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Assessment of existing buildings performance using system dynamics technique

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  • Marzouk, Mohamed
  • Seleem, Noreihan

Abstract

Buildings’ performance is a vital aspect of organizational activity which is affected by maintenance procedures and policies. Therefore, facility maintenance plays a crucial role in informing strategic decision making. This research proposes the use of key performance indicators (KPIs) to dynamically model and simulate the performance of the existing facilities. Further, it transforms the descriptive analytics into predictive and prescriptive analytics by assessing the robustness of plans and policies set and by predicting future outcomes, through analyzing of multiple scenarios. Moreover, the research highlights the effect of energy consumption on the operating costs, and how it affects the building performance. For instance, when decreasing energy consumption by 58%, the operating costs can decrease to 55%, taking into account that the operating costs include not only that of energy, but also the cost of materials consumed. Hence, the building performance should increase unless the deferred maintenance has not been completed yet. This paper is part of a research that aims at using System dynamics modeling to quantify the interrelationship and interdependence of KPIs which studies the performance of existing building from three aspects financially, physically and functionally, as it is potentially effective in analyzing how maintenance expenditures can be optimized to maintain the desired level of building performance as demonstrated by several simulation scenarios. All parameters and variables used are quantified in terms of cost and introduced in a dynamic model where the functionality of the KPIs is dependent on the availability of data from the buildings in use. The proposed approach helps facility management professionals not only in tracking the indicators, but also in quantifying and studying the correlation between them based on available information. This leads to enhancing facility management decisions, with measurable facility performance outcomes.

Suggested Citation

  • Marzouk, Mohamed & Seleem, Noreihan, 2018. "Assessment of existing buildings performance using system dynamics technique," Applied Energy, Elsevier, vol. 211(C), pages 1308-1323.
    • Handle: RePEc:eee:appene:v:211:y:2018:i:c:p:1308-1323
    DOI: 10.1016/j.apenergy.2017.10.111
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    References listed on IDEAS

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    1. Sarel Lavy & John A. Garcia & Phil Scinto & Manish K. Dixit, 2014. "Key performance indicators for facility performance assessment: simulation of core indicators," Construction Management and Economics, Taylor & Francis Journals, vol. 32(12), pages 1183-1204, December.
    2. Lü, Xiaoshu & Lu, Tao & Kibert, Charles J. & Viljanen, Martti, 2015. "Modeling and forecasting energy consumption for heterogeneous buildings using a physical–statistical approach," Applied Energy, Elsevier, vol. 144(C), pages 261-275.
    3. Cui, Can & Wu, Teresa & Hu, Mengqi & Weir, Jeffery D. & Li, Xiwang, 2016. "Short-term building energy model recommendation system: A meta-learning approach," Applied Energy, Elsevier, vol. 172(C), pages 251-263.
    4. Hunt, J.C.R. & Timoshkina, Y. & Baudains, P. J. & Bishop, S.R., 2012. "System Dynamics Applied to Operations and Policy Decisions," European Review, Cambridge University Press, vol. 20(3), pages 324-342, May.
    5. Jay W. Forrester, 1968. "Industrial Dynamics--After the First Decade," Management Science, INFORMS, vol. 14(7), pages 398-415, March.
    6. Koo, Choongwan & Hong, Taehoon, 2015. "Development of a dynamic operational rating system in energy performance certificates for existing buildings: Geostatistical approach and data-mining technique," Applied Energy, Elsevier, vol. 154(C), pages 254-270.
    7. Manfren, Massimiliano & Aste, Niccolò & Moshksar, Reza, 2013. "Calibration and uncertainty analysis for computer models – A meta-model based approach for integrated building energy simulation," Applied Energy, Elsevier, vol. 103(C), pages 627-641.
    8. Chung, William, 2011. "Review of building energy-use performance benchmarking methodologies," Applied Energy, Elsevier, vol. 88(5), pages 1470-1479, May.
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    Cited by:

    1. José Sánchez Ramos & MCarmen Guerrero Delgado & Servando Álvarez Domínguez & José Luis Molina Félix & Francisco José Sánchez de la Flor & José Antonio Tenorio Ríos, 2019. "Systematic Simplified Simulation Methodology for Deep Energy Retrofitting Towards Nze Targets Using Life Cycle Energy Assessment," Energies, MDPI, vol. 12(16), pages 1-27, August.
    2. Yongli Wang & Shanshan Song & Mingchen Gao & Jingyan Wang & Jinrong Zhu & Zhongfu Tan, 2020. "Accounting for the Life Cycle Cost of Power Grid Projects by Employing a System Dynamics Technique: A Power Reform Perspective," Sustainability, MDPI, vol. 12(8), pages 1-28, April.

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