With the increasing concern on the environment and climate change, scientists focused on the way in which new structures, especially in the field of energy. In this sense, the concept of sustainable buildings is developing day by day in terms of energy efficiency. The sustainable building concept identifies five objectives which are resource efficiency, energy efficiency, prevention of pollution, harmonization with the environment; and also using integrated and systemic approaches. To increase energy efficiency in buildings, the integration of Solar Energy Systems into buildings attaches importance in terms of sustainable engineering designs. Evacuated Tube Heat Pipe (ETHP) solar energy systems are also noteworthy in this regard. This paper presents the results of an experimental study that is an ETHP solar collector system. ETHP systems are an alternative solar energy system to low-efficiency planary collectors. Only water was used to avoid losses in the heat transfer from the fluid to the fluid. Water is inserted in the vacuum tube in order to improve the rate of heat transfer, such that the mode of heat transfer from the inner surface of the vacuum tube to the heat pipe becomes convection via the water, as well as conduction through the installed. The exergy efficiency of the ETHP system was calculated as 32.94%. For a long time, artificial neural networks (ANN) have been widely applied in energy efficiency for modeling and optimization of various processes. In the field of processing, recent studies confirm the validity and effectiveness of using ANNs as promising and most powerful computer modeling techniques. Within the scope of this study, exergy efficiency was evaluated on the developed Artificial Neural Network algorithm. The effect rates of parameters such as pressure, temperature, and ambient temperature affecting exergy efficiency were calculated. Finally, significant findings obtained were evaluated in terms of thermodynamics rules.
Tolon, F. E., Karabuga, A., Tolon, M., & Utlu, Z. (2019). Evaluation of Thermodynamic Analysis of Solar Energy Systems Integrated into Sustainable Buildings with Artificial Neural Network: A Case Study. In Procedia Computer Science (Vol. 158, pp. 91–98). Elsevier B.V. https://doi.org/10.1016/j.procs.2019.09.031