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It is known that the energy needs of a naturally ventilated building consume 40% less energy than a mechanically ventilated building. Especially in summer months, the energy needs of mosques increase greatly due to cooling loads. The aim of the study is to provide passive cooling with the stack effect by taking advantage of the large floor height of mosques compared to other buildings and to evaluate the effect of the number of roof openings on passive cooling. Within the scope of the study, a prototype mosque was designed in line with general practices and regulations in Türkiye. Two different opening alternatives were created. In the first alternative, air outlets are created as a single large opening. In the second alternative, the number of openings was increased to four, but the air outlet size remained the same in total. The two prepared alternatives were transferred to ANSYS Fluent software. In this way, the passive cooling of the building was visualized through simulation. The temperature and velocity contour graphs were created as a result of the analysis. According to the results, the alternative in which more than one roof opening was designed, provided better passive cooling than the alternative with a single opening. [ABSTRACT FROM AUTHOR]

This study investigates the efficiency of a biogas-powered cooling system through the utilization of energy and exergy calculations. Biogas, which can be generated and stored in small-scale plants as needed, serves as a viable fuel source for absorption cooling systems. The present research focuses on the biogas consumption of a triple-effect absorption cooling system specifically designed to supply a fixed cooling load of 100 kW under varying operational conditions. This study highlights the coefficient of performance and exergetic coefficient of performance values of the system, along with the exergy destruction rates of its individual components, at the optimal temperatures of operation. Furthermore, to determine necessary biogas consumption, this study explores the establishment of dedicated farms for various animal species, ensuring an adequate number of animals for biogas production. The findings reveal a coefficient of performance of 1.78 and an exergetic coefficient of performance of 35.4% at the optimized operating temperatures. The minimum mass flow rate of biogas is determined to be 0.0034 kgs−1, facilitating the operation of the boiler with a methane content of 65%. This study concludes that a total of 290 head of cattle is required to generate the annual biogas consumption necessary for the cooling system. Additionally, the number of the cattle is enough to establish 284 biogas plants in Bursa Province in Türkiye. [ABSTRACT FROM AUTHOR]

Energy efficiency in building design tends to be important in reducing the share of construction sector in energy consumption and environmental problems caused by it. In this study, energy performance of an existing office structure was evaluated by integrating shading elements and double skin facade systems within the scope of energy efficiency in buildings. Initially, current energy performance of the building was examined by design builder program and then, the shading element alternatives and the double skin facade system alternatives were separately integrated into the structure and simulated by solar tool and design builder program. Alternatives with the highest energy performances were determined in both building components. Later in the study, the shading element and the double skin facade system alternatives, which gave the most successful results, were integrated into the building model simultaneously and the energy performance thus evaluated was compared with the energy performance of the existing building. According to results, the Shaded Double Skin Facade scenario indicated a drop in heating load of around 2% and a decrease in cooling load of about 45% compared to the present building model. [ABSTRACT FROM AUTHOR]

Many models have been developed to predict the energy consumption of various building types, including residential, office, institutional, educational, and commercial buildings. However, to date, no models have been designed specifically to predict poultry buildings' energy consumption. To address this information gap, this study integrated data-driven techniques, including artificial neural networks (ANN), support vector regressions (SVR), and random forest (RF), into a physical model to predict the energy consumption of poultry buildings in different climatic zones in Turkey. The following statistical indices were employed to evaluate the model's effectiveness: Root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2). The calculated and predicted values of the heating and cooling loads were also compared using visualization techniques. The results indicated that the RF model was the most accurate during the testing period according to the RMSE (0.695 and 6.514 kWh), MAPE (3.328 and 2.624%), and R2 (0.990 and 0.996) indices for heating and cooling loads, respectively. Overall, this model offers a simple decision-support tool to estimate the energy requirements of different buildings and weather conditions. [ABSTRACT FROM AUTHOR]