LEE- Enerji Bilim ve Teknoloji Lisansüstü Programı
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Konu "bina bilgi modellemesi" ile LEE- Enerji Bilim ve Teknoloji Lisansüstü Programı'a göz atma
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ÖgeImplementing district-level retrofit strategies by utilizing the building information modelling-based modelling on a case study(Graduate School, 2024-07-07) Azari, Haniyeh Kamaneh ; Sözer, Hülya ; 301191050 ; Energy Science and TechnologyAddressing the imperative to reduce reliance on fossil fuels due to resource constraints underscores its significance. This study aims to diminish energy demands at the district level. Leveraging the Building Information Modeling (BIM) platform enables versatile perspectives, termed building and district levels, across various domains using diverse programs. Focused on Istanbul's KARTAL district, KIZILAY Street, this research surveys 78 buildings categorized by functionality and age, scrutinizing population, energy, cost, and greenery indicators at the district level. At the building level, two representative constructions, new and old, are chosen for BIM-based and energy modeling. Several software including Autodesk Revit, AutoCAD, E-Quest, PV watts Calculator, and Microsoft Office are used in this project. Here is a brief explanation of BIM-based and energy modeling of the selected buildings. The initial phase involves creating structural and architectural models of buildings using Revit Software to generate Building Information Modeling (BIM) models of the designated structures. These models are the basis for calculating the material quantities per square meter for each building. Subsequently, the energy profiles of the buildings are established by transferring the models to the Insight360 software. This step yields the average energy consumption per square meter for a year with other pertinent energy-related characteristics of the buildings. Furthermore, leveraging gbXML files exported from the Revit models and supplementary data inputted into the Green Building Studio program, a comprehensive analysis is conducted. This analysis encompasses various factors, such as annual energy consumption, associated costs, carbon dioxide emissions, water consumption and costs, potential wind energy utilization, and life cycle expenses. Strategies encompass passive (envelope insulation, window glazing, overhangs, blinds), active (photovoltaic panels, heat pumps), and operational management (HVAC system adjustments). The mentioned strategies are analyzed in the E-Quest energy model of each building based on the added numerical data and materials U-values to optimize energy efficiency. Extending results to the entire area allows for calculating the street's pre- and post-retrofitting needs at the district level. Furthermore, the impact of greenery and shading at the district level is assessed. In conclusion, implementing these steps at the building and district levels forecasted a reduction of KIZILAY Street's energy needs by 48.62%. The district's energy cost decreased by 49.02% which is a significant amount.