Search Results (1,624)

Accurate evaluation of indoor daylighting performance is essential for improving visual comfort and reducing lighting energy use in office buildings. However, simulation-based daylighting analysis is often too time-consuming to support rapid comparison of multiple design options in early-stage design. To address this issue, this study proposes MTL-Light, an explainable chained multi-task learning framework for fast daylighting performance prediction in typical office units. A parametric simulation dataset was constructed, and multiple representative daylighting indicators were extracted from the spatial distribution of daylight factors on the work plane. MTL-Light was then developed to jointly predict these indicators by modeling their interdependencies within a lightweight multi-task learning architecture. In addition, SHAP was employed to interpret the prediction results by quantifying the marginal contributions of geometric design variables. The results show that, compared with single-task models, MTL-Light achieves higher accuracy and more stable performance across multiple indicators, particularly for metrics sensitive to spatial distribution. Moreover, it reduces daylighting evaluation from minute-level simulation to millisecond-level inference. The interpretability analysis further indicates that room depth and window geometry dominate daylighting performance, while different indicators exhibit different sensitivities to geometric variables. Full article

Building details are often treated as technical externalities, subordinate to form, image and architectural narrative. Reading details as liminal spaces reverses that hierarchy. The joint concentrates transitions between the inside and outside, public and private, exposure and protection, and these transitions are constructed as intervals, experienced through thickness, reveal, edge condition, shadow, touch, and the small resistances that accompany crossing. The article develops its analysis through archival hand-drawn detail drawings from the Azrieli Architectural Archive. It defines building details as both technical assemblies and threshold devices, points where architecture becomes accountable to perception as well as to climate, labor, regulation, and everyday use. A semiotic reading of large-scale sheets shows how line weight, hatching, notation, and layout encode priorities, marking boundaries between what must be precisely resolved and what may remain adjustable. The archive is treated as a laboratory of “detail families,” recurring junction types such as windows, stairs, and envelope edges that reveal office-specific languages of joining. Two case studies, by the architects Ram Karmi and Arieh Sharon with Eldar Sharon, show how micro-variations in depth, overlap, and edge control tune thresholds, producing perceptual tipping points where comfort can shift into irritation, calm into unease, and openness into vulnerability. Although grounded in a local archive, the argument addresses a broader condition of contemporary practice: standardization and digital production chains can relocate authorship and responsibility away from the joint, precisely where buildings most affect everyday conduct. The paper proposes a liminal literacy of detailing as both a historiographic method and a design ethic aimed at making threshold decisions legible, contestable, and accountable in present-day workflows. Full article

This study investigates integrating an air-to-water heat pump (HP) into an existing office building served by a district heating (DH) system to improve energy performance and reduce environmental impact. The system was modelled using Polysun software, considering two operating scenarios: a conventional configuration based solely on DH and a hybrid configuration combining DH with a HP. The analysis was performed using hourly simulations over a typical meteorological year, allowing a detailed evaluation of system behaviour under varying climatic conditions. The results indicate that the hybrid system reduces total energy consumption by approximately 24%, while natural gas consumption decreases by about 36%. Although electricity consumption increases due to HP operation, the overall energy performance is significantly improved. The HP operates efficiently within the analysed temperature range, with COP values ranging from 1.8 to 3.0 and a seasonal performance coefficient of approximately 3.6. The system ensures full coverage of the heating demand, with a negligible deficit, confirming appropriate sizing and control strategy. From an environmental perspective, the hybrid configuration results in approximately 29 t CO₂ per year less than the conventional system. These results demonstrate that integrating HPs into existing DH systems can represent a viable solution for similar buildings under comparable operating conditions. Beyond the quantified energy and environmental benefits, the novelty of the study lies in evaluating a hybrid solution under real operating conditions affected by DH instability. The results highlight practical implications for system resilience, operational flexibility, and the applicability of this retrofit strategy to existing buildings connected to conventional DH networks. Full article

