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Sensitivity of Characterizing the Heat Loss Coefficient through On-Board Monitoring: A Case Study Analysis

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  • Marieline Senave

    (Building Physics Section, Department of Civil Engineering, KU Leuven, Kasteelpark Arenberg 40—Box 2447, BE-3001 Heverlee, Belgium
    Unit Smart Energy and Built Environment, Flemish Institute for Technological Research (VITO), Boeretang 200, BE-2400 Mol, Belgium
    Cities in Transition, EnergyVille, Thor Park 8310, BE-3600 Genk, Belgium)

  • Staf Roels

    (Building Physics Section, Department of Civil Engineering, KU Leuven, Kasteelpark Arenberg 40—Box 2447, BE-3001 Heverlee, Belgium)

  • Stijn Verbeke

    (Unit Smart Energy and Built Environment, Flemish Institute for Technological Research (VITO), Boeretang 200, BE-2400 Mol, Belgium
    Cities in Transition, EnergyVille, Thor Park 8310, BE-3600 Genk, Belgium
    EMIB, Faculty of Applied Engineering, University of Antwerp, Groenenborgerlaan 171, BE-2020 Antwerp, Belgium)

  • Evi Lambie

    (Building Physics Section, Department of Civil Engineering, KU Leuven, Kasteelpark Arenberg 40—Box 2447, BE-3001 Heverlee, Belgium
    Cities in Transition, EnergyVille, Thor Park 8310, BE-3600 Genk, Belgium)

  • Dirk Saelens

    (Building Physics Section, Department of Civil Engineering, KU Leuven, Kasteelpark Arenberg 40—Box 2447, BE-3001 Heverlee, Belgium
    Cities in Transition, EnergyVille, Thor Park 8310, BE-3600 Genk, Belgium)

Abstract

Recently, there has been an increasing interest in the development of an approach to characterize the as-built heat loss coefficient (HLC) of buildings based on a combination of on-board monitoring (OBM) and data-driven modeling. OBM is hereby defined as the monitoring of the energy consumption and interior climate of in-use buildings via non-intrusive sensors. The main challenge faced by researchers is the identification of the required input data and the appropriate data analysis techniques to assess the HLC of specific building types, with a certain degree of accuracy and/or within a budget constraint. A wide range of characterization techniques can be imagined, going from simplified steady-state models applied to smart energy meter data, to advanced dynamic analysis models identified on full OBM data sets that are further enriched with geometric info, survey results, or on-site inspections. This paper evaluates the extent to which these techniques result in different HLC estimates. To this end, it performs a sensitivity analysis of the characterization outcome for a case study dwelling. Thirty-five unique input data packages are defined using a tree structure. Subsequently, four different data analysis methods are applied on these sets: the steady-state average, Linear Regression and Energy Signature method, and the dynamic AutoRegressive with eXogenous input model (ARX). In addition to the sensitivity analysis, the paper compares the HLC values determined via OBM characterization to the theoretically calculated value, and explores the factors contributing to the observed discrepancies. The results demonstrate that deviations up to 26.9% can occur on the characterized as-built HLC, depending on the amount of monitoring data and prior information used to establish the interior temperature of the dwelling. The approach used to represent the internal and solar heat gains also proves to have a significant influence on the HLC estimate. The impact of the selected input data is higher than that of the applied data analysis method.

Suggested Citation

  • Marieline Senave & Staf Roels & Stijn Verbeke & Evi Lambie & Dirk Saelens, 2019. "Sensitivity of Characterizing the Heat Loss Coefficient through On-Board Monitoring: A Case Study Analysis," Energies, MDPI, vol. 12(17), pages 1-29, August.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:17:p:3322-:d:261819
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    Citations

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    Cited by:

    1. Zhang, Xiang & Rasmussen, Christoffer & Saelens, Dirk & Roels, Staf, 2022. "Time-dependent solar aperture estimation of a building: Comparing grey-box and white-box approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    2. Zhang, Xiang & Saelens, Dirk & Roels, Staf, 2022. "Estimating dynamic solar gains from on-site measured data: An ARX modelling approach," Applied Energy, Elsevier, vol. 321(C).
    3. Christoffer Rasmussen & Peder Bacher & Davide Calì & Henrik Aalborg Nielsen & Henrik Madsen, 2020. "Method for Scalable and Automatised Thermal Building Performance Documentation and Screening," Energies, MDPI, vol. 13(15), pages 1-23, July.
    4. Lukas Lundström & Jan Akander, 2019. "Bayesian Calibration with Augmented Stochastic State-Space Models of District-Heated Multifamily Buildings," Energies, MDPI, vol. 13(1), pages 1-28, December.
    5. Evi Lambie & Dirk Saelens, 2020. "Identification of the Building Envelope Performance of a Residential Building: A Case Study," Energies, MDPI, vol. 13(10), pages 1-28, May.
    6. Vikas Sharma & Abul K. Hossain & Ganesh Duraisamy, 2021. "Experimental Investigation of Neat Biodiesels’ Saturation Level on Combustion and Emission Characteristics in a CI Engine," Energies, MDPI, vol. 14(16), pages 1-18, August.

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