Browsing by Author "Kazanasmaz,T."

Now showing 1 - 2 of 2

Citation - WoS: 15
Citation - Scopus: 18
Multi-Objective Evolutionary Optimization of Photovoltaic Glass for Thermal, Daylight, and Energy Consideration
(Elsevier Ltd, 2023) Taşer,A.; Kazanasmaz,T.; Kundakcı Koyunbaba,B.; Durmuş Arsan,Z.; 01. Izmir Institute of Technology
The potential of fenestration systems is increased by incorporating photovoltaic technology into windows. This recently developed technology enhances the ability to generate energy from the building façade, improve the thermal and daylight performance of buildings, and visual comfort of occupants. Integrating an evolutionary optimization algorithm into this technology is one of the possible sustainable solutions to enhance building performance and minimize environmental impact. This paper uses a genetic evolutionary optimization algorithm to explore the optimum performance of photovoltaic glass in an architecture studio regarding annual energy consumption, energy generation, and daylight performance. Design variables include a window-to-wall ratio (i.e., window size and location) and amorphous-silicon thin-film solar cell transparency to generate optimum Pareto-front solutions for the case building. Optimization objectives are minimizing annual thermal (i.e., heating and cooling) loads and maximizing Spatial Daylight Autonomy. Optimized results of low-E semi-transparent amorphous-silicon photovoltaic glass applied on the façade show that the spatial daylight autonomy is increased to 82% with reduced glare risk and higher visual comfort for the occupants. Photovoltaic glass helped reduce the selected room's seasonal and annual lighting loads by up to 26.7%. Lastly, compared to non-optimized photovoltaic glass, they provide 23.2% more annual electrical energy. © 2023 International Solar Energy Society
Citation - Scopus: 1
Performance Indices of Soft Computing Models To Predict the Heat Load of Buildings in Terms of Architectural Indicators
(Yildiz Technical University, 2016) Turhan,C.; Kazanasmaz,T.; Akkurt,G.G.; 01. Izmir Institute of Technology
This study estimates the heat load of buildings in Izmir/Turkey by three soft computing (SC) methods; Artificial Neural Networks (ANNs), Fuzzy Logic (FL) and Adaptive Neuro-based Fuzzy Inference System (ANFIS) and compares their prediction indices. Obtaining knowledge about what the heat load of buildings would be in architectural design stage is necessary to forecast the building performance and take precautions against any possible failure. The best accuracy and prediction power of novel soft computing techniques would assist the practical way of this process. For this purpose, four inputs, namely, wall overall heat transfer coefficient, building area/ volume ratio, total external surface area and total window area/total external surface area ratio were employed in each model of this study. The predicted heat load is evaluated comparatively using simulation outputs. The ANN model estimated the heat load of the case apartments with a rate of 97.7% and the MAPE of 5.06%; while these ratios are 98.6% and 3.56% in Mamdani fuzzy inference systems (FL); 99.0% and 2.43% in ANFIS. When these values were compared, it was found that the ANFIS model has become the best learning technique among the others and can be applicable in building energy performance studies. © 2016. All Rights Reserved.