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The retrofit processes for buildings necessitates long-term planning and costly operations and requires a collaborative approach where a high number of alternative solutions should be explored by stakeholders. However, the evaluation of a range of retrofit solutions is a complex process wherein various design parameters and objectives are involved. The identification of the most effective solutions requires a collaborative evaluation in order to satisfy all stakeholders' expectations; however, during the decision-making process, stakeholders may generally have conflicting objectives. This paper discusses different user preference-based decision-making approaches for building retrofit that involves the collaborative evaluation of multiple design parameters and objectives simultaneously. For this purpose, we demonstrate a simulation-based approach for performative exploration for building retrofits, which may allow a broader consideration of alternative retrofit solutions to increase

The early design process has the most salient design decisions for architects. It is crucial to observe the impact of these design decisions in terms of performance-based design. However, because of the large amount of variance of the performance criteria in the early design parameters, the decision-making is highly arduous. The current study proposes a method to quantify output uncertainty and presents the relationship between independent and dependent variables for providing insight into the decision-making process. The energy simulations for hypothetical office building based on TS-825 requirements are executed with cooling and heating demand (kWh/m 2-year) outputs for two different regions, i.e., Erzurum as a cold climate and Izmir as a hot-humid climate. Researchers compute the input parameters' impact on building performance with quasi-random statistical sampling and filtering techniques. Respectively, ineffective parameters are eliminated with factor fixing, and factor prior

Abstract: The urban population increases continuously since the industrial revolution, and the residential buildings have the primary responsibility for the total energy demand. There is a need for the analysis of the residential building stock for energy efficiency and sustainable planning. However, energy modeling and simulation in urban scale is expensive in computational complexity and time, due to various building geometries and occupancy types. This research proposes a method to increase the efficiency of the simulation process by reorganizing the building geometries with functional clustering and radiation analysis scaling. In order to accelerate the urban building energy modeling (UBEM) process, the building geometries are modified based on energy simulation standards, then, clustering is determined based on radiation analysis and outside boundary conditions. The candidates are selected according to the selection percentage that has been identified before the process to simul

Abstract: The changing weather conditions due to global climate change is expected to have a direct impact on buildings' energy demand and occupant comfort. These conditions are estimated to become more challenging for educational facilities due to their high occupant density and the students' sensitivity to heat. This study aims to present an approach for a comparative analysis for multi-objective optimization results that are projected under different climate change conditions. Two separate optimization processes were performed using NSGA-II for an existing educational building, with the goal of minimizing occupant discomfort and energy use. The differences between the resulting Pareto-sets were analyzed based on the hypervolume difference and statistical evaluations, including the T-test and the distribution of properties. The results of the two optimization processes showed that future weather conditions should be considered on the retrofit process as two Pareto-set have