Window glazing systems play a critical role in building energy performance, particularly in office buildings with large window-to-wall ratios, which introduce complex trade-offs between energy consumption, thermal comfort, and visual comfort. This study develops a two-stage optimization-assessment framework to assess glazing performance across six climate regions defined by the TS 825 standard in Türkiye. In the first stage, a genetic algorithm-based multi-objective optimization approach was employed to minimize annual energy consumption (heating, cooling, and daylight-linked lighting) and thermal discomfort hours. In the second stage, the resulting Pareto-optimal solutions were further evaluated and ranked according to spatial disturbing glare (sDG) performance using annual glare simulations. The results show that energy-optimal solutions are not necessarily visually acceptable, highlighting the limitations of single-criterion approaches. While static low-e glazing provides competitive energy performance under several climate conditions, it may lead to increased glare risk, particularly at high window-to-wall ratios and in sun-exposed orientations. Dynamic glazing systems, although not consistently superior in energy terms, offer a more balanced performance when glare is considered, especially in colder climates. These findings emphasize the need for a climate-based, multi-criteria, and integrated approach to glazing selection. Full article

A framework was developed in this study for setting and adjusting energy-saving targets for existing public-sector office buildings. Using self-reported energy data, we removed outliers and grouped buildings by average daily operating hours. We analyzed electricity use intensity distributions and assigned reduction rates based on each building’s percentile within its group, allowing for larger improvements from high-consumption buildings while limiting pressure on already efficient ones. The framework achieved an average annual energy-saving effect of about 1% and can inform future revisions of energy management policies and target values for public office buildings. Full article

This study presents a coupled building simulation framework that evaluates thermal and daylight performance concurrently within a unified multi-objective decision space. Unlike conventional sequential workflows, where daylight metrics are assessed after energy optimization or used primarily for compliance verification, the proposed approach embeds EnergyPlus and Radiance simulations directly within the same optimization loop. This structure enables a systematic exploration of non-linear interactions between Energy Use Intensity (EUI), cooling loads, Spatial Daylight Autonomy (SDA), and Annual Sunlight Exposure (ASE) during early-stage façade design. The framework is demonstrated through a medium-rise office building in India’s National Capital Region, a composite climate characterized by strong seasonal and directional variability. Parametric variation in façade orientation, window-to-wall ratio, and external shading configurations was explored using a multi-objective genetic algorithm to identify Pareto-optimal performance regimes. The results reveal distinct orientation-dependent trade-off structures between solar exposure, cooling demand, and daylight availability that are not evident in rule-based or sequential simulation approaches. In particular, a transitional East-facing façade regime emerges in which balanced shading and glazing proportions achieve near–North-facing cooling performance while maintaining high daylight autonomy under controlled sunlight exposure. Rather than proposing a single optimal solution, the study demonstrates how tightly coupled thermal–daylight simulation can function as a knowledge-discovery tool, enabling the extraction of transferable façade response patterns from simulation outputs. The findings highlight the limitations of prescriptive orientation hierarchies in composite climates and illustrate the value of integrated simulation workflows for performance-driven early-stage design across diverse climatic contexts. Although the study references thermal performance, the optimization objectives are limited to peak cooling load and annual Energy Use Intensity (EUI). Occupant comfort indices such as Predicted Mean Vote (PMV) and Predicted Percentage Dissatisfied (PPD) were not explicitly simulated. Therefore, results are interpreted as energy–daylight performance optimization rather than direct thermal comfort optimization. Full article

Forecasting and intelligent operation of HVAC systems in public buildings are crucial for energy conservation, carbon reduction, and enhancing occupant comfort. This area has seen significant progress. However, for intermittent HVAC systems, noise reduction techniques applied to periodic load sequences may distort the load during start–stop transitions. To tackle this challenge, an innovative hybrid prediction method, GCMWSG-HOA-LightGBM, is proposed in this study. The GCMWSG filter utilizes period division and boundary protection mechanisms to avoid cross-period coupling errors in traditional filters, preserving the authenticity of cooling load start–stop transitions. Additionally, the HOA is employed to automatically optimize the hyperparameters of the LightGBM model, enhancing both parameter tuning efficiency and prediction accuracy. The proposed model is validated using operational data from an office building in Foshan, Guangdong Province, and its performance is compared with several hybrid models: HOA-LightGBM, BWO-LightGBM, AO-LightGBM, TPE-LightGBM, HOA-RF, BWO-RF, AO-RF and TPE-RF. The results show that the GCMWSG-HOA-LightGBM model outperforms all comparison models across all metrics, achieving R², MAPE, and CVRMSE values of 92.22%, 7.05%, and 9.46%, respectively, highlighting its accuracy and stability. The findings provide an efficient solution for intelligent load prediction of HVAC systems with intermittent and periodic characteristics, offering valuable engineering insights and theoretical guidance. Full article

