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: 77Citation - Scopus: 95Thermal, Daylight, and Energy Potential of Building-Integrated Photovoltaic (bipv) Systems: a Comprehensive Review of Effects and Developments(Elsevier, 2023) Taşer, Aybüke; Kundakçı Koyunbaba, Başak; Kazanasmaz, Zehra TuğçeAccording to energy consumption data of the European Union, buildings account for 40 % of overall energy consumption in all sectors. The rise in building energy demand seriously affects global warming. To reduce demand, buildings must be designed to be energy-efficient. As part of energy-efficiency initiatives, unique systems that employ renewable energy sources should be implemented in buildings. As a new technology, building-integrated photovoltaics is considered an essential technology to achieve this target. Several variables affect the thermal, daylight, and energy performance of building-integrated photovoltaic systems; related to environmental and photovoltaic-related parameters. Thus, the challenges and effects of these variables on the overall performance of these systems should be investigated. This research analyzes building-integrated photovoltaic implemented studies and presents a state-of-art review of recent developments. The study not only summarizes the existing studies developed in this field so far but also analyzes the variables and makes concrete generalizations and inferences. It enables finding gaps and deficiencies in the literature and provides a better understanding of all the variables that affect the performance of building-integrated photovoltaic systems by interpreting the results in detail and representing them graphically instead of only through textual analysis. Results show that building-integrated photovoltaics contribute to constructing a sustainable future for cities. Developments in this industry motivate researchers in this field, whose work will make it easier to cope with future ecological challenges. It helps to build a more sustainable future for society. With new developments, it will be possible to mitigate the effects of future environmental problems.Article Citation - WoS: 27Citation - Scopus: 34Multi-Zone Optimisation of High-Rise Buildings Using Artificial Intelligence for Sustainable Metropolises. Part 2: Optimisation Problems, Algorithms, Results, and Method Validation(Pergamon-Elsevier Science LTD, 2021) Ekici, Berk; Kazanasmaz, Zehra Tuğçe; Turrin, Michela; Taşgetiren, M. Fatih; Sarıyıldız, I. SevilHigh-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.Article Citation - WoS: 38Citation - Scopus: 50Multi-Zone Optimisation of High-Rise Buildings Using Artificial Intelligence for Sustainable Metropolises. Part 1: Background, Methodology, Setup, and Machine Learning Results(Elsevier Ltd., 2021) Ekici, Berk; Kazanasmaz, Zehra Tuğçe; Turrin, Michela; Taşgetiren, M. Fatih; Sarıyıldız, I. SevilDesigning 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 The Author(s)Article Citation - WoS: 21Citation - Scopus: 27Exploring the Impact of External Shading System on Cognitive Task Performance, Alertness and Visual Comfort in a Daylit Workplace Environment(SAGE Publications Inc., 2019) Leccese, F.; Salvodori, G.; Öner, Merve; Kazanasmaz, Zehra TuğçeThe authors examined the effect of external shading system on cognitive performance, alertness and visual comfort of visual display terminal (VDT) users under two realistic office lighting settings in this study. Daylight was the source of illumination being considered as the most significant and preferred one. A total of 26 participants performed visual and cognitive demanding tasks as well as providing subjective alertness, performance and visual evaluations in a full-scale mock-up VDT workstation. Two trials (with and without shading system) were executed during one experimental session. Results revealed that the use of a shading system improves the performance of a user on colour-naming task requiring sustained attention, while no differential effects were observed on tasks involving other cognitive skills such as search velocity and vigilance. Within-subject performance differences were more pronounced during morning hours. Higher performance was reported in some cognitive tests when the subjective sensation of visual discomfort was lower.Article Citation - WoS: 7Citation - Scopus: 9Fuzzy Logic Model for the Categorization of Manual Lighting Control Behaviour Patterns Based on Daylight Illuminance and Interior Layout(SAGE Publications Inc., 2019) Cılasun Kunduracı, Arzu; Kazanasmaz, Zehra TuğçeIn 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.Article Citation - WoS: 40Citation - Scopus: 51Three Approaches To Optimize Optical Properties and Size of a South-Facing Window for Spatial Daylight Autonomy(Elsevier Ltd., 2016) Kazanasmaz, Zehra Tuğçe; Grobe, Lars Oliver; Bauer, Carsten; Krehel, Marek; Wittkopf, StephenThis study presents optimization approaches by a recent Climate-Based-Daylight-Modeling tool, EvalDRC, to figure out the necessary area for a daylight redirecting micro-prism film (MPF) while minimizing the glazing area. The performance of a window in terms of spatial Daylight Autonomy (sDA) is optimized by its geometry and optical properties. Data implemented in simulation model are gathered through on-site measurements and Bidirectional-Scattering Distribution Function (BSDF) gonio-measurements. EvalDRC based on Radiance with a data driven model of the films' BSDF evaluates the window configurations in the whole year. The case to achieve an sDA of at least 75% is a South-facing window of a classroom in Switzerland. A window zone from 0.90 m to 1.80 m height provides view to the outside. The upper zone from 1.80 m to 3.60 m is divided into six areas of 0.30 m height in three optimization approaches