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Objective Building Energy Performance Benchmarking Using Data Envelopment Analysis and Monte Carlo Sampling

Author

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  • Seong-Hwan Yoon

    (Convergence Laboratory, KT Institute of Convergence Technology, Seoul 06763, Korea)

  • Cheol-Soo Park

    (School of Civil, Architectural Engineering and Landscape Architecture, Sungkyunkwan University, Suwon 16419, Gyeonggi, Korea)

Abstract

An objective measure of building energy performance is crucial for performance assessment and rational decision making on energy retrofits and policies of existing buildings. One of the most popular measures of building energy performance benchmarking is Energy Use Intensity (EUI, kwh/m 2 ). While EUI is simple to understand, it only represents the amount of consumed energy per unit floor area rather than the real performance of a building. In other words, it cannot take into account building services such as operation hours, comfortable environment, etc. EUI is often misinterpreted by assuming that a lower EUI for a building implies better energy performance, which may not actually be the case if many of the building services are not considered. In order to overcome this limitation, this paper presents Data Envelopment Analysis (DEA) coupled with Monte Carlo sampling. DEA is a data-driven and non-parametric performance measurement method. DEA can quantify the performance of a given building given multiple inputs and multiple outputs. In this study, two existing office buildings were selected. For energy performance benchmarking, 1000 virtual peer buildings were generated from a Monte Carlo sampling and then simulated using EnergyPlus. Based on a comparison between DEA-based and EUI-based benchmarking, it is shown that DEA is more performance-oriented, objective, and rational since DEA can take into account input (energy used to provide the services used in a building) and output (level of services provided by a building). It is shown that DEA can be an objective building energy benchmarking method, and can be used to identify low energy performance buildings.

Suggested Citation

  • Seong-Hwan Yoon & Cheol-Soo Park, 2017. "Objective Building Energy Performance Benchmarking Using Data Envelopment Analysis and Monte Carlo Sampling," Sustainability, MDPI, vol. 9(5), pages 1-12, May.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:5:p:780-:d:98046
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    References listed on IDEAS

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    1. Ki Uhn Ahn & Deuk-Woo Kim & Seung-Eon Lee & Chang-U Chae & Hyun Mi Cho, 2022. "Temporal Segmentation for the Estimation and Benchmarking of Heating and Cooling Energy in Commercial Buildings in Seoul, South Korea," Sustainability, MDPI, vol. 14(17), pages 1-14, September.
    2. Arjunan, Pandarasamy & Poolla, Kameshwar & Miller, Clayton, 2020. "EnergyStar++: Towards more accurate and explanatory building energy benchmarking," Applied Energy, Elsevier, vol. 276(C).
    3. Martínez-de-Alegría, Itziar & Río, Rosa-María & Zarrabeitia, Enara & Álvarez, Izaskun, 2021. "Heating demand as an energy performance indicator: A case study of buildings built under the passive house standard in Spain," Energy Policy, Elsevier, vol. 159(C).
    4. Cosme Segador-Vegas & Justo García-Sanz-Calcedo & Daniel Encinas-Martín, 2018. "Determination of the Energy Behaviour in Municipalities with Fewer than 6000 Inhabitants in Badajoz (Spain)," Energies, MDPI, vol. 11(9), pages 1-16, August.

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