Office Building Tenants’ Electricity Use Model for Building Performance Simulations

Andrea Ferrantelli, Helena Kuivjõgi, Jarek Kurnitski and Martin Thalfeldt
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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
Martin Thalfeldt: Department of Civil Engineering and Architecture, Tallinn University of Technology, 19086 Tallinn, Estonia

Energies, 2020, vol. 13, issue 21, 1-19

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.

Keywords: building simulation; office buildings; energy performance; energy modelling; HVAC; analytical modelling; statistical analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
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