Browsing by Author "Kazanasmaz, Z. Tugce"

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Citation - WoS: 7
Citation - Scopus: 9
Fuzzy logic model for the categorization of manual lighting control behaviour patterns based on daylight illuminance and interior layout
(SAGE Publications Ltd info@sagepub.co.uk, 2019) Arzu Cilasun Kunduraci; Tugce Kazanasmaz; Cılasun Kunduracı, Arzu; Kunduraci, Arzu Cilasun; Kazanasmaz, Z. Tugce
In considering total building energy consumption lighting plays an important role in shaping energy consumption and use. Although key strategies (such as energy efficient lighting products lighting control systems and energy simulation software) are developed so far such attempts may be unsuccessful unless users are not taken into consideration. Users’ behaviours and their manual lighting control actions depend on various factors though within the scope of this study manual lighting control behaviour was analysed only in terms of interior layout and daylight illuminance. Three private offices in Izmir Institute of Technology were monitored using illuminance metres and occupancy/light detectors under eight different interior layout conditions. In relation to change of interior layout and daylight penetrations users’ manual lighting control behaviours were monitored. The obtained data were then used to construct a fuzzy logic model in MATLAB FIS editor. A fuzzy logic algorithm was applied to classify behaviour patterns about the tendency to turn on the lights. This kind of prediction of the light usage tendency regarding the occupancy is aimed to foresee the ‘possible’ manual lighting control behaviour within given conditions. The gathered classification can be used further in future studies of manual lighting control behaviour and energy-saving estimations/simulations. © 2019 Elsevier B.V. All rights reserved.
Citation - WoS: 38
Citation - Scopus: 52
Multi-zone optimisation of high-rise buildings using artificial intelligence for sustainable metropolises. Part 1: Background methodology setup and machine learning results
(Elsevier Ltd, 2021) Berk Ekici; Tugce Kazanasmaz; Michela Turrin; M. Fatih Tasgetiren; I. Sevil Sariyildiz; Ekici, Berk; Turrin, Michela; Tasgetiren, M. Fatih; Sariyildiz, I. Sevil; Kazanasmaz, Z. Tugce
Designing high-rise buildings is one of the complex tasks of architecture because it involves interdisciplinary performance aspects in the conceptual phase. The necessity for sustainable high-rise buildings has increased owing to the demand for metropolises based on population growth and urbanisation trends. Although artificial intelligence (AI) techniques support swift decision-making when addressing multiple performance aspects related to sustainable buildings previous studies only examined single floors because modelling and optimising the entire building requires extensive computational time. However different floor levels require various design decisions because of the performance variances between the ground and sky levels of high-rises in dense urban districts. This paper presents a multi-zone optimisation (MUZO) methodology to support decision-making for an entire high-rise building considering multiple floor levels and performance aspects. The proposed methodology includes parametric modelling and simulations of high-rise buildings as well as machine learning and optimisation as AI methods. The specific setup focuses on the quad-grid and diagrid shading devices using two daylight metrics of LEED: spatial daylight autonomy and annual sunlight exposure. The parametric model generated samples to develop surrogate models using an artificial neural network. The results of 40 surrogate models indicated that the machine learning part of the MUZO methodology can report very high prediction accuracies for 31 models and high accuracies for six quad-grid and three diagrid models. The findings indicate that the MUZO can be an important part of designing high-rises in metropolises while predicting multiple performance aspects related to sustainable buildings during the conceptual design phase. © 2021 Elsevier B.V. All rights reserved.
Citation - WoS: 28
Citation - Scopus: 35
Multi-zone optimisation of high-rise buildings using artificial intelligence for sustainable metropolises. Part 2: Optimisation problems algorithms results and method validation
(Elsevier Ltd, 2021) Berk Ekici; Tugce Kazanasmaz; Michela Turrin; M. Fatih Tasgetiren; I. Sevil Sariyildiz; Ekici, Berk; Turrin, Michela; Tasgetiren, M. Fatih; Sariyildiz, I. Sevil; Kazanasmaz, Z. Tugce
High-rise building optimisation is becoming increasingly relevant owing to global population growth and urbanisation trends. Previous studies have demonstrated the potential of high-rise optimisation but have been focused on the use of the parameters of single floors for the entire design, thus the differences related to the impact of the dense surroundings are not taken into consideration. Part 1 of this study presents a multi-zone optimisation (MUZO) methodology and surrogate models (SMs) which provide a swift and accurate prediction for the entire building design, hence the SMs can be used for optimisation processes. Owing to the high number of parameters involved in the design process the optimisation task remains challenging. This paper presents how MUZO can cope with an enormous number of parameters to optimise the entire design of high-rise buildings using three algorithms with an adaptive penalty function. Two design scenarios are considered for quad-grid and diagrid shading devices glazing type and building-shape parameters using the setup and the SMs developed in part 1. The optimisation part of the MUZO methodology reported satisfactory results for spatial daylight autonomy and annual sunlight exposure by meeting the Leadership in Energy and Environmental Design standards in 19 of 20 optimisation problems. To validate the impact of the methodology optimised designs were compared with 8748 and 5832 typical quad-grid and diagrid scenarios respectively using the same design parameters for all floor levels. The findings indicate that the MUZO methodology provides significant improvements in the optimisation of high-rise buildings in dense urban areas. © 2021 Elsevier B.V. All rights reserved.
Thermal and lighting energy benefits of photovoltaic glass in an architecture studio
(Institute of Electrical and Electronics Engineers Inc., 2022) Aybüke Taser; Zeynep Durmus Arsan; Basak Kundakci Koyunbaba; Tugce Kazanasmaz; Taser, Aybuke; Arsan, Zeynep Durmus; Koyunbaba, Basak Kundakci; Kazanasmaz, Z. Tugce
Buildings are responsible for 40% of the total energy consumption which is critical for global warming. Thus our buildings are expected to be renovated following the zero-energy building (ZEB) strategies. In the context of ZEB strategies renewable energy sources are crucial. It is necessary to understand their role in a nearly-ZEB for future scenarios. This research aims to find out the thermal daylight and energy performance of thin-film amorphous-silicon (a-Si) photovoltaic (PV) glass on an architecture studio of an education building at Izmir Institute of Technology (IZTECH) Campus in Izmir Turkey. Simulation modeling and field measurements have become the methods applied in three scenarios to test the benefits of such a PV glass in terms of thermal and lighting energy consumption and comfort levels. Scenarios included a-Si thin-film modules in three transmittance values modeled in existing windows. Research findings propose that PV glasses have the potential to balance the room's lighting loads in a range between 15.1-and 20.3%. They improved occupant thermal and visual comfort by preventing overheating and glare risks. They also decreased cooling loads. However an essential development could not be achieved in reducing heating loads since new PV glasses absorb less heat due to a lower g-value. © 2022 Elsevier B.V. All rights reserved.