Heritage buildings constitute a significant element of the United Kingdom’s (UK) built environment, with 460,000 listed buildings across England, Scotland, Wales and Northern Ireland. These assets present substantial challenges for national decarbonisation due to statutory constraints on fabric alteration and the need to consider whole-life carbon impacts. This study evaluates the impact of conservation-compatible retrofit strategies on the operational energy and carbon performance of Fitzroy House, a Grade II listed late-modern office building in Nottingham. Dynamic building simulation (IES Virtual Environment) was used to assess baseline performance and to develop two retrofit scenarios incorporating improvements to glazing, airtightness, roof insulation, and the introduction of mechanical ventilation with heat recovery (MVHR). Climate resilience was evaluated using future weather files for the 2080s. Results are derived from comparative scenario-based modelling rather than calibrated predictions of absolute performance. Within this framework, the proposed measures can reduce annual heating demand by up to 68%, cooling demand by 60%, and operational carbon emissions by approximately 41% (district heating) to 64% (natural gas), relative to the as-built baseline under the most advanced retrofit scenario. Performance remains broadly robust under future climate scenarios, although cooling loads increase modestly. The findings demonstrate that, while meaningful reductions in operational carbon are achievable, retrofit outcomes are fundamentally shaped by conservation constraints, which act as an interpretive framework defining the limits and possibilities of intervention. However, results should be interpreted as indicative of relative performance improvements rather than fully generalizable or predictive outcomes, and embodied carbon impacts are not included within the scope of this study. The research provides an evidence-based pathway for improving similar late-modern listed office buildings while highlighting the limits imposed by conservation requirements and existing building fabric. Full article

Addressing the prevalent issues of scattered data sources, reliance on multi-software collaboration, and low integration efficiency between Building Information Modeling (BIM) and Life Cycle Assessment (LCA) in current building life cycle carbon emission accounting, this study aims to construct a BIM-driven, data-traceable automated method for building life cycle carbon accounting. This paper proposes a life cycle carbon accounting framework based on Revit secondary development. By defining unified data mapping rules and constructing a scalable localized carbon emission factor database, this framework achieves a seamless workflow from BIM model information extraction and intelligent factor matching to phased accounting and report generation. Taking an office building in Nanning as an empirical case study, the results indicate that the operational stage and the building material production stage are the primary emission sources, accounting for 78.82% and 24.13% of the total emissions, respectively; the transportation stage accounts for 1.68%; the construction stage accounts for 0.40%; and the demolition and recycling stage exhibits negative emissions of −3.53% due to material recovery benefits. The accounting results of the developed plugin exhibit a relative error of 6.67% compared to traditional methods, and the robustness of the accounting framework is verified through uncertainty analysis. Sensitivity analysis further reveals that the grid emission factor, key material factors, and building design service life are the core variables affecting carbon emissions. The contribution of this study lies in proposing an operable and scalable BIM-LCA integrated solution. Its practical value resides in providing a real-time data feedback tool for low-carbon optimization during the building design stage, as well as offering a highly transparent methodological reference for carbon accounting in engineering practice, thereby supporting data-driven decision-making in the pursuit of sustainable urban development. Full article

