energy within office buildings.

Suggested Citation

  • Simplice Igor Noubissie Tientcheu & Shyama P. Chowdhury & Thomas O. Olwal, 2019. "Intelligent Energy Management Strategy for Automated Office Buildings," Energies, MDPI, vol. 12(22), pages 1-27, November.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:22:p:4326-:d:286468
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    References listed on IDEAS

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    1. Yu, Xu & Su, Yuehong, 2015. "Daylight availability assessment and its potential energy saving estimation –A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 494-503.
    2. Chen, Xi & Yang, Hongxing & Lu, Lin, 2015. "A comprehensive review on passive design approaches in green building rating tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1425-1436.
    3. Marinakis, Vangelis & Doukas, Haris & Karakosta, Charikleia & Psarras, John, 2013. "An integrated system for buildings’ energy-efficient automation: Application in the tertiary sector," Applied Energy, Elsevier, vol. 101(C), pages 6-14.
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    Cited by:

    1. Yoko E. Fukumura & Julie McLaughlin Gray & Gale M. Lucas & Burcin Becerik-Gerber & Shawn C. Roll, 2021. "Worker Perspectives on Incorporating Artificial Intelligence into Office Workspaces: Implications for the Future of Office Work," IJERPH, MDPI, vol. 18(4), pages 1-15, February.
    2. Gleydson de Oliveira Cavalcanti & Handson Claudio Dias Pimenta, 2023. "Electric Energy Management in Buildings Based on the Internet of Things: A Systematic Review," Energies, MDPI, vol. 16(15), pages 1-29, August.
    3. Qadeer Ali & Muhammad Jamaluddin Thaheem & Fahim Ullah & Samad M. E. Sepasgozar, 2020. "The Performance Gap in Energy-Efficient Office Buildings: How the Occupants Can Help?," Energies, MDPI, vol. 13(6), pages 1-27, March.
    4. Wanida Saetang & Supaporn Chai-Arayalert & Siriwan Kajornkasirat & Jinda Kongcharoen & Aekarat Saeliw & Kritsada Puangsuwan & Supattra Puttinaovarat, 2024. "Eco-Friendly Office Platform: Leveraging Machine Learning and GIS for Carbon Footprint Management and Green Space Analysis," Sustainability, MDPI, vol. 16(21), pages 1-20, October.
    5. Dongsu Kim & Jongman Lee & Sunglok Do & Pedro J. Mago & Kwang Ho Lee & Heejin Cho, 2022. "Energy Modeling and Model Predictive Control for HVAC in Buildings: A Review of Current Research Trends," Energies, MDPI, vol. 15(19), pages 1-30, October.

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