IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v211y2018icp1308-1323.html
   My bibliography  Save this article

Assessment of existing buildings performance using system dynamics technique

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261917315532
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2017.10.111?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    5. Chung, William, 2011. "Review of building energy-use performance benchmarking methodologies," Applied Energy, Elsevier, vol. 88(5), pages 1470-1479, May.
    6. 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.
    7. Jay W. Forrester, 1968. "Industrial Dynamics--After the First Decade," Management Science, INFORMS, vol. 14(7), pages 398-415, March.
    8. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. Qicong Cai & Baizhan Li & Wenbo He & Miao Guo, 2024. "Energy Consumption Calculation of Civil Buildings in Regional Integrated Energy Systems: A Review of Characteristics, Methods and Application Prospects," Sustainability, MDPI, vol. 16(13), pages 1-25, July.
    3. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Capozzoli, Alfonso & Piscitelli, Marco Savino & Neri, Francesco & Grassi, Daniele & Serale, Gianluca, 2016. "A novel methodology for energy performance benchmarking of buildings by means of Linear Mixed Effect Model: The case of space and DHW heating of out-patient Healthcare Centres," Applied Energy, Elsevier, vol. 171(C), pages 592-607.
    2. Vaisi, Salah & Varmazyari, Pouya & Esfandiari, Masoud & Sharbaf, Sara A., 2023. "Developing a multi-level energy benchmarking and certification system for office buildings in a cold climate region," Applied Energy, Elsevier, vol. 336(C).
    3. Geraldi, Matheus Soares & Ghisi, Enedir, 2022. "Data-driven framework towards realistic bottom-up energy benchmarking using an Artificial Neural Network," Applied Energy, Elsevier, vol. 306(PA).
    4. Zhou, Yuren & Lork, Clement & Li, Wen-Tai & Yuen, Chau & Keow, Yeong Ming, 2019. "Benchmarking air-conditioning energy performance of residential rooms based on regression and clustering techniques," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    5. Ciulla, Giuseppina & Lo Brano, Valerio & D’Amico, Antonino, 2016. "Modelling relationship among energy demand, climate and office building features: A cluster analysis at European level," Applied Energy, Elsevier, vol. 183(C), pages 1021-1034.
    6. 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.
    7. Li, Zhengwei & Han, Yanmin & Xu, Peng, 2014. "Methods for benchmarking building energy consumption against its past or intended performance: An overview," Applied Energy, Elsevier, vol. 124(C), pages 325-334.
    8. Jeong, Cheoljoon & Byon, Eunshin, 2024. "Calibration of building energy computer models via bias-corrected iteratively reweighted least squares method," Applied Energy, Elsevier, vol. 360(C).
    9. Raza, Muhammad Qamar & Nadarajah, Mithulananthan & Ekanayake, Chandima, 2017. "Demand forecast of PV integrated bioclimatic buildings using ensemble framework," Applied Energy, Elsevier, vol. 208(C), pages 1626-1638.
    10. Yang, Liu & Yan, Haiyan & Lam, Joseph C., 2014. "Thermal comfort and building energy consumption implications – A review," Applied Energy, Elsevier, vol. 115(C), pages 164-173.
    11. Khayatian, Fazel & Sarto, Luca & Dall'O', Giuliano, 2017. "Building energy retrofit index for policy making and decision support at regional and national scales," Applied Energy, Elsevier, vol. 206(C), pages 1062-1075.
    12. Park, Hyo Seon & Lee, Minhyun & Kang, Hyuna & Hong, Taehoon & Jeong, Jaewook, 2016. "Development of a new energy benchmark for improving the operational rating system of office buildings using various data-mining techniques," Applied Energy, Elsevier, vol. 173(C), pages 225-237.
    13. Tomasz Jałowiec & Henryk Wojtaszek, 2021. "Analysis of the RES Potential in Accordance with the Energy Policy of the European Union," Energies, MDPI, vol. 14(19), pages 1-33, September.
    14. Lu, Yakai & Tian, Zhe & Zhou, Ruoyu & Liu, Wenjing, 2021. "A general transfer learning-based framework for thermal load prediction in regional energy system," Energy, Elsevier, vol. 217(C).
    15. Masechaba Nthunya & Nien-Tsu Tuan & Corrinne Shaw & Ian Jay, 2017. "A Systemic Exploration of Lesotho’s Basic Education through Interactive Management," Systemic Practice and Action Research, Springer, vol. 30(3), pages 257-276, June.
    16. Nutkiewicz, Alex & Yang, Zheng & Jain, Rishee K., 2018. "Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow," Applied Energy, Elsevier, vol. 225(C), pages 1176-1189.
    17. Tong, Zheming & Chen, Yujiao & Malkawi, Ali & Liu, Zhu & Freeman, Richard B., 2016. "Energy saving potential of natural ventilation in China: The impact of ambient air pollution," Applied Energy, Elsevier, vol. 179(C), pages 660-668.
    18. Severinsen, A. & Myrland, Ø., 2022. "ShinyRBase: Near real-time energy saving models using reactive programming," Applied Energy, Elsevier, vol. 325(C).
    19. Sahar AlMashaqbeh & Jose Eduardo Munive-Hernandez, 2023. "Risk Analysis under a Circular Economy Context Using a Systems Thinking Approach," Sustainability, MDPI, vol. 15(5), pages 1-17, February.
    20. Àlex Alonso & Jordi de la Hoz & Helena Martín & Sergio Coronas & Pep Salas & José Matas, 2020. "A Comprehensive Model for the Design of a Microgrid under Regulatory Constraints Using Synthetical Data Generation and Stochastic Optimization," Energies, MDPI, vol. 13(21), pages 1-26, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:211:y:2018:i:c:p:1308-1323. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.