Search Results (978)

Enhancing the real-world performance of sustainably designed and certified green buildings remains a significant challenge, particularly in hot climates where efforts to improve thermal comfort often conflict with energy efficiency goals. In the United Arab Emirates (UAE), even newly constructed facilities with green building certifications present opportunities for retrofitting and performance optimization. This study investigates the energy and thermal comfort performance of a LEED Gold-certified, mixed-use university campus in Dubai through a calibrated digital twin developed using IES thermal modelling software. The analysis evaluated existing sustainable design strategies alongside three retrofit energy conservation measures (ECMs): (1) improved building envelope U-values, (2) installation of additional daylight sensors, and (3) optimization of fan coil unit efficiency. Simulation results demonstrated that the three ECMs collectively achieved a total reduction of 15% in annual energy consumption. Thermal comfort was assessed using operative temperature distributions, Predicted Mean Vote (PMV), and Predicted Percentage of Dissatisfaction (PPD) metrics. While fan coil optimization yielded the highest energy savings, it led to less favorable comfort outcomes. In contrast, enhancing envelope U-values maintained indoor conditions consistently within ASHRAE-recommended comfort zones. To further support energy reduction and progress toward Net Zero targets, the study also evaluated the integration of a 228.87 kW rooftop solar photovoltaic (PV) system, which offset 8.09% of the campus’s annual energy demand. By applying data-driven thermal modelling to assess retrofit impacts on both energy performance and occupant comfort in a certified green building, this study addresses a critical gap in the literature and offers a replicable framework for advancing building performance in hot climate regions. Full article

With the increasing awareness of energy consumption issues, there has been a growing emphasis on energy-saving retrofits for central air-conditioning systems that constitute a significant proportion of energy consumption in buildings. Efficient energy utilization can be achieved by optimizing the modeling of the equipment within the chiller plants of central air-conditioning systems. Traditional modeling approaches have been static and have focused on modeling within narrow time frames when a certain amount of equipment operating data has accumulated, thus prioritizing the precision of the model itself while overlooking the fact that energy-saving retrofits are a long-term process. This study proposes a modeling scheme for the equipment within chiller plants throughout the energy-saving retrofit process. Based on the differences in the amount of available operating data for the equipment and the progress of retrofit implementation, the retrofit process was divided into three stages, each employing different modeling techniques and ensuring smooth transitions between the stages. The equipment within the chiller plants is categorized into two types based on the clarity of their operating characteristics, and two modeling schemes are proposed accordingly. Based on the proposed modeling scheme, chillers and chilled-water pumps were selected to represent the two types of equipment. Real operating data from actual retrofit projects was used to model the equipment and evaluate the accuracy of the model predictions. The results indicate that the models established by the proposed modeling scheme exhibit good accuracy at each stage of the retrofit, with the coefficients of variation (CV) remaining below 6.88%. Furthermore, the prediction accuracy improved as the retrofitting process progressed. The modeling scheme performs better on equipment with simpler and clearer operating characteristics, with a CV as low as 0.67% during normal operation stages. This underscores the potential application of the proposed modeling scheme throughout the energy-saving retrofit process and provides a model foundation for the subsequent optimization of the refrigeration system. Full article

This study empirically evaluates the energy and carbon reduction effects of an envelope retrofit applied to an aging public building in South Korea. Unlike previous studies that primarily relied on simulation-based analyses, this work fills the empirical research gap by using actual utility billing data collected over one pre-retrofit year (2019) and two post-retrofit years (2023–2024). The retrofit included improvements to exterior walls, roofs, and windows, aiming to enhance thermal insulation and airtightness. The analysis revealed that monthly electricity consumption was reduced by 14.7% in 2023 and 8.0% in 2024 compared to that in the baseline year, with corresponding decreases in electricity costs and carbon dioxide emissions. Seasonal variations were evident: energy savings were significant in the winter due to reduced heating demand, while cooling energy use slightly increased in the summer, likely due to diminished solar heat gains resulting from improved insulation. By addressing both heating and cooling impacts, this study offers practical insights into the trade-offs of envelope retrofitting. The findings contribute to the body of knowledge by demonstrating the real-world performance of retrofit technologies and providing data-driven evidence that can inform policies and strategies for improving energy efficiency in public buildings. Full article

