Feasibility of Solar Thermal Collectors via Artificial Neural Network

Today, the concept of net zero- energy buildings has become a major concern regarding fossil fuels combustion side effects, such as air pollution or global warming. NG (Natural Gas) is a kind of fossil fuels which is widely combusted in Iran to meet various buildings heating demand. However, this research is proposed mitigating NG flaring by substituting solar energy as an auxiliary energy source in dwelling buildings and estimating accurate values of SGI (Solar Global Irradiance) on tilted planes via Artificial Neural Network (ANN) respectively. To fulfill this objective a solar thermal collector is designed which is either responsible to preheat water circulates in radiators loop, to provide space heating, or domestic hot water to supply washing demand in flats. Indeed, in case of utilizing this application fewer NG will be combusted for rising input water temperature to required value and fewer CO2 emission eventually. Also a dwelling complex including 136 apartments is considered perform this research. This complex is situated in Mashhad, the second megacity in Iran. Besides, to evaluate this potential four steps have been taking into account.