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Probabilistic energy consumption analysis in buildings using point estimate method

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  • Bordbari, Mohammad Javad
  • Seifi, Ali Reza
  • Rastegar, Mohammad

Abstract

This paper analyzes the energy consumption of buildings considering the uncertainty of structural and environmental parameters. To this end, two-point estimate method (2PEM) is used to model the uncertainties. EnergyPlus software is used in this paper to evaluate the energy consumption, the thermal comfort, and the energy cost of a building. We examine the proposed method in a retail building to show the effectiveness of the method in comparison with the Monte-Carlo Simulation and deterministic methods. The results show that the 2PEM method although may cause a bit accuracy loss, it can considerably reduce the simulation time by more than 97%. In addition, a sensitivity analysis is effectuated in this paper to investigate the impacts of different climate zones on the results of energy consumption analysis.

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  • Bordbari, Mohammad Javad & Seifi, Ali Reza & Rastegar, Mohammad, 2018. "Probabilistic energy consumption analysis in buildings using point estimate method," Energy, Elsevier, vol. 142(C), pages 716-722.
    • Handle: RePEc:eee:energy:v:142:y:2018:i:c:p:716-722
    DOI: 10.1016/j.energy.2017.10.091
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    1. Aien, Morteza & Hajebrahimi, Ali & Fotuhi-Firuzabad, Mahmud, 2016. "A comprehensive review on uncertainty modeling techniques in power system studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 1077-1089.
    2. Zhao, Deyin & Zhong, Ming & Zhang, Xu & Su, Xing, 2016. "Energy consumption predicting model of VRV (Variable refrigerant volume) system in office buildings based on data mining," Energy, Elsevier, vol. 102(C), pages 660-668.
    3. Zhao, Hai-xiang & Magoulès, Frédéric, 2012. "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3586-3592.
    4. Dirks, James A. & Gorrissen, Willy J. & Hathaway, John H. & Skorski, Daniel C. & Scott, Michael J. & Pulsipher, Trenton C. & Huang, Maoyi & Liu, Ying & Rice, Jennie S., 2015. "Impacts of climate change on energy consumption and peak demand in buildings: A detailed regional approach," Energy, Elsevier, vol. 79(C), pages 20-32.
    5. Hsu, David, 2015. "Identifying key variables and interactions in statistical models of building energy consumption using regularization," Energy, Elsevier, vol. 83(C), pages 144-155.
    6. Liu, Long & Zhao, Jing & Liu, Xin & Wang, Zhaoxia, 2014. "Energy consumption comparison analysis of high energy efficiency office buildings in typical climate zones of China and U.S. based on correction model," Energy, Elsevier, vol. 65(C), pages 221-232.
    7. Huang, Jianhua & Gurney, Kevin Robert, 2016. "The variation of climate change impact on building energy consumption to building type and spatiotemporal scale," Energy, Elsevier, vol. 111(C), pages 137-153.
    8. Malekpour, Ahmad Reza & Niknam, Taher, 2011. "A probabilistic multi-objective daily Volt/Var control at distribution networks including renewable energy sources," Energy, Elsevier, vol. 36(5), pages 3477-3488.
    9. Li, Qiong & Meng, Qinglin & Cai, Jiejin & Yoshino, Hiroshi & Mochida, Akashi, 2009. "Applying support vector machine to predict hourly cooling load in the building," Applied Energy, Elsevier, vol. 86(10), pages 2249-2256, October.
    10. Naji, Sareh & Keivani, Afram & Shamshirband, Shahaboddin & Alengaram, U. Johnson & Jumaat, Mohd Zamin & Mansor, Zulkefli & Lee, Malrey, 2016. "Estimating building energy consumption using extreme learning machine method," Energy, Elsevier, vol. 97(C), pages 506-516.
    11. Biswas, M.A. Rafe & Robinson, Melvin D. & Fumo, Nelson, 2016. "Prediction of residential building energy consumption: A neural network approach," Energy, Elsevier, vol. 117(P1), pages 84-92.
    12. Kipping, A. & Trømborg, E., 2017. "Modeling hourly consumption of electricity and district heat in non-residential buildings," Energy, Elsevier, vol. 123(C), pages 473-486.
    13. Soteris A. Kalogirou, 2006. "Artificial neural networks in energy applications in buildings," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 1(3), pages 201-216, July.
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