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An evaluation of robust controls for passive building thermal mass and mechanical thermal energy storage under uncertainty

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  • Kim, Sean Hay

Abstract

Passive building thermal mass and mechanical thermal energy storage (TES) are known as one of state-of-the-art demand-side control instruments. Specifically, Model-based Predictive Control (MPC) for this operation has the potential to significantly increase performance and bring economic advantages. However, due to the uncertainty in certain operating conditions in the field, its control effectiveness could be diminished and/or seriously damaged, which results in poor performance.

Suggested Citation

  • Kim, Sean Hay, 2013. "An evaluation of robust controls for passive building thermal mass and mechanical thermal energy storage under uncertainty," Applied Energy, Elsevier, vol. 111(C), pages 602-623.
    • Handle: RePEc:eee:appene:v:111:y:2013:i:c:p:602-623
    DOI: 10.1016/j.apenergy.2013.05.030
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    1. Unknown, 2008. "Front Matter," Economics of Agriculture, Institute of Agricultural Economics, vol. 55(1), pages 1-5.
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    Cited by:

    1. Annelies Vandermeulen & Ina De Jaeger & Tijs Van Oevelen & Dirk Saelens & Lieve Helsen, 2020. "Analysis of Building Parameter Uncertainty in District Heating for Optimal Control of Network Flexibility," Energies, MDPI, vol. 13(23), pages 1-25, November.
    2. Jiahui Ying & Jian Yao & Rongyue Zheng, 2024. "Robustness Indicators for the Impact of Occupant Behavior Uncertainty on Building Energy Consumption," Energies, MDPI, vol. 17(18), pages 1-26, September.
    3. Hu, Mengqi, 2015. "A data-driven feed-forward decision framework for building clusters operation under uncertainty," Applied Energy, Elsevier, vol. 141(C), pages 229-237.
    4. Kotireddy, Rajesh & Hoes, Pieter-Jan & Hensen, Jan L.M., 2018. "A methodology for performance robustness assessment of low-energy buildings using scenario analysis," Applied Energy, Elsevier, vol. 212(C), pages 428-442.
    5. Tang, Hong & Wang, Shengwei & Li, Hangxin, 2021. "Flexibility categorization, sources, capabilities and technologies for energy-flexible and grid-responsive buildings: State-of-the-art and future perspective," Energy, Elsevier, vol. 219(C).
    6. Cotana, Franco & Rossi, Federico & Filipponi, Mirko & Coccia, Valentina & Pisello, Anna Laura & Bonamente, Emanuele & Petrozzi, Alessandro & Cavalaglio, Gianluca, 2014. "Albedo control as an effective strategy to tackle Global Warming: A case study," Applied Energy, Elsevier, vol. 130(C), pages 641-647.
    7. Michailidis, Iakovos T. & Schild, Thomas & Sangi, Roozbeh & Michailidis, Panagiotis & Korkas, Christos & Fütterer, Johannes & Müller, Dirk & Kosmatopoulos, Elias B., 2018. "Energy-efficient HVAC management using cooperative, self-trained, control agents: A real-life German building case study," Applied Energy, Elsevier, vol. 211(C), pages 113-125.
    8. Turner, W.J.N. & Walker, I.S. & Roux, J., 2015. "Peak load reductions: Electric load shifting with mechanical pre-cooling of residential buildings with low thermal mass," Energy, Elsevier, vol. 82(C), pages 1057-1067.
    9. Fiorentini, Massimo & Wall, Josh & Ma, Zhenjun & Braslavsky, Julio H. & Cooper, Paul, 2017. "Hybrid model predictive control of a residential HVAC system with on-site thermal energy generation and storage," Applied Energy, Elsevier, vol. 187(C), pages 465-479.

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