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    Enhancing energy efficiency and cost-effectiveness while reducing CO2 emissions in a hospital building
    (Elsevier Ltd, 2023) As, Murat
    Global population growth drives fossil fuel usage, surpassing natural greenhouse gas emissions and causing climate change, higher temperatures, floods, and agricultural erosion. The study underscores the significance of appropriate material selection and meticulous building design to establish a healthy living environment and mitigate energy consumption. The research is dedicated to the reduction of CO2 emissions. Hospitals, due to their constant energy requirements, require optimal temperature and comfort management for patients, necessitating careful design to avoid disruptions and power outages. The principal objective of this research is to enhance energy efficiency in the design phase of hospital buildings in Turkey and to develop an energy-efficient hospital model. This objective is achieved by evaluating different insulation materials, building orientations, lighting efficiency, and window-to-wall ratios. The methodology involves creating a reference structure for energy load calculations using Revit software and utilizing the Green Building Studio (GBS) program to conduct energy analyses in 7 different cities across Turkey. The study found that the utilization of building parameters with renewable energy sources reduced energy consumption by 57.5%, total costs by 16.24%, and CO2 emissions by 26.3%. The fundamental contribution of this study is to demonstrate the feasibility of reducing energy consumption, costs, and CO2 emissions through design adjustments. The novelty of this research lies in its comprehensive approach to optimizing energy efficiency and CO2 emissions in hospital buildings in Turkey. These findings underline the significance of energy-efficient design, particularly considering the impact of increased energy usage and CO2 emissions during the COVID-19 pandemic.
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    Öğe
    Machine learning algorithms for energy efficiency: Mitigating carbon dioxide emissions and optimizing costs in a hospital infrastructure
    (Elsevier Ltd, 2024) As, Murat; Bilir, Turhan
    As with many other sectors, to improve the energy performance and energy neutrality requirements of individual buildings and groups of buildings, built environment is also making use of machine learning for improved energy demand predictions. The goal of achieving energy neutrality through maximized use of on-site produced renewable energy and attaining optimal level of energy performance at building-cluster level requires reliable short term (resolution shorter than one day) energy demand predictions. However, the prediction and analysis of the energy performance of buildings is still focused on the individual building level and not on small neighborhood scale or building clusters. In a smart grid context, to better understand electricity consumption at different spatial levels, prediction should be at both individual as well as at building-cluster levels, especially for neighborhoods with definite boundaries (such as universities, hospitals). Therefore, in this paper, using data from 47 commercial buildings, a number of machine learning algorithms were evaluated to predict the electricity demand at individual building level and aggregated level in hourly intervals. Predicting at hourly granularity is important to understand short-term dynamics, yet most of the neighborhood scale studies are limited to yearly, monthly, weekly, or daily data resolutions. Two years of data were used in training the model and the prediction was performed using another year of untrained data. Learning algorithms such as; boosted-tree, random forest, SVM-linear, quadratic, cubic, fine-Gaussian as well as ANN were all analysed and tested for predicting the electricity demand of individual and groups of buildings. The results showed that boosted-tree, random forest, and ANN provided the best outcomes for prediction at hourly granularity when metrics such as computational time and error accuracy are compared.