Through a systematic review of sustainable rural dwelling recovery, this study offers a broader reflection on retrofitting practices, viewing eco-efficiency as a means to enhance both cultural heritage and agricultural landscapes. The work is based on the assumption that vernacular architecture in rural contexts embodies historical, cultural, and typological values worthy of preservation, while remaining adaptable to reuse through eco-efficient solutions and technological innovation. Using the PRISMA protocol, 115 scientific contributions were selected from 1711 initial records and classified into four macro-groups: landscape relationships; seismic and energy retrofitting; construction techniques and innovative materials; and morphological–typological analysis. Results show a predominance (over 50%) of passive design strategies, compatible materials, and low-impact techniques, while active systems are applied more selectively to protect cultural integrity. The study identifies replicable methodological models combining sustainability, cultural continuity, and functional adaptation, offering recommendations for future operational guidelines. Conscious eco-efficient retrofitting thus emerges as a strategic tool for the integrated valorization of rural landscapes and heritage. Full article

Infrared thermography is a common approach used in building inspection for identifying building envelope thermal anomalies that cause energy loss and occupant thermal discomfort. Detecting these anomalies is essential to improve the thermal performance of energy-inefficient buildings through energy retrofit design and correspondingly reduce operational energy costs and environmental impacts. A thermal bridge is an unwanted conductive heat transfer. On the other hand, an infiltration/exfiltration anomaly is an uncontrollable convective heat transfer, typically happening around windows and doors, but it can also be due to a defect that comprises a building envelope’s integrity. While the existing literature underscores the significance of automatic thermal anomaly identification and offers insights into automated methodologies, there is a notable gap in addressing an automated workflow that leverages building envelope component segmentation for enhanced detection accuracy. Consequently, an automatic thermal anomaly identification workflow from visible and thermal images was developed to test it, utilizing segmented building envelope information compared to a workflow without any semantic segmentation. Therefore, building envelope images (e.g., walls and windows) were segmented based on a U-Net architecture compared to a more conventional semantic segmentation approach. The results were discussed to better understand the importance of the availability of training data and for scaling the workflow. Then, thermal anomaly thresholds for different target domains were detected using probability distributions. Finally, thermal anomaly masks of those domains were computed. This study conducted a comprehensive examination of a campus building in Syracuse, New York, utilizing a drone-based data collection approach. The case study successfully detected diverse thermal anomalies associated with various envelope components. The proposed approach offers the potential for immediate and accurate in situ thermal anomaly detection in building inspections. Full article

This research proposes an integrated lighting and solar shading strategy to improve energy efficiency and user comfort in a retrofit project in a temperate-humid climate. The study examines a future library addition to an existing faculty building in Bursa, featuring highly glazed façades (77% southwest, 81% northeast window-to-wall ratio), an open-plan layout, and situated within an unobstructed low-rise campus environment. Trade-offs between daylight availability, heating, cooling, lighting energy use, and visual and thermal comfort are evaluated through integrated lighting (DIALux Evo), climate-based daylight (CBDM), and energy simulations (DesignBuilder, EnergyPlus, Radiance). Fifteen solar shading configurations—including brise soleil, overhangs, side fins, egg crates, and louvres—are evaluated alongside a daylight-responsive LED lighting system that meets BS EN 12464-1:2021. Compared to the reference case’s unshaded glazing, optimal design significantly improves building performance: a brise soleil with 0.4 m slats at 30° reduces annual primary energy use by 28.3% and operational carbon emissions by 29.1% and maintains thermal comfort per ASHRAE 55:2023 Category II (±0.7 PMV; PPD < 15%). Daylight performance achieves 91.5% UDI and 2.1% aSE, with integrated photovoltaics offsetting 129.7 kWh/m² of grid energy. This integrated strategy elevates the building’s energy class under national benchmarks while addressing glare and overheating in the original design. Full article

Urban areas are responsible for most of Europe’s energy demand and emissions and urgently require building retrofits to meet climate neutrality goals. This study evaluates the energy efficiency potential of three public school buildings in western Macedonia, Greece—a cold-climate region with high heating needs. The buildings, constructed between 1986 and 2003, exhibited poor insulation, outdated electromechanical systems, and inefficient lighting, resulting in high oil consumption and low energy ratings. A robust methodology is applied, combining detailed on-site energy audits, thermophysical diagnostics based on U-value calculations, and a techno-economic assessment utilizing Net Present Value (NPV), Internal Rate of Return (IRR), and SWOT analysis. The study evaluates a series of retrofit measures, including ceiling insulation, high-efficiency lighting replacements, and boiler modernization, against both technical performance criteria and financial viability. Results indicate that ceiling insulation and lighting system upgrades yield positive economic returns, while wall and floor insulation measures remain financially unattractive without external subsidies. The findings are further validated through sensitivity analysis and policy scenario modeling, revealing how targeted investments, especially when supported by public funding schemes, can maximize energy savings and emissions reductions. The study concludes that selective implementation of cost-effective measures, supported by public grants, can achieve energy targets, improve indoor environments, and serve as a replicable model of targeted retrofits across the region, though reliance on external funding and high upfront costs pose challenges. Full article

