# Modeling Heat Consumption of an Office Building During COVID-19 Restrictions
###### Abstract
[11] report the results when the indoor temperature varies in a much narrower range with an average of 23 \({}^{\circ}\)C, with a maximum value of 23.4 \({}^{\circ}\)C, a minimum value of 22.6 \({}^{\circ}\)C, and a difference of 0.8 \({}^{\circ}\)C, i.e., quite-constant thermal comfort with almost no fluctuations. The operational data show that their methodology is promising at ensuring indoor temperature at 23 \(\pm\) 0.4 \({}^{\circ}\)C. Similar equations and plots, as shown in [11], are used here to assess the daily fluctuation of supply and indoor temperatures. The same as us, [PERSON] et al. [12] showed profiles generated according to several scenarios, although for the first 6 weeks only, they focused on the daily indoor temperature. It is pertinent to remark that in [12], the difference between the profiles increased over time, meaning that [PERSON] et al. 's scenarios had different lifetime capabilities. Furthermore, [PERSON] et al. [10] compared these controllers' performances to find out the perspectives of suggested controllers in contemporary HVAC systems. The limitations of most of the control patterns are that they only utilize setbacks (especially daily ones) to a very limited extent [9]. However, new daily setbacks have been introduced lately to decrease energy consumption. The most real reason for the setbacks during midday is COVID-19 restrictions limiting the number of employees. [PERSON] et al. [14] introduced compensation times, which were also obtained with the help of operational data. However, to ensure the desired 24 and 48 h compensation periods, they mainly modified the heat production and elaborated on the potential of creating thermal energy storage within a DH system. [PERSON] et al. [15] studied the heat emitted by occupants and domestic appliances creates thermal plumes caused by the buoyancy effect and upward ventilation direction. The heat in the vicinity of the room's ceiling also changes the building's thermal inertia. The accumulated heat discharges through the exhaust ventilation without any heat recovery and does not affect the energy consumption profiles of the building. The difference is the author's focus on a residential building and its specs. They studied a building in relatively artificial conditions, where the indoor temperature was fixed at 20 \({}^{\circ}\)C and furniture was represented by a planar element with an area of 100 m\({}^{2}\). The thickness of envelopes was set to 4 cm while material-specific heat capacity was 1.4 kJ/kg K with a material thermal conductivity of 0.3 W/mK and a 600 kg/m\({}^{3}\) density. Unlike office buildings, in residential ones, most of the cooling thermal load resulting from thermal sources such as lightning, people, machines, and accumulated next to the ceiling is never met by an HVAC system, except for radiation heat transfer (mainly solar insolation). For a high-temperature DH system, the primary measures involve decreasing distribution losses by decreasing the network temperatures and ensuring lower SH demand [9]. Based on this conclusion, it may be possible to continue the research with the present paper aiming at decreasing network return temperatures and enhancing modeling quality. Unlike previous papers, a low return temperature is declared a key factor in keeping high performance and decreasing primary energy consumption. An increased temperature difference on the system level also eases DH system expansion without endangering the indoor comfort of existing consumers. Unlike previous papers, e.g., [9; 16; 17; 18], energy demand is now more flexible as outdoor temperatures increase or decrease; energy demand depends now on specific building characteristics and regional particularities, e.g., whether it is a weekday or a weekend. A framework to simulate the steady-state demand in DH systems is formulated for a branched DH network, which addresses the issues of the previously proposed methods [9; 17; 18].