Artificial intelligence (AI) is rapidly redefining the management of urban energy systems by coupling predictive analytics with closed-loop control across buildings, power grids, and mobility networks, positioning cities as critical leverage points in global decarbonization efforts. Contemporary urban environments generate vast, heterogeneous datasets that enable advanced machine learning applications; however, limitations remain, including interpretability–fairness trade-offs, fragmented data governance, interoperability gaps, cybersecurity risks, and insufficient long-term validation across diverse climatic and socio-economic contexts. This review evaluates AI-driven strategies for energy-efficient urban systems and identifies the technical and governance conditions required for scalable impact. The evidence synthesized indicates that supervised and ensemble learning models achieve high predictive accuracy for electricity demand and chiller performance, with models such as Random Forest Regression achieving R² values up to 0.9835 in electricity consumption forecasting, while unsupervised approaches detect latent inefficiencies in HVAC systems, delivering measurable savings typically around 6% under controlled benchmarking conditions. Deep learning architectures improve multi-building forecasting and real-time control, with hybrid CNN–LSTM models achieving prediction accuracies up to 97% and outperforming traditional statistical approaches in weekly energy demand forecasting achieving higher prediction accuracy and significant energy savings in complex urban subsystems with reported reductions of approximately 21–23% in residential and educational buildings and up to 37% in office HVAC systems. Hybrid and physics-informed AI models embed thermodynamic principles into data-driven frameworks, improving robustness, interpretability, and generalization. IoT sensor networks and edge-computing architectures support adaptive HVAC, demand–response, and smart-grid management, while integrated building–grid–mobility systems enhance load balancing, storage use, and carbon reduction. AI-enhanced strategies offer a credible pathway toward measurable reductions in urban energy use and emissions with deep reinforcement learning in digital twin environments reducing HVAC energy demand by 10–35% while maintaining thermal comfort within ±0.5 °C in line with ASHRAE standards, and overall energy savings reaching up to 44% in optimized systems when supported by interoperable infrastructures, secure digital-twin architectures, and standardized measurement and verification protocols. Full article

Establishing a reliable heating energy benchmark for urban buildings is essential for effective energy management, yet benchmark accuracy is often constrained by multiple building characteristics and uncertainty in energy prediction. This study investigated the influence of scale heterogeneity on the heating energy use intensity (EUI) of office buildings. A Bayesian surrogate model was developed, trained, and validated, yielding acceptable accuracy, with a CVRMSE of 12.37% and an NMBE of −1.02%, both within the limits recommended by ASHRAE Guideline 14-2023. The validated model was then used to simulate the heating EUI of office buildings with floor areas from 100 to 100,000 ${\mathrm{m}}^2$ under climatic conditions ranging from 3250 to 9698 HDD65. The results showed a clear inverse relationship between building scale and heating EUI. Smaller buildings were more sensitive to scale variation, with pronounced declines around 1000 and 3000 ${\mathrm{m}}^2$, while the decline rate weakened beyond 5000 ${\mathrm{m}}^2$. Climatic severity remained the dominant factor controlling the absolute level of heating demand, but the climatic differences in heating EUI gradually narrowed as building scale increased. Moreover, the scale effect persisted longer under colder climatic conditions. These findings provide a reference for establishing scale-sensitive heating energy benchmarks in urban public buildings. Full article

This study addresses the critical issues of rigid energy use and insufficient demand-side responsiveness in office buildings’ Variable Refrigerant Flow (VRF) systems under complex summer conditions. Existing research lacks fine-grained characterisation of short-term load fluctuations and often fails to accurately couple energy efficiency with humidity-adapted thermal comfort. To fill this gap, this paper proposes an integrated Model Predictive Control (MPC) framework driven by load characteristic identification and a novel hybrid prediction model. First, based on actual hourly metered data (683,280 records), K-means clustering was employed to identify three typical load patterns, pinpointing short-term peak loads in core office zones as the primary target for flexible regulation. Second, a high-precision GS-DBO-ELM prediction model—integrating Grid Search and Dung Beetle Optimisation—was developed to capture the nonlinear dynamics of VRF energy consumption and Predicted Mean Vote (PMV). The model achieved an R² of 0.99 with relative errors constrained within ±5%. Finally, a multi-objective MPC strategy, solved via an improved Artificial Hummingbird Algorithm (HAGSAHA) and weighted by the Analytic Hierarchy Process (AHP), was implemented to dynamically adjust zone-level temperature setpoints. Results demonstrate that the proposed MPC strategy reduces daily cooling energy consumption by 7.95–10.69% and peak loads by 15.3%, while maintaining strict thermal comfort (PMV within ±0.5). Under a time-of-use pricing mechanism, the flexible scheduling strategy achieved a 12.37% total electricity reduction and a 9.54% reduction in operating costs. This work provides a highly replicable, climate-tailored solution for low-carbon, flexible energy management in public buildings. Full article