This current review examines modeling approaches for renovating reinforced concrete (RC) buildings for vertical forest (VF) application, taking into account structural retrofitting, energy systems, forestry integration, and occupant comfort. The study assesses research conducted with an advanced 3D finite element analysis and the use of retrofitting modeling techniques, including textile-reinforced mortar (TRM), fiber-reinforced polymer (FRP), seismic joints, and green concrete applications. The energy system modeling methods are reviewed, taking into account the complexity of incorporating vegetation and seasonal variations. During forestry integration, three main design parameters are identified, namely, root systems, trunks, and crowns, for their critical role in the structural stability and optimal environmental performance. The comfort models are identified evolving from static to adaptive models incorporating thermal, acoustic, visual and air quality parameters. The current review consists of more than one hundred studies indicating that the integration of natural systems to buildings requires a multidimensional and multidisciplinary approach with sophisticated systems. The findings of this review provide the basis for implementing VF models to RC buildings, while highlighting areas requiring further research and validation. Full article

The present study was motivated by the need to enhance indoor air quality and reduce airborne disease transmission in dense urban environments where high-rise residential buildings face challenges in achieving effective natural ventilation. The problem lies in the lack of scalable and convenient tools to optimize natural ventilation rate, particularly in urban settings with varying building heights. To address this, the scientific technique developed with an innovative metric, the urbanized natural ventilation effectiveness factor (uNVeF), integrates regression analysis of wind direction, velocity, air change rate per hour (ACH), window configurations, and building height to quantify ventilation efficiency. By employing a field measurement methodology, the measurements were conducted across 25 window-opening scenarios in a 13.9 m² residential unit on the 35/F of a Hong Kong public housing building, supplemented by the Hellman Exponential Law with a site-specific friction coefficient (0.2907, R² = 0.9232) to estimate the lower floor natural ventilation rate. The results confirm compliance with Hong Kong’s statutory 1.5 ACH requirement (Practice Note for Authorized Persons, Registered Structural Engineers, and Registered Geotechnical Engineers) and achieving a peak ACH at a uNVeF of 0.953 with 75% window opening. The results also revealed that lower floors can maintain 1.5 ACH with adjusted window configurations. Using the Wells–Riley model, the estimation results indicated significant airborne disease infection risk reductions of 96.1% at 35/F and 93.4% at 1/F compared to the 1.5 ACH baseline which demonstrates a strong correlation between ACH, uNVeF and infection risks. The uNVeF framework offers a practical approach to optimize natural ventilation and provides actionable guidelines, together with future research on the scope of validity to refine this technique for residents and developers. The implications in the building industry include setting up sustainable design standards, enhancing public health resilience, supporting policy frameworks for energy-efficient urban planning, and potentially driving innovation in high-rise residential construction and retrofitting globally. Full article

The impacts of renovation actions on pre- and post-retrofitting building performances are complex to analyse, particularly small and potentially self-actuated actions, such as adding insulation layers to a cold roof slab or changing doors. These interventions are widespread in small residential houses and cases where the owners are the residents. However, a large research gap currently remains regarding the impact of sustainable solutions on building performance. This study aims to address this issue by proposing a methodology based on commercial cloud monitoring solutions and middleware development that analyses and reports on the impact of such solutions to end users, allowing for an analysis of real variations in air temperature levels. The methodology is applied to two single/double-family residential houses, acting as demo cases for verification, across a multi-year time horizon. In both cases, measurements were conducted before and after typical limited renovation actions. Alongside the proposed methodology, descriptions of the smart solutions’ requirements are provided. The results mainly focus on temperature variations. Finally, the impact of the solutions on energy consumption was analysed for one of the buildings, and feedback was briefly provided by the users. Full article

The transition to a decarbonised energy system in Ireland presents significant socio-technical challenges. This paper, focused on the work of the SMARTLAB project at the Citizen Innovation Lab in Limerick city, investigated the potential of a localised living lab approach to address these challenges. Engaging across 70 buildings and their inhabitants, the project captured the evolution of attitudes and intentions towards the clean energy transition in ways directly relevant to future policy implementation across grid redevelopment, smart service design, and national retrofit. Project methodology was framed by a living lab approach, with wireless energy and indoor environment sensors installed in participant buildings and participant journeys developed by harnessing the Citizen Innovation Lab ecosystem. The results indicate behaviour changes among participants, particularly focusing on indoor environmental conditions. The study concludes that embedded, localised living labs offer a methodological framework which can capture diverse datasets and encompass complex contemporary contexts towards transition goals. Full article

