Academia.eduAcademia.edu

Figure 2 – uploaded by Giulia Ulpiani

See full PDFdownloadDownload figure
Although simplified, the hourly dynamic method requires more input parameters compared to the monthly method, notably in terms of climatic data and occupancy patterns, as highlighted in Table 3

Table 3 Although simplified, the hourly dynamic method requires more input parameters compared to the monthly method, notably in terms of climatic data and occupancy patterns, as highlighted in Table 3

Related Figures (8)

Table 1. Geometrical features Table 2. Opaque envelope thermophysical parameters
Table 1. Geometrical features Table 2. Opaque envelope thermophysical parameters
Figure 1. Building plan, South-West view and North-East view ventilation rate at 0.5 h’. Table 2 recaps the thermal properties of opaque elements for each configuration.
Figure 1. Building plan, South-West view and North-East view ventilation rate at 0.5 h’. Table 2 recaps the thermal properties of opaque elements for each configuration.
Table 3. Main differences on input data between the monthly quasi steady-state and the hourly dynamic calculation methods
Table 3. Main differences on input data between the monthly quasi steady-state and the hourly dynamic calculation methods
Figure 2. Daily average of hourly load and external air temperature (UNI EN ISO 52016-1) Figure 2 shows the daily energy demand, computed through the hourly dynamic method for the reference nZEBs in Milan, Rome and Palermo. The method guarantees a higher level of detail, which helps spotting the dynamic variation of the building energy performance. The colored areas in the figure allow immediate comparison of the energy consumption throughout the year: expectedly given the external air temperature trends, Palermo’s nZEB would consume less than Milan’s nZEB in summertime, and reverse the roles in wintertime. The annual heating/cooling consumptions would hit 1486/1541 kWh/y in Milan, 974/1897 kWh/y in Rome and 543/2143kWh/y in Palermo.
Figure 2. Daily average of hourly load and external air temperature (UNI EN ISO 52016-1) Figure 2 shows the daily energy demand, computed through the hourly dynamic method for the reference nZEBs in Milan, Rome and Palermo. The method guarantees a higher level of detail, which helps spotting the dynamic variation of the building energy performance. The colored areas in the figure allow immediate comparison of the energy consumption throughout the year: expectedly given the external air temperature trends, Palermo’s nZEB would consume less than Milan’s nZEB in summertime, and reverse the roles in wintertime. The annual heating/cooling consumptions would hit 1486/1541 kWh/y in Milan, 974/1897 kWh/y in Rome and 543/2143kWh/y in Palermo.
Figure 3. Monthly heating and cooling energy needs obtained with the monthly quasi steady-state method (UNI/TS 11300) and the hourly dynamic method (UNI EN ISO 52016-1), and their percentage differences (A%) Figure 3 compares the results of the two calculation methods in terms of monthly energy needs for heating and cooling, obtained in the three climatic zones under test. In wintertime, the monthly, semi- stationary method tends to underestimate the useful energy in Milan and Rome (up to -100%) and to overestimate it in Palermo (up to +59%). Conversely, in summertime, the discrepancy narrows down and greater accordance is achieved (differences no higher than 11%).
Figure 3. Monthly heating and cooling energy needs obtained with the monthly quasi steady-state method (UNI/TS 11300) and the hourly dynamic method (UNI EN ISO 52016-1), and their percentage differences (A%) Figure 3 compares the results of the two calculation methods in terms of monthly energy needs for heating and cooling, obtained in the three climatic zones under test. In wintertime, the monthly, semi- stationary method tends to underestimate the useful energy in Milan and Rome (up to -100%) and to overestimate it in Palermo (up to +59%). Conversely, in summertime, the discrepancy narrows down and greater accordance is achieved (differences no higher than 11%).
Table 4. Standard nZEBs energy assessment for the considered case studies
Table 4. Standard nZEBs energy assessment for the considered case studies
Figure 4 displays average and standard deviation of the hourly energy needs, computed in the three climatic zones on monthly basis by means of the hourly dynamic method. These results are contrasted with the mean hourly needs obtained by dividing the monthly total by the number of hours per each month. Apparently, although the results look similar in terms on monthly means, there exist significant dispersion in terms of hourly data, notably during the cooling season. Furthermore, the hourly method includes the calculation of the operative temperature for each thermal zone. Thus, it is possible to compute the following comfort ranges in accordance with EN 15251 [18] thresholds: high level of expectation (Cat. I), normal level of expectation (Cat. II), moderate level of expectation (Cat. III). Figure 5 shows how the discomfort hours tend to decrease from Milan to Rome and from Rome to Palermo. Discomfort mostly occurs because of operative temperatures below the comfort lower limit for the three above-mentioned categories.
Figure 4 displays average and standard deviation of the hourly energy needs, computed in the three climatic zones on monthly basis by means of the hourly dynamic method. These results are contrasted with the mean hourly needs obtained by dividing the monthly total by the number of hours per each month. Apparently, although the results look similar in terms on monthly means, there exist significant dispersion in terms of hourly data, notably during the cooling season. Furthermore, the hourly method includes the calculation of the operative temperature for each thermal zone. Thus, it is possible to compute the following comfort ranges in accordance with EN 15251 [18] thresholds: high level of expectation (Cat. I), normal level of expectation (Cat. II), moderate level of expectation (Cat. III). Figure 5 shows how the discomfort hours tend to decrease from Milan to Rome and from Rome to Palermo. Discomfort mostly occurs because of operative temperatures below the comfort lower limit for the three above-mentioned categories.
5. Conclusions
5. Conclusions
Related topics:
Building Energy SimulationThermal Comfort of Buildings' OccupantsNew Calculation Method
580 California St., Suite 400
San Francisco, CA, 94104
© 2025 Academia. All rights reserved