Abstract

A solar combisystem is utilized to supply domestic hot water (DHW) and heat a residential building. This system decreases the fossil fuel consumption and, hence, reduces air pollution and global warming. This paper proposes an innovative method to design and optimize a suitable solar combisystem for a residential building with respect to technical and economic parameters. First, different configurations of solar combisystems, including various components, are considered. Then, the optimum design variables are determined for each configuration using Grouped Method of Data Handling (GMDH) type of Artificial Neural Network (ANN) and Non-Dominated Sorting Genetic Algorithm II (NSGA-II). Total Solar Fraction (TSF) and Life Cycle Cost (LCC) are considered as objective functions. Finally, the optimum design is chosen for the solar combisystem. Typically, researchers have focused on evaluating the performance of a single solar combisystem configuration in each study. However, this research takes a different approach by optimizing multiple configurations, resulting in a significant improvement in the total solar fraction by 7.3%. This is the main novelty of this paper. Based on the results, the optimum configuration of the solar combisystem includes 17.91 m2 of evacuated-tube collectors with a tilt angle of 50°. Also, the volume of the hot water tank and heating buffer tank are, respectively, equal to 204 and 500 L (L). In this system, solar energy provides 94% of the required energy for supplying DHW and 23% of the energy for heating the building. Moreover, reduction of annual CO2 emissions is 1806 kg. This paper presents a guideline to design solar systems for the residential buildings considering technical and economic aspects.

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