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Buildings - especially the residential buildings - are among the high priority areas in climate change due to their large share of CO2 emissions, their significant energy saving potentials and the increased comfort expectations of their occupants. A systematic and numerical investigation of how building performance will be affected by climate change in the future is crucial for both retrofitting the existing buildings and making long-term strategical decisions. This paper presents the simulation-based results of the climate change impact assessment for a typical multi-storey residential building in Istanbul. In this study, RCP4.5 and RCP8.5 scenarios - representing the low/medium and high CO2 emission scenarios - defined by the Intergovernmental Panel on Climate Change (IPCC) are used to generate two different climate data sets for 2060. Three cooling scenarios (natural ventilation, hybrid ventilation and air-conditioned) have been developed to represent different building usage profiles for the building simulations. Investigated performance metrics were selected as building heating/cooling energy consumption, CO2 emission and occupant thermal comfort. Analysis results analysis show that the predicted temperature rise will have a strong impact on the building cooling energy consumption and/or occupant thermal comfort. The results indicate the necessity of taking measures for adaptation of buildings to the changing climate and the importance of decarbonization of electricity generation to mitigate the effects of climate change. This study will also serve as a basis for future retrofit studies for the changing climate. [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]