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Unmasking the causal relationships latent in the interplay between occupant’s actions and indoor ambience: A building energy management outlook

Author

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  • Pal, Monalisa
  • Alyafi, Amr Alzouhri
  • Ploix, Stéphane
  • Reignier, Patrick
  • Bandyopadhyay, Sanghamitra

Abstract

Assiduous attention to building energy management is essential as the building sector contributes hugely to global energy consumption. Zero cost human-based energy retrofit planning is a promising solution as it can be applied to the existing buildings. Aside from a few works on obtaining optimal retrofits, studies on the impact of occupants’ actions are scarce. The primary objective of this work is to demonstrate how the changes in occupants’ actions, towards Pareto-optimality, can be beneficial for a better indoor ambience and thereby, associating the users with the energy systems. The proposed approach considers the context, documented by sensors, of an office at Grenoble Institute of Technology, France. Utilizing this data, approximation-guided evolutionary multi-objective optimization algorithm (AGE-II) generates actions by minimizing thermal dissatisfaction, air quality dissatisfaction, heater energy consumption and interference with the daily routine. The optimal and historical plans are compared to reveal the causality of the energy systems that are influenced by occupant’s actions. These explanations develop awareness of the effect of changing occupants’ actions to an optimal plan by presenting to the occupants the reasons for such changes. Hence, the proposed work helps the occupants to perceive the impact of actions which allow them to adapt their habits to a more energy-efficient routine.

Suggested Citation

  • Pal, Monalisa & Alyafi, Amr Alzouhri & Ploix, Stéphane & Reignier, Patrick & Bandyopadhyay, Sanghamitra, 2019. "Unmasking the causal relationships latent in the interplay between occupant’s actions and indoor ambience: A building energy management outlook," Applied Energy, Elsevier, vol. 238(C), pages 1452-1470.
    • Handle: RePEc:eee:appene:v:238:y:2019:i:c:p:1452-1470
    DOI: 10.1016/j.apenergy.2019.01.118
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    References listed on IDEAS

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    1. Yang, Tao & Pan, Yiqun & Mao, Jiachen & Wang, Yonglong & Huang, Zhizhong, 2016. "An automated optimization method for calibrating building energy simulation models with measured data: Orientation and a case study," Applied Energy, Elsevier, vol. 179(C), pages 1220-1231.
    2. Cui, Borui & Fan, Cheng & Munk, Jeffrey & Mao, Ning & Xiao, Fu & Dong, Jin & Kuruganti, Teja, 2019. "A hybrid building thermal modeling approach for predicting temperatures in typical, detached, two-story houses," Applied Energy, Elsevier, vol. 236(C), pages 101-116.
    3. Ramakrishnan, Sayanthan & Wang, Xiaoming & Sanjayan, Jay & Wilson, John, 2017. "Thermal performance assessment of phase change material integrated cementitious composites in buildings: Experimental and numerical approach," Applied Energy, Elsevier, vol. 207(C), pages 654-664.
    4. Wang, Wei & Chen, Jiayu & Huang, Gongsheng & Lu, Yujie, 2017. "Energy efficient HVAC control for an IPS-enabled large space in commercial buildings through dynamic spatial occupancy distribution," Applied Energy, Elsevier, vol. 207(C), pages 305-323.
    5. Eguaras-Martínez, María & Vidaurre-Arbizu, Marina & Martín-Gómez, César, 2014. "Simulation and evaluation of Building Information Modeling in a real pilot site," Applied Energy, Elsevier, vol. 114(C), pages 475-484.
    6. Pisello, Anna Laura & Asdrubali, Francesco, 2014. "Human-based energy retrofits in residential buildings: A cost-effective alternative to traditional physical strategies," Applied Energy, Elsevier, vol. 133(C), pages 224-235.
    7. Nguyen, Anh-Tuan & Reiter, Sigrid & Rigo, Philippe, 2014. "A review on simulation-based optimization methods applied to building performance analysis," Applied Energy, Elsevier, vol. 113(C), pages 1043-1058.
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