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.
Highlights
It is well known that the energy use of buildings depends remarkably on occupant behaviour, which, e.g., includes indoor climate parameter preferences, how different systems are operated as well as when and by how many people the buildings are used
We shall report the data analysis in detail, using a descriptive step-by-step approach guiding the reader through the development of a simple method for generating energy consumption profiles for application in building performance simulations (BPS)
It is quite feasible to model this type of energy data with high accuracy even with simple methods; models should be conveniently chosen according to the specific BPS
Summary
It is well known that the energy use of buildings depends remarkably on occupant behaviour, which, e.g., includes indoor climate parameter preferences, how different systems are operated as well as when and by how many people the buildings are used. The authors conclude that this might occur by a loose implementation of the occupants’ behaviour in building energy performance simulations (BPS), where climate data and physical characteristics of the building are addressed in detail whilst occupancy schedules are fixed according to generic standards. Upon recognition of this problem, in the past decade a number of research efforts have considered how modelling the occupant behaviour lifestyles impacts the building energy use, either for direct implementation into BPS [3,4,5,6,7] or with a more general formulation [8,9,10]. Klein et al [5] use a distributed comfort evaluation based on Energies 2020, 13, 5541; doi:10.3390/en13215541 www.mdpi.com/journal/energies
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