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Office Building Tenants’ Electricity Use Model for Building Performance Simulations

Author

Listed:
  • Andrea Ferrantelli

    (Department of Civil Engineering and Architecture, Tallinn University of Technology, 19086 Tallinn, Estonia)

  • Helena Kuivjõgi

    (Department of Civil Engineering and Architecture, Tallinn University of Technology, 19086 Tallinn, Estonia)

  • Jarek Kurnitski

    (Department of Civil Engineering and Architecture, Tallinn University of Technology, 19086 Tallinn, Estonia
    Department of Civil Engineering, Aalto University, 00076 Aalto, Finland)

  • Martin Thalfeldt

    (Department of Civil Engineering and Architecture, Tallinn University of Technology, 19086 Tallinn, Estonia)

Abstract

Large office buildings are responsible for a substantial portion of energy consumption in urban districts. However, thorough assessments regarding the Nordic countries are still lacking. In this paper we analyse the largest dataset to date for a Nordic office building, by considering a case study located in Stockholm, Sweden, that is occupied by nearly a thousand employees. Distinguishing the lighting and occupants’ appliances energy use from heating and cooling, we can estimate the impact of occupancy without any schedule data. A standard frequentist analysis is compared with Bayesian inference, and the according regression formulas are listed in tables that are easy to implement into building performance simulations (BPS). Monthly as well as seasonal correlations are addressed, showing the critical importance of occupancy. A simple method, grounded on the power drain measurements aimed at generating boundary conditions for the BPS, is also introduced; it shows how, for this type of data and number of occupants, no more complexities are needed in order to obtain reliable predictions. For an average year, we overestimate the measured cumulative consumption by only 4.7%. The model can be easily generalised to a variety of datasets.

Suggested Citation

  • Andrea Ferrantelli & Helena Kuivjõgi & Jarek Kurnitski & Martin Thalfeldt, 2020. "Office Building Tenants’ Electricity Use Model for Building Performance Simulations," Energies, MDPI, vol. 13(21), pages 1-19, October.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:21:p:5541-:d:433094
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    References listed on IDEAS

    as
    1. Jerzy Mikulik, 2018. "Energy Demand Patterns in an Office Building: A Case Study in Kraków (Southern Poland)," Sustainability, MDPI, vol. 10(8), pages 1-16, August.
    2. Bates, Douglas & Mächler, Martin & Bolker, Ben & Walker, Steve, 2015. "Fitting Linear Mixed-Effects Models Using lme4," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 67(i01).
    3. Delzendeh, Elham & Wu, Song & Lee, Angela & Zhou, Ying, 2017. "The impact of occupants’ behaviours on building energy analysis: A research review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 1061-1071.
    4. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    5. Marie Laure Delignette-Muller & Christophe Dutang, 2015. "fitdistrplus : An R Package for Fitting Distributions," Post-Print hal-01616147, HAL.
    6. Delignette-Muller, Marie Laure & Dutang, Christophe, 2015. "fitdistrplus: An R Package for Fitting Distributions," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i04).
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