On the real energy model of a solar thermal systems in buildings: Creation and validation

• Method for creating a energy output model of existing solar thermal system. • Approach relying on physical dependencies. • The influence of external (meteorological) and internal (temperature of hot water in storage tank) factors on energy from solar thermal system • Comparison of empirical and the theoretical energy models. Among the various energy systems that exploit renewable energy sources, solar thermal collectors are widely applied in order to promote sustainable buildings, with large central installations requiring complex control methods. However, there is no universal method to estimate the energy yield from solar thermal collectors integrated in complex combi-systems that cover space heating and domestic hot water (DHW) and in some cases even cooling. This work tries to bridge this research gap and proposes a new method for development of a real energy model of an integrated solar thermal system in building, which considers the specific and local operational characteristics of individual systems. The experimental data was collected at a facility located at the Faculty of Environmental Engineering and Energy of the Lublin University of Technology, where a solar thermal collector was used for preparation of DHW using a water storage tank with a volume of 350 dm 3 . The meteorological data and characteristic parameters of the investigated system were monitored with a 5 min time step. The influence of individual external factors (solar irradiance, wind speed, outdoor temperature) as well as the impact of internal temperature of hot water in storage tank, on energy yield of the system was addressed by the development of the energy model of the solar thermal system. The proposed method was characterized by a low mean absolute percentage error (MAPE) of 19.4 % for 5-minute data and 12.3 % for hourly data, resulting to a much higher performance compared to the theoretical energy model of solar thermal collector (MAPE of 137.3 %). Future research will focus on expanding and validating the proposed approach in existing buildings and integrate artificial intelligence into the proposed energy model.