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A modular optimisation model for reducing energy consumption in large scale building facilities

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Listed:
  • Petri, Ioan
  • Li, Haijiang
  • Rezgui, Yacine
  • Chunfeng, Yang
  • Yuce, Baris
  • Jayan, Bejay

Abstract

With the pressing regulatory requirement to increase energy efficiency in our built environment, significant researching efforts have been recently directed towards energy optimisation with the overall objective of reducing energy consumption. Energy simulation and optimisation identify a class of applications that demand high performance processing power in order to be realised within a feasible time-frame. The problem becomes increasingly complex when undertaking such energy simulation and optimisation in large scale buildings such as sport facilities where the generation of optimal set points can be timing inefficient.

Suggested Citation

  • Petri, Ioan & Li, Haijiang & Rezgui, Yacine & Chunfeng, Yang & Yuce, Baris & Jayan, Bejay, 2014. "A modular optimisation model for reducing energy consumption in large scale building facilities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 38(C), pages 990-1002.
    • Handle: RePEc:eee:rensus:v:38:y:2014:i:c:p:990-1002
    DOI: 10.1016/j.rser.2014.07.044
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    References listed on IDEAS

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    1. anonymous, 2001. "Energy costs show through to consumers," Western economic developments, Federal Reserve Bank of San Francisco, issue Mar, pages 1-3.
    2. Saidur, R., 2009. "Energy consumption, energy savings, and emission analysis in Malaysian office buildings," Energy Policy, Elsevier, vol. 37(10), pages 4104-4113, October.
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    Cited by:

    1. Dasheng Lee & Fu-Po Tsai, 2020. "Air Conditioning Energy Saving from Cloud-Based Artificial Intelligence: Case Study of a Split-Type Air Conditioner," Energies, MDPI, vol. 13(8), pages 1-25, April.
    2. Mawson, Victoria Jayne & Hughes, Ben Richard, 2021. "Optimisation of HVAC control and manufacturing schedules for the reduction of peak energy demand in the manufacturing sector," Energy, Elsevier, vol. 227(C).
    3. Deb, Chirag & Zhang, Fan & Yang, Junjing & Lee, Siew Eang & Shah, Kwok Wei, 2017. "A review on time series forecasting techniques for building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 902-924.
    4. Elnour, Mariam & Fadli, Fodil & Himeur, Yassine & Petri, Ioan & Rezgui, Yacine & Meskin, Nader & Ahmad, Ahmad M., 2022. "Performance and energy optimization of building automation and management systems: Towards smart sustainable carbon-neutral sports facilities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    5. Ali Alhamami & Ioan Petri & Yacine Rezgui & Sylvain Kubicki, 2020. "Promoting Energy Efficiency in the Built Environment through Adapted BIM Training and Education," Energies, MDPI, vol. 13(9), pages 1-24, May.
    6. Casals, Miquel & Gangolells, Marta & Forcada, Núria & Macarulla, Marcel & Giretti, Alberto & Vaccarini, Massimo, 2016. "SEAM4US: An intelligent energy management system for underground stations," Applied Energy, Elsevier, vol. 166(C), pages 150-164.
    7. Shi, Xing & Tian, Zhichao & Chen, Wenqiang & Si, Binghui & Jin, Xing, 2016. "A review on building energy efficient design optimization rom the perspective of architects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 872-884.
    8. Ioan Petri & Sylvain Kubicki & Yacine Rezgui & Annie Guerriero & Haijiang Li, 2017. "Optimizing Energy Efficiency in Operating Built Environment Assets through Building Information Modeling: A Case Study," Energies, MDPI, vol. 10(8), pages 1-17, August.
    9. Stamatis Chrysikopoulos & Panos Chountalas, 2018. "Integrating energy and environmental management systems to enable facilities to qualify for carbon funds," Energy & Environment, , vol. 29(6), pages 938-956, September.
    10. Salata, Ferdinando & Ciancio, Virgilio & Dell'Olmo, Jacopo & Golasi, Iacopo & Palusci, Olga & Coppi, Massimo, 2020. "Effects of local conditions on the multi-variable and multi-objective energy optimization of residential buildings using genetic algorithms," Applied Energy, Elsevier, vol. 260(C).

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