# Modeling Heat Consumption of an Office Building During COVID-19 Restrictions
## 4 Results
This led to a lower inconsistency between the actual and optimized load variation curves in relation to an ordinary profile in [25]. As shown in Figure 1b, from 06:00 to 10:00 and from 15:00 to 21:00, the discrepancy between actual and predicted heat consumption was especially large, with a maximum error of 0.15 kW and an average difference of 0.11 kW. The primary reason was that the impact of TRVs and thermal inertia was not considered. For instance, as shown in Figure 2b, the maximum energy demand registered was 0.52 kW at 12:00, while the heat demand at 4:00 was only 0.01 kW. Figure 2a highlights that the heat absorbed due to solar insolation was not negligible, especially when night energy demand (about 5.1 kW) was compared to the total amount consumed by the entire HVAC system during the day (about 4.7 kW, 8.51% less). Because of solar insolation, tuning the control of the building glazing's transmittance was taking more attention day by day in the literature, and it had already come into use in some buildings, such as high-rise hotel buildings in the United Arab Emirates. Here, the energy consumption profiles during the daily hours dynamically also changed according to the internal heat gains due to the usage of household appliances (e.g., fridges, kettles, and coffee machines), electronic devices (e.g., desktop computers and all-in-ones), and gadgets (e.g., vending machines or 3D printers). The maximum energy consumption accounted for 0.55 kW at 11:00. After that, we added three alternative scenarios by engaging the morning and evening setbacks in the control strategy of the DH substation's valve. The minimum heat consumption and indoor temperature were registered during nighttime hours when the setback was scheduled (from 22:00 to 7:00). Due to this handicap, with the help of TRVs, radiant heating applications could be limited to less occupied or vacant offices. At the same time, employing substation controllers with indoor temperature feedback perfectly integrated the DH technology. It addressed issues like overheating, high thermal load, and slow response time associated with conventional SH systems. Another advantage of flexible operation was energy efficiency. If the indoor temperature feedback was considered, the energy consumption did not reach 0.5 kW in the evening. Moreover, the daily average heat demand decreased, becoming only 0.25 kW. The downside was decreasing the secondary mass flow rate, which may have hampered achieving indoor comfort in the offices where an SH system did not bring ample energy earlier [12]. To compare, [PERSON] et al. [10] investigated an advanced controller (PI one), a combination of the conventional and the previous controller, i.e., a cascaded PI controller consisting of outer and inner loops. The same as [4], we proved that HVAC parameters (such as energy demand and indoor temperature) depend on the number of people (i.e., internal heat gains); therefore, the rational employment of control strategies may contribute to the faster prediction of actual heat demand and indoor thermal comfort in the short term (1-3 h), further guiding the optimal regulation along with the energy saving of HVAC systems. However, the contribution of some strategies, such as solar insolation, thermal inertia (#5), and wind (#6), was almost negligible (between 1 and 5.1 MWh/day). At the same time, the indoor temperature feedback (#1), night setbacks (#3), and TRVs (#4) brought as much as 18.2% of the total savings (Table 7). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenarios** & **Cost Saving per Day** & **Energy Saving** & **Energy-Saving Rate** \\  & **[EUR]** & **(kWh)** & **[\%]** \\ \hline \#1 (Indoor temperature feedback) & 270.4 & 38.626 & 6.4\% \\ \#2 (Solar insolation) & 24.9 & 3556 & 0.6\% \\ \#3 (Night setbacks) & 347.5 & 49,647 & 8.2\% \\ \#4 (TRVs) & 979.9 & 21,776 & 3.6\% \\ \#5 (Thermal inertia) & 228.5 & 5078 & 0.8\% \\ \#6 (Wind) & 48.6 & 1080 & 0.2\% \\ All the scenarios implemented & 1899 & 119.7 & 19.8\% \\ \hline \hline \end{tabular}
\end{table}