研究目的
To investigate the thermal buffering effect of a combined green roof with photovoltaics and compare it to a normal extensive green roof.
研究成果
The PV+green roof combination functions as a year-round thermal buffer, providing both summer and winter thermal protection. The system adapts to weather conditions, offering cooler temperatures during the day in summer and warmer temperatures in winter. However, under extreme cold conditions below -10°C, the system's buffering effect is exhausted, indicating a need for further optimization.
研究不足
The study found that the PV+green roof system's buffering effect is exhausted under very long cold periods with temperatures below -10°C, leading to a risk of frozen ground. Further research is needed to prevent the discharge of the buffering effect under such extreme conditions.
1：Experimental Design and Method Selection:
The study involved the installation of a PV flat roof system in combination with an extensive green roof. Temperature measurements were taken at the interface between the roof sealing and the substrate for the PV+green roof combination and at several points with a conventional extensive green roof (GD).
2：Sample Selection and Data Sources:
The vegetation and green roof were selected and supervised by the project partner, the University of Natural Resources and Life Sciences, Vienna. The substrate heights ranged between 5 cm and 15 cm.
3：List of Experimental Equipment and Materials:
The PV system was divided into two PV groups (F4 and F5), each equipped with 12 glass-glass monocrystalline PV modules with a residual light transmission of 30% and a total output of 3.72 kWp.
4：72 kWp.
Experimental Procedures and Operational Workflow:
4. Experimental Procedures and Operational Workflow: Measurement sensors were installed at the boundary between the substrate and the roof sealing of the respective measuring point. Their depth corresponded to the height of the substrate for the given measuring point.
5：Data Analysis Methods:
The measured values were logged at 10-minute intervals. From these values, the hourly average values were determined. The data were then modeled and evaluated over a ten-month period.
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