Gonca Özer

The increasing pace of urbanization and climate change necessitate a holistic assessment of building energy performance during the early design phase. This study proposes an Evolutionary Field Optimization (EFO)-based multi-input multi-output artificial neural network (MIMO-ANN) model to simultaneously predict the heating load, cooling load, CO₂ emissions, and lighting energy consumption of smart buildings. The model’s dataset consists of 7963 observations generated via EnergyPlus building energy simulations of standardized TOKİ residential units constructed post-earthquake in Türkiye. No operational or physically measured building energy consumption data were used in the model development process. For the validation setting, the simulation-generated dataset was split into training (60%), validation (10%), and test (30%) subsets. The EFO algorithm was employed to automatically optimize the ANN architecture by dynamically determining the optimal number of hidden layers and neurons. The optimization process demonstrated strong global search capability and fast convergence, reducing the objective function by approximately 86% within 10 iterations. Experimental results on the test subset showed exceptional predictive accuracy for simulation data, with test R² values ranging from 0.9996 to 0.9998 across all four outputs, indicating that the optimized network topology effectively avoided overfitting. While the model’s performance under real-world operational uncertainties and varying occupant behaviors remains to be fully investigated, the proposed EFO-ANN framework provides a computationally efficient and highly accurate analytical core for early-stage design. It serves as a strategic decision-support tool intended for architects and engineers designing post-disaster housing, public authorities forming national energy efficiency policies, and developers building predictive engines for digital twin-enabled smart building systems.

The architectural design process is a multi-layered production process in which abstract ideas are transformed into spatial organizations. One of the fundamental components of this process is geometry. In architecture, geometry is not only a formal representational tool but also a fundamental design instrument that shapes spatial organization, structural systems, user experience, and environmental performance. Basic geometric elements such as points, lines, surfaces, and volumes constitute the building blocks of architectural form, while the relationships among these elements define the holistic structure of spatial organization.

Designing energy-efficient buildings for reconstruction in disaster areas is essential for mitigating environmental challenges and supporting sustainable recovery. However, in Türkiye, standardized mass housing is often implemented following disasters for rapid reconstruction, frequently overlooking local climatic conditions. The Housing Development Administration of Türkiye (TOKİ) has initiated standardized mass housing projects across 11 provinces in the disaster area of the 2023 Türkiye Kahramanmaraş earthquake. This study conducts a comparative analysis of heating and cooling energy consumption and CO₂ emissions for a reference TOKİ housing using DesignBuilder simulations across the 11 impacted provinces, to show the environmental impact of standardized material selection. The evaluated archetype features a rectangular geometry with a symmetrical floor plan, comprising 20 dwelling units and a fixed façade configuration governed by orientation. The façade consists of reinforced concrete shear walls, vertically perforated brick walls, and PVC-framed double-glazed windows. The analysis utilized the updated climate zoning of the Turkish Standard for Thermal Insulation in Buildings (TS 825), which recently expanded from four to six climate zones. Findings indicate significant variations: intra-zone annual heating and cooling energy consumption differed by up to 184.85% and 52.84%, respectively. CO₂ emissions showed relatively limited differences, with a maximum of 12.35% intra-zone. These findings show that energy consumption varies significantly both between and within climate zones, highlighting the limitations of relying solely on generalized climate zone data for building design and material selection. Moreover, an evaluation of the latest TS 825 revision shows that the updated classification does not yield consistent heating and cooling energy outcomes for certain provinces. The study concludes that province-specific climatic data should be integrated into post-disaster housing design, particularly during material selection, to improve energy efficiency, reduce emissions, and support sustainable recovery.

Post-disaster permanent housing plays a critical role in long-term recovery. In cold-climate regions like Bingöl, Türkiye, such housing must ensure adequate thermal comfort and indoor air quality (IAQ) to support occupant well-being. This study evaluates the environmental performance of a representative single-storey post-earthquake housing unit constructed after the 2003 Bingöl earthquake. Field measurements were conducted between December 2023 and February 2024 in five rooms, recording temperature, relative humidity, air velocity, CO² concentrations and PM (PM₁–₁₀) levels. Results revealed that most rooms failed to meet ASHRAE 55 winter comfort thresholds, with temperature deviations exceeding 3.5°C in under-heated spaces. Stoves improved thermal comfort but elevated CO₂ and PM concentrations, indicating trade-offs between comfort and IAQ. Dynamic simulations using DesignBuilder tested envelope retrofit scenarios including insulation and glazing upgrades. Simulations showed a 7.1% reduction in discomfort hours, 60% lower heating energy demand, and a 12.3% drop in CO₂ emissions. These improvements were cost-effective and readily applicable in post-disaster contexts. This study contributes by combining field-based environmental data with simulation modelling to assess both thermal comfort and IAQ in cold-climate post-disaster housing. The findings inform resilient and sustainable housing strategies for future disaster recovery efforts.