As climate change intensifies the frequency and severity of extreme weather events, the urgency for resilient and sustainable urban development becomes increasingly critical. This study investigates the role of digital twins in advancing climate-responsive urban strategies, with a focus on their integration into zero-energy buildings (ZEBs) and smart city frameworks. A systematic literature review was conducted following PRISMA guidelines, covering 1000 articles initially retrieved from Scopus and Web of Science between 2014 and 2024. After applying inclusion and exclusion criteria, 70 full-text articles were analyzed. Bibliometric analysis using VOSviewer revealed five key application areas of digital twins: energy efficiency optimization, renewable energy integration, design and retrofitting, real-time monitoring and control, and predictive maintenance. The findings suggest that digital twins can contribute to up to 30–40% improvement in building energy efficiency through enhanced performance monitoring and predictive modeling. This review synthesizes trends, identifies research gaps, and contextualizes the findings within the Middle Eastern urban landscape, where climate action and smart infrastructure development are strategic priorities. While offering strategic guidance for urban planners and policymakers, the study also acknowledges limitations, including the regional focus, lack of primary field data, and potential publication bias. Overall, this work contributes to advancing digital twin applications in climate-resilient, zero-energy urban development. Full article

Heritage building conservation plays a special role in addressing modern sustainability challenges by preserving the cultural identity, retrofitting, restoring, and renovating these structures to improve energy performance, which is crucial for revitalisation. This research aims to use Heritage Building Information Modelling (HBIM) to increase energy efficiency and environmental sustainability in historic buildings. Retrofitting heritage buildings presents unique challenges and opportunities to simultaneously reduce energy consumption and carbon emissions while maintaining historical integrity. Traditional approaches are often insufficient to meet heritage structures’ energy needs. Modern technologies such as information building modelling and energy simulations can offer solutions. HBIM is a vigorous digital framework that facilitates interdisciplinary collaboration and offers detailed insights into building restoration and energy modelling. HBIM supports the integration of thermal and energy efficiency measures while maintaining the authenticity of heritage architecture by creating a comprehensive database. Using a case study heritage building, this research demonstrates how retrofitting the different aspects of heritage buildings can improve energy performance. Evaluating the preservation of heritage buildings’ cultural and architectural values and the effectiveness of using HBIM to model energy performance offers a viable framework for sustainable retrofitting of heritage buildings. Full article

The architectural, engineering, construction, and operation (AECO) sector is one of the main contributors to energy consumption and greenhouse gas emissions in Europe, making the renovation of the existing building stock a priority. However, defining effective and economically sustainable interventions remains a challenge, partly due to the variability of building characteristics and the lack of digital tools to support data-driven decision making. This research aims to identify the main factors influencing the energy consumption of buildings by analyzing a large database of building characteristics using machine learning algorithms. Based on the parameters that the analysis shows to have the greatest impact, the average cost of energy retrofitting measures will be used to elaborate a cost–benefit analysis model and the economic payback time for each measure, individually or in combination. The expected result is the creation of a tool that will allow the operator to evaluate the choice of interventions based on the energy efficiency that can be achieved and/or the economic sustainability. The proposed methodology aims to provide a digital approach that is replicable and adaptable to different territorial realities and useful for strategic planning of energy transformation in the building sector. Full article

Assessing energy consumption in existing old residential buildings is key for urban energy conservation and decarbonization. Previous studies on old residential building energy assessment face challenges due to data limitations and inadequate prediction methods. This study develops a novel approach integrating building energy simulation and machine learning to predict large-scale old residential building energy use using multi-source datasets. Using Guangzhou as a case study, open-source building data was collected to identify 31,209 old residential buildings based on age thresholds and areas of interest (AOIs). Key building form parameters (i.e., long side, short side, number of floors) were then classified to identify residential archetypes. Building energy consumption data for each prototype was generated using EnergyPlus (V23.2.0) simulations. Furthermore, XGBoost and Random Forest machine learning algorithms were used to predict city-scale old residential building energy consumption. Results indicated that five representative prototypes exhibited cooling energy use ranging from 17.32 to 21.05 kWh/m², while annual electricity consumption ranged from 60.10 to 66.53 kWh/m². The XGBoost model demonstrated strong predictive performance (R² = 0.667). SHAP (Shapley Additive Explanations) analysis identified the Building Shape Coefficient (BSC) as the most significant positive predictor of energy consumption (SHAP value = 0.79). This framework enables city-level energy assessment for old residential buildings, providing critical support for retrofitting strategies in sustainable urban renewal planning. Full article