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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

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  • Capozzoli, Alfonso
  • Piscitelli, Marco Savino
  • Neri, Francesco
  • Grassi, Daniele
  • Serale, Gianluca

Abstract

The current EU energy efficiency directive 2012/27/EU defines the existing building stocks as one of the most promising potential sector for achieving energy saving. Robust methodologies aimed to quantify the potential reduction of energy consumption for large building stocks need to be developed. To this purpose, a benchmarking analysis is necessary in order to support public planners in determining how well a building is performing, in setting credible targets for improving performance or in detecting abnormal energy consumption. In the present work, a novel methodology is proposed to perform a benchmarking analysis particularly suitable for heterogeneous samples of buildings. The methodology is based on the estimation of a statistical model for energy consumption – the Linear Mixed Effects Model –, so as to account for both the fixed effects shared by all individuals within a dataset and the random effects related to particular groups/classes of individuals in the population. The groups of individuals within the population have been classified by resorting to a supervised learning technique. Under this backdrop, a Monte Carlo simulation is worked out to compute the frequency distribution of annual energy consumption and identify a reference value for each group/class of buildings. The benchmarking analysis was tested for a case study of 100 out-patient Healthcare Centres in Northern Italy, finally resulting in 12 different frequency distributions for space and Domestic Hot Water heating energy consumption, one for each class of homogeneous class of buildings. From the median value of each frequency distribution, reference values were extracted to be used in a benchmarking analysis. Beyond being flexible, open and upgradeable over time, a benchmarking analysis relying on both a sound statistical basis and on stochastic simulation is indeed able to overcome the limitations of the more common deterministic or one-dimensional benchmarking approach.

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  • 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.
    • Handle: RePEc:eee:appene:v:171:y:2016:i:c:p:592-607
    DOI: 10.1016/j.apenergy.2016.03.083
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    Cited by:

    1. Chung, William & Yeung, Iris M.H., 2017. "Benchmarking by convex non-parametric least squares with application on the energy performance of office buildings," Applied Energy, Elsevier, vol. 203(C), pages 454-462.
    2. Maria Psillaki & Nikolaos Apostolopoulos & Ilias Makris & Panagiotis Liargovas & Sotiris Apostolopoulos & Panos Dimitrakopoulos & George Sklias, 2023. "Hospitals’ Energy Efficiency in the Perspective of Saving Resources and Providing Quality Services through Technological Options: A Systematic Literature Review," Energies, MDPI, vol. 16(2), pages 1-21, January.
    3. Wang, Endong & Alp, Neslihan & Shi, Jonathan & Wang, Chao & Zhang, Xiaodong & Chen, Hong, 2017. "Multi-criteria building energy performance benchmarking through variable clustering based compromise TOPSIS with objective entropy weighting," Energy, Elsevier, vol. 125(C), pages 197-210.
    4. Piscitelli, Marco Savino & Giudice, Rocco & Capozzoli, Alfonso, 2024. "A holistic time series-based energy benchmarking framework for applications in large stocks of buildings," Applied Energy, Elsevier, vol. 357(C).
    5. Petojević, Zorana & Gospavić, Radovan & Todorović, Goran, 2018. "Estimation of thermal impulse response of a multi-layer building wall through in-situ experimental measurements in a dynamic regime with applications," Applied Energy, Elsevier, vol. 228(C), pages 468-486.
    6. Capozzoli, Alfonso & Piscitelli, Marco Savino & Brandi, Silvio & Grassi, Daniele & Chicco, Gianfranco, 2018. "Automated load pattern learning and anomaly detection for enhancing energy management in smart buildings," Energy, Elsevier, vol. 157(C), pages 336-352.
    7. Cai, Wei & Liu, Fei & Zhang, Hua & Liu, Peiji & Tuo, Junbo, 2017. "Development of dynamic energy benchmark for mass production in machining systems for energy management and energy-efficiency improvement," Applied Energy, Elsevier, vol. 202(C), pages 715-725.
    8. Talita Mariane Cristino & Antonio Faria Neto & Antonio Fernando Branco Costa, 2018. "Energy efficiency in buildings: analysis of scientific literature and identification of data analysis techniques from a bibliometric study," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 1275-1326, March.
    9. Gonzalo Sánchez-Barroso & Jaime González-Domínguez & Justo García-Sanz-Calcedo, 2020. "Potential Savings in DHW Facilities through the Use of Solar Thermal Energy in the Hospitals of Extremadura (Spain)," IJERPH, MDPI, vol. 17(8), pages 1-16, April.

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