Many design parameters influence the energy demand of buildings. While research on building energy performance typically emphasizes physical design parameters and the technical characteristics of buildings, the behavior, actions, duration, and intensity of use by occupants are also critical factors impacting energy performance. Buildings should be constructed with functions aligned to the intended actions within spaces, ensuring user needs are considered during the design process. In this study, the variation in building energy performance was analyzed based on 9 different building functions and 3 climate types. Building energy models were developed using the BIM-BES methodology, and energy simulations were conducted using DesignBuilder software. The simulation results were analyzed through graphs, and energy performance characteristics were compared. Regression analyses were performed in Excel to explore the linear relationship between energy performance and occupant-related parameters, while correlation matrices were prepared and analyzed in Python. The findings revealed energy differences exceeding 80% due to variations in climate parameters and building functions. These results emphasize the critical importance of location-specific design and the integration of user needs during the design phase. This study highlights the necessity of user-centered approaches to improve building energy performance, supported by numerical data.

In terms of ensuring the sustainability of vernacular building culture, the evaluation of buildings should consider not only visual and cultural values but also energy efficiency, environmental impact, and indoor thermal comfort. This study comparatively examines the performance of stone and adobe wall materials, widely used in Anatolia, under different climatic conditions. In the simulations conducted using DesignBuilder software, building geometry and indoor use scenarios were kept constant, while only exterior wall material and climate data were treated as variables. Annual data for the year 2023 were analyzed. The findings indicate that adobe-walled structures stand out in hot and transitional climates with lower heating and cooling energy demands, reduced electricity consumption, lower carbon emissions, and better thermal comfort conditions. In Kars, representing a cold continental climate, both materials remained outside comfort thresholds; however, adobe structures performed better in terms of energy use, environmental impact, and thermal comfort. This comprehensive evaluation highlights the potential of climate-responsive use of local materials and offers valuable contributions to design strategies focused on sustainability and cultural heritage. The results present not only context-specific insights for Anatolia but also universally applicable, generalizable recommendations for other regions with similar climatic conditions and vernacular building cultures.

This study examines the spatial perceptions and expectations of students regarding collective living spaces on the campus of Bingöl University in Turkey. Utilizing a mixed-methods approach that integrates quantitative survey data with qualitative open-ended responses, the research investigates the relationship between flexible and multifunctional spatial designs and user experience. Statistical analyses reveal that students prioritize spaces that can adapt to both individual study and social interaction. Significant differences in spatial preferences were identified based on variables such as academic disciplines, year of study, and the amount of time spent on campus. Students emphasized design criteria such as sustainability, digital integration, comfort, and cultural identity. Based on these findings, eight conceptual spatial scenarios were developed to guide campus planning. This research not only contributes to the literature on campus planning within a specific context but also offers applicable recommendations for inclusive and student-centered designs in alignment with contemporary architectural principles.

Improving energy efficiency, reducing consumption, and enhancing indoor thermal comfort are key concerns in sustainable architecture. While much research has addressed minimizing heating demands during winter, fewer studies have explored strategies to improve thermal comfort and reduce cooling loads during summer. This study aims to bridge that gap by analyzing the combined effects of window opening ratios, solar shading devices, and HVAC systems on summer energy performance and indoor comfort in faculty offices at Bingöl University. The hypothesis suggests that optimizing window openings, implementing suitable shading strategies, and selecting effective HVAC systems can significantly enhance thermal comfort and lower cooling energy use. The study explores four main questions: (1) How effective is natural ventilation through varying window openings? (2) How much can solar shading reduce overheating and cooling loads? (3) How do mechanical systems interact with passive design strategies? (4) What is the combined effect of all three parameters on performance? A parametric simulation approach was applied using DesignBuilder software. Scenarios included window openings from 5% to 50%, ten solar shading configurations, and five HVAC types. A total of 498 simulations generated a robust dataset for performance analysis. Results show that integrated optimization can reduce cooling energy use by up to 62% and improve thermal comfort by up to 54% compared to the base case. These findings confirm the initial hypothesis and underscore the value of holistic design strategies. In conclusion, this research offers a structured framework for improving summer thermal performance in educational office spaces. It provides actionable insights for architects, engineers, and policymakers seeking to enhance indoor environmental quality and energy efficiency in warm climate zones.

With a focus on reducing building energy consumption, approaches that simultaneously optimize multiple passive design parameters in industrial buildings have received limited attention. Most existing studies tend to examine building geometry or individual design parameters under limited scenarios, underscoring the potential benefits of adopting a comprehensive, multiparameter approach that integrates climate-responsive and sustainable design strategies. This study bridges that gap by systematically optimizing key passive design parameters—building geometry, orientation, window-to-wall ratio (WWR), and glazing type—to minimize energy loads and enhance sustainability across five distinct climate zones. Fifteen different building geometries with equal floor areas and volumes were analyzed, considering fifteen glazing types and multiple orientations varying by 30° increments. DesignBuilder simulations yielded 16,900 results, and due to the inherent challenges in directly optimizing building geometry within simulation environments, the data were restructured to reveal underlying relationships. An Energy Performance Optimization Model, based on a Particle Swarm Optimization (PSO) algorithm integrated with an Artificial Neural Network (ANN), was developed to identify optimal design solutions tailored to specific climatic conditions. The optimization results successfully determined the optimal combinations of building geometry, orientation, WWR, and glazing type to reduce heating and cooling loads, thereby promoting energy efficiency and reducing carbon emissions in industrial buildings. This study offers a practical design solution set and provides architects with actionable recommendations during the early design phase, establishing a machine learning-based framework for achieving sustainable, energy-efficient, and climate-responsive industrial building designs.