Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Citation - WoS: 64Citation - Scopus: 74Optimization of an Envelope Retrofit Strategy for an Existing Office Building(Elsevier Ltd., 2012) Güçyeter, Başak; Günaydın, Hüsnü Murat; 02.02. Department of Architecture; 02. Faculty of Architecture; 01. Izmir Institute of TechnologyEnergy-efficient retrofits include improvement of building envelope via insulation, employment of building integrated renewable energy technologies, and climate control strategies. Building envelope improvements with insulation is a common approach, yet decision-making plays an important role in determining the most appropriate envelope retrofit strategy. In this study, main objective is to evaluate and optimize envelope retrofit strategies through a calibrated simulation approach. Based on an energy performance audit and monitoring, an existing building is evaluated on performance levels and improvement potentials with basic energy conservation measures (ECMs). The existing building is monitored for a full year and monitoring data is used in calibrating the simulation model. In order to obtain a better-performing building envelope three retrofit strategies including several ECMs are proposed. Retrofit strategies are simulated through calibrated base-case model, and results are evaluated according to changes in indoor environmental parameters and annual energy consumption measures. The analysis of results indicated that pre-assessed strategies yield close results. Therefore, a more comprehensive evaluation based on different decisive criteria is used in optimization of the final retrofit strategy, with the intention to evaluate the effect of individual ECMs on annual end-use energy consumption and investment.Article Citation - WoS: 91Citation - Scopus: 122Artificial Neural Networks To Predict Daylight Illuminance in Office Buildings(Elsevier Ltd., 2009) Kazanasmaz, Zehra Tuğçe; Günaydın, Hüsnü Murat; Kazanasmaz, Zehra Tuğçe; Günaydın, Hüsnü Murat; 02.02. Department of Architecture; 02. Faculty of Architecture; 01. Izmir Institute of TechnologyA prediction model was developed to determine daylight illuminance for the office buildings by using artificial neural networks (ANNs). Illuminance data were collected for 3 months by applying a field measuring method. Utilizing weather data from the local weather station and building parameters from the architectural drawings, a three-layer ANN model of feed-forward type (with one output node) was constructed. Two variables for time (date, hour), 5 weather determinants (outdoor temperature, solar radiation, humidity, UV index and UV dose) and 6 building parameters (distance to windows, number of windows, orientation of rooms, floor identification, room dimensions and point identification) were considered as input variables. Illuminance was used as the output variable. In ANN modeling, the data were divided into two groups; the first 80 of these data sets were used for training and the remaining 20 for testing. Microsoft Excel Solver used simplex optimization method for the optimal weights. The model's performance was then measured by using the illuminance percentage error. As the prediction power of the model was almost 98%, predicted data had close matches with the measured data. The prediction results were successful within the sample measurements. The model was then subjected to sensitivity analysis to determine the relationship between the input and output variables. NeuroSolutions Software by NeuroDimensions Inc., was adopted for this application. Researchers and designers will benefit from this model in daylighting performance assessment of buildings by making predictions and comparisons and in the daylighting design process by determining illuminance.Article Citation - Scopus: 250A Neural Network Approach for Early Cost Estimation of Structural Systems of Buildings(Elsevier Ltd., 2004) Günaydın, Hüsnü Murat; Doğan, Sevgi Zeynep; Doğan, Sevgi Zeynep; Günaydın, Hüsnü Murat; 02.02. Department of Architecture; 02. Faculty of Architecture; 01. Izmir Institute of TechnologyThe importance of decision making in cost estimation for building design processes points to a need for an estimation tool for both designers and project managers. This paper investigates the utility of neural network methodology to overcome cost estimation problems in early phases of building design processes. Cost and design data from thirty projects were used for training and testing our neural network methodology with eight design parameters utilized in estimating the square meter cost of reinforced concrete structural systems of 4-8 storey residential buildings in Turkey, an average cost estimation accuracy of 93% was achieved.