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Optimisation of HVAC control and manufacturing schedules for the reduction of peak energy demand in the manufacturing sector

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  • Mawson, Victoria Jayne
  • Hughes, Ben Richard

Abstract

Manufacturing companies are subjected to peak-load-dependent energy prices/tariffs, and are faced with high costs for the peak consumption utilised. Complex interactions between HVAC control management, manufacturing schedules, required facility conditions and thermal energy flows in and around the building are difficult to analyse, thus little knowledge exists regarding the interactions between machine, building and HVAC level thermal energy flows. This study utilised simulation for the analysis of thermal energy flows in and around a manufacturing facility, with machine learning adopted for the prediction of spikes in energy consumption based upon weather conditions, occupancy and manufacturing schedules, thus identifying potential energy inefficiencies.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:227:y:2021:i:c:s036054422100685x
    DOI: 10.1016/j.energy.2021.120436
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    References listed on IDEAS

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    Cited by:

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    2. Andre Dionisio Rocha & Nelson Freitas & Duarte Alemão & Magno Guedes & Renato Martins & José Barata, 2021. "Event-Driven Interoperable Manufacturing Ecosystem for Energy Consumption Monitoring," Energies, MDPI, vol. 14(12), pages 1-19, June.
    3. Wang, Kung-Jeng & Lin, Chiuhsiang Joe & Dagne, Teshome Bekele & Woldegiorgis, Bereket Haile, 2022. "Bilayer stochastic optimization model for smart energy conservation systems," Energy, Elsevier, vol. 247(C).
    4. Fábio de Oliveira Neves & Henrique Ewbank & José Arnaldo Frutuoso Roveda & Andrea Trianni & Fernando Pinhabel Marafão & Sandra Regina Monteiro Masalskiene Roveda, 2022. "Economic and Production-Related Implications for Industrial Energy Efficiency: A Logistic Regression Analysis on Cross-Cutting Technologies," Energies, MDPI, vol. 15(4), pages 1-19, February.
    5. Hu, Changshuai & Du, Dan & Huang, Junbing, 2023. "The driving effect of energy demand evolution: From the perspective of heterogeneity in technology," Energy, Elsevier, vol. 275(C).

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