Search Results (330)

This study investigates the effectiveness of passive design in low-rise residential buildings located in arid desert climates, using the Dubai Solar Decathlon Middle East (SDME) competition as a case study. This full-scale experiment offers a unique opportunity to evaluate design solutions under controlled, realistic conditions; prescriptive, modeled performance; and monitored performance assessments. The prescriptive assessment reviews geometry, orientation, envelope thermal properties, and shading. Most houses adopt compact forms, with envelope-to-volume and envelope-to-floor area ratios averaging 1 and 3.7, respectively, and window-to-wall ratios of approximately 17%, favoring north-facing openings to optimize daylight while reducing heat gain. Shading is strategically applied, horizontal on south façades and vertical on east and west. The thermal properties significantly exceed the local code requirements, with wall performance up to 80% better than that mandated. The modeled assessment uses Building Energy Models (BEMs) to simulate the impact of prescriptive measures on energy performance. Three variations are applied: assigning minimum local code requirements to all the houses to isolate the geometry (baseline); removing shading; and applying actual envelope properties. Geometry alone accounts for up to 60% of the variation in cooling intensity; shading reduces loads by 6.5%, and enhanced envelopes lower demand by 14%. The monitored assessment uses contest-period data. Indoor temperatures remain stable (22–25 °C) despite outdoor fluctuations. Energy use confirms that houses with good designs and airtightness have lower cooling loads. Airtightness varies widely (avg. 14.5 m³/h/m²), with some well-designed houses underperforming due to construction flaws. These findings highlight the critical role of passive design as the first layer for improving the energy performance of the built environment and advancing toward net-zero targets, specifically in arid desert climates. Full article

To address energy shortages and achieve carbon peaking/neutrality, this study develops a distributed renewable-based integrated energy system (IES) for rural active zero-energy buildings (ZEBs). Energy consumption patterns of typical rural houses are analyzed, guiding the design of a resource-tailored IES that balances economy and sustainability. Key equipment capacities are optimized to achieve net-zero/zero energy consumption targets. For typical daily cooling/heating/power loads, equipment output is scheduled using a dual-objective optimization model minimizing operating costs and CO₂ emissions. Results demonstrate that: (1) Net-zero-energy IES outperforms separated production (SP) and full electrification systems (FES) in economic-environmental benefits; (2) Zero-energy IES significantly reduces rural building carbon emissions. The proposed system offers substantial practical value for China’s rural energy transition. Full article

This study provides a detailed dataset from two modern homes constructed inside an environmentally controlled chamber. These data are used to carefully calibrate a dynamic thermal simulation model of these homes. The calibrated models show good agreement with measurements taken under controlled conditions. The two case study homes, “The Future Home” and “eHome2”, were constructed within the University of Salford’s Energy House 2.0, and high-quality data were collected over eight days. The calibration process involved updating U-values, air permeability rates, and modelling refinements, such as roof ventilation, ground temperatures, and sub-floor void exchange rates, set as boundary conditions. Results demonstrated a high level of accuracy, with performance gaps in whole-house heat transfer coefficient reduced to 0.5% for “The Future Home” and 0.6% for “eHome2”, falling within aggregate heat loss test uncertainty ranges by a significant amount. The study highlights the improved accuracy of calibrated dynamic thermal simulation models, compared to results from the steady-state Standard Assessment Procedure model. By providing openly accessible calibrated models and a clearly defined methodology, this research presents valuable resources for future building performance modelling studies. The findings support the UK’s transition to dynamic modelling approaches proposed in the recently introduced Home Energy Model approach, contributing to improved prediction of energy efficiency and aligning with goals for zero carbon ready and sustainable housing development. Full article

This study examines the demographic and macroeconomic factors influencing housing prices in London from Q3 2014 to Q4 2022, focusing on the pre- and post-Brexit referendum periods. Using multiple regression analysis, the research evaluates the impact of interest rates, inflation, construction costs, population changes, and net migration on the housing price index (HPI) across various market segments. The findings suggest that interest rate base rates, consumer price inflation, and construction output price indices were significant predictors of housing price fluctuations. Notably, cash purchases exhibited the strongest explanatory power due to a reduced sensitivity to market changes. Additionally, London’s population was a key determinant, particularly affecting first-time buyers and mortgage-backed purchases. These results contribute to a deeper understanding of the London housing market and offer insights into policy measures addressing housing affordability and investment dynamics. Full article

To address the issues of poor thermal performance and high energy consumption in rural dwellings in cold regions of China, this study investigates multi-type energy-efficient retrofitting strategies for rural houses in the Hebei–Tianjin region. By utilizing a two-step cluster analysis method, 458 rural dwellings from 32 villages were classified based on household demographics, architectural features, and energy consumption patterns, identifying three typical categories: pre-1980s adobe dwellings, 