Indonesia is approaching the ‘last mile’ of elimination for several skin-related neglected tropical diseases (skin NTDs): notably, leprosy, yaws and lymphatic filariasis (LF). However, persistent transmission in selected districts highlights systemic weaknesses in surveillance. This paper aimed to analyse the health system, operational and sociocultural barriers to integrated skin NTDs surveillance in Indonesia. A descriptive analysis of the national programmatic review of integrated skin NTDs was conducted in 2024, using a mixed-methods descriptive evaluation based on routine data and thematic analysis. Comparative case studies of the Belitung, Mimika and Sorong Selatan Districts were conducted using routine data, programme reports, and structured observations at primary health centres, district health offices and laboratories. Qualitative insights from programme managers, health workers and communities were thematically analysed. Integrated surveillance was constrained by fragmented governance, inflexible financing, and uneven workforce capacity, alongside operational challenges like delayed detection and geographic inaccessibility. Furthermore, sociocultural factors such as stigma and population mobility, combined with zoonotic LF transmission in Belitung, significantly undermine effectiveness and long-term programmatic sustainability. Despite strong national policy commitment and substantial progress in disease elimination, significant gaps remain between integration frameworks and operational realities at the district level. Accelerating skin NTDs elimination in Indonesia requires context-adapted integration, strengthened digital surveillance, sustained subnational financing, workforce capacity building and, in zoonotic settings, a One Health approach. Addressing these factors is essential for achieving and sustaining elimination in the last mile. Indonesia has achieved substantial progress across major skin NTDs, while also revealing persistent gaps that threaten the sustainability of elimination gains. Full article

To improve the indoor thermal comfort of embedded atriums and corridors in office buildings during summer, this study aims to optimize air conditioning airflow organization in atriums using computational fluid dynamics (CFD) simulations. Field measurements were carried out to collect air parameters, which were subsequently used to validate the established CFD model. Taking a six-story office building in Xi’an as the research subject and stratified air conditioning as the baseline case, this study investigated the effects of air inlet layout, air inlet type, and air volume distribution on the indoor thermal environment. The results revealed significant vertical temperature stratification within the atrium, with average temperatures ranging from 23.5 °C to 46.1 °C. Based on comparative analysis of multiple optimization scenarios, the following conclusions are drawn: adopting swirl diffusers in the corridors with an air inlet quantity ratio of 1:1:1:1:2 from the first to fifth floors, combined with uniform air supply volume across the first to fourth floors, can maintain the average Predicted Mean Vote (PMV) of each floor within the range of −0.1 to 0.3. Conversely, excessive air supply volume on upper floors and insufficient air supply volume on lower floors significantly degrade the corridor thermal comfort. Full article

Fixed external louvers are widely used to improve the environmental performance of glass curtain wall office buildings, yet existing studies more often report preferred solutions than transferable decision ranges for early-stage design. This study develops interval-based design rules for a standard-floor prototype of a point-supported glass curtain wall office building in Tianjin, a representative cold-climate city in China. A seven-variable design space integrating spatial-scale and shading variables was evaluated for 3000 Latin hypercube samples in a Rhino–Grasshopper–Honeybee workflow linked to Radiance and EnergyPlus, using Tianjin’s typical meteorological year data and GB 55015—2021-based office schedules, including an occupant density of 10 m²/person and occupied heating/cooling setpoints of 20/26 °C. Raw-sample statistics, Bootstrap-based stability testing, and surrogate-model-assisted continuous-response analysis were used to identify dominant variables, single-objective preferred intervals, and a neutral equal-weight baseline compromise zone. Under a neutral equal-weight baseline adopted for early-stage comparison, the compromise interval is concentrated around 20–25°, with 15–30° as a practical starting range, while alternative weighting scenarios show directional shifts toward the prioritized objective. Full article