including the operation of sunshades as well. First, the size of the clear glazing is incrementally reduced to find the smallest acceptable window-to-wall ratio (WWR). Second, micro-prism films are applied to an incrementally varying fraction the initial glazed area to determine the minimum film-to-window ratio (FWR). Finally, both approaches are combined for a minimum FWR and WWR. With clear glazing and WWR of 75%, the sDA of 70.2% fails to meet the requirements. An sDA of 86.4% and 80.8% can be achieved with WWR 75%, FWR 1/9 and WWR 50%, FWR 1/2 respectively. The results demonstrate the films' potential to improve the performance of windows with reduced WWR.Article Citation - WoS: 96Citation - Scopus: 105Comparative Study of a Building Energy Performance Software (kep-Iyte and Ann-Based Building Heat Load Estimation(Elsevier Ltd., 2014) Turhan, Cihan; Kazanasmaz, Zehra Tuğçe; Erlalelitepe Uygun, İlknur; Ekmen, Kenan Evren; Gökçen Akkurt, GüldenThe several parameters affect the heat load of a building; geometry, construction, layout, climate and the users. These parameters are complex and interrelated. Comprehensive models are needed to understand relationships among the parameters that can handle non-linearities. The aim of this study is to predict heat load of existing buildings benefiting from width/length ratio, wall overall heat transfer coefficient, area/volume ratio, total external surface area, total window area/total external surface area ratio by using artificial neural networks and compare the results with a building energy simulation tool called KEP-IYTE-ESS developed by Izmir Institute of Technology. A back propagation neural network algorithm has been preferred and both simulation tools were applied to 148 residential buildings selected from 3 municipalities of Izmir-Turkey. Under the given conditions, a good coherence was observed between artificial neural network and building energy simulation tool results with a mean absolute percentage error of 5.06% and successful prediction rate of 0.977. The advantages of ANN model over the energy simulation software are observed as the simplicity, the speed of calculation and learning from the limited data sets.Article Citation - WoS: 23Citation - Scopus: 26On the Relation Between Architectural Considerations and Heating Energy Performance of Turkish Residential Buildings in Izmir(Elsevier Ltd., 2014) Kazanasmaz, Zehra Tuğçe; Erlalelitepe Uygun, İlknur; Gökçen Akkurt, Gülden; Turhan, Cihan; Ekmen, Kenan EvrenBy considering the energy efficiency legislations among the European Union, Turkey is responsible to provide regulations to comply for the latest European Energy Performance of Buildings Directive 2010/31/EC. New legislation in Turkey requires information about the evaluation of energy performance of existing buildings. This study aimed to determine energy performance of residential buildings in Izmir, regarding significant relationships between their performance and architectural configuration through statistical analysis. The focus was on the heating energy consumption due to Energy Efficiency Law (2007) and Building Energy Performance Regulation (2008), and Standard Assessment Method for Energy Performance of Dwellings (KEP-SDM). This energy performance assessment method was based on Turkish standard TS 825, and European standard EN ISO 13790. It is known that architectural configuration of buildings and design norms have impact on energy performance of buildings. However, emphasis was given on significant values of architectural considerations through certain area-based ratios. The levels of these ratios were matched with the levels of energy consumption. By this, the consideration was to take early-precautions against high energy consumptions in the early design stage and to enhance legislation by adding recommendations of concrete architectural values. These would assist to predict the level of energy performance in the early design phase. Findings would provide feedback information on the residential building stock in İzmir, Turkey.Article Citation - WoS: 3Citation - Scopus: 3The Impact of Planimetric Configuration on Structurally Damaged Residential Buildings(Taylor and Francis Ltd., 2009) Kazanasmaz, Zehra TuğçeThis study was conducted to determine a significant relationship between planimetric configuration and vulnerability of hazardous buildings located in seismic zones by developing design and construction efficiency indicators. Case study examples were chosen from residential buildings in Bolu, Düzce and Kaynasli in Turkey, which were damaged by the 1999 earthquakes. Utilizing field survey drawings, efficiency quotients; compactness quotients; construction efficiency ratios; aspect ratios and height-to-base ratios were defined as planimetric configuration indicators. The significant relationship between these aspects and the damage level of buildings were determined through statistical analyses and scatter charts. Planimetric configuration - including building geometry, cantilever projections and layout of columns -was reviewed according to the Turkish Earthquake Code. Findings revealed certain dependencies for efficiency ratios, which would satisfactorily predict the seismic vulnerability of buildings based on planimetric configuration. Researchers in the field of architecture who are engaged in earthquake-resistant design may use the general methodology. In addition, architects and structural engineers can use this approach presented here to evaluate their design.Article Citation - WoS: 91Citation - Scopus: 122Artificial Neural Networks To Predict Daylight Illuminance in Office Buildings(Elsevier Ltd., 2009) Kazanasmaz, Zehra Tuğçe; Günaydın, Hüsnü Murat; Binol, SelcenA 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.