Search

The outdoor temperature is one of the most important parameters affecting the calculation of energy requirements of buildings. Determination of the thermal insulation details and heating energy requirement of the buildings in Turkey, where energy consumption is dominated by space heating, is made according to TS 825 "Thermal Insulation Rules in Buildings" standard. In TS 825, the provinces are clustered in degree-day regions (DDR) according to their climatic conditions. The main climatic parameter that separates these DDRs from each other is the outdoor temperature. In this study, it is aimed to discuss the effect of outdoor temperature on the building heating energy requirement and validity of the DDRs in the standard according to outdoor temperature data. In this context, the heating energy requirement of the case building is calculated according to different outdoor temperature data of the provinces and the outdoor temperature data of the DDRs in the current and recommended version of the standard. The effectiveness of the DDRs and the updates in the standard are examined by comparing them separately for 81 provinces. The results show that in some DDR clusters, Qyear values of the provinces from different DGBs are very close to each other; even, comparative Qyear values of some of them contradict with their DDR. This situation suggests that the DDR approach should be re-examined. Besides, the new DDRs in the recommended version of the standard is rather compatible with the comparative results. In addition, the selection of the heating threshold temperature has been shown to have a limited effect on the calculations, while the up-to-dateness of the outdoor temperature data to be used in the analyses has been shown to significantly affect the heating energy calculation of the building. [ABSTRACT FROM AUTHOR]

In this study, the effects of climate change on the energy consumption and thermal comfort of the education building were examined and the effectiveness of passive improvement scenarios based on window parameters was evaluated by machine learning and statistical analysis. The research was based on a four-stage approach based on building simulations: (i) creation and analysis of climate change scenario-modified climate datasets, (ii) climate change impact analysis on existing building, (iii) comparative analysis of improvement scenarios, and (iv) analysis of predictive models based on machine learning. An existing secondary school building in Ankara was chosen as a case study for the evaluation of the selected performance indicators. 2025 scenarios were parametrically modeled with varying window parameters. After analyzing the complete dataset generated from performance simulations with descriptive statistics, Random Forest (RF) prediction models are trained with a subset of the data for each performance indicator. For each performance indicator, the feature importance of fine-tuned RF models was calculated with 10-fold cross-validation method, and it was seen that the window SHGC value was the most critical parameter among the tested variables. Performance predictions with RF models deviate 2% on average from their actual values and imply high predictive capacity. Moreover, with the retrofit scenarios, total energy consumption showed a reduction of up to 50%, whereas a significant improvement in indoor thermal comfort was observed. The results emphasize that the right selection of window parameters in existing educational buildings has a great effect on building energy performance. The results show that machine learning can be used effectively in the adaptation processes of buildings to climate change. The method used can be extended to cover different building parameters and technologies. [ABSTRACT FROM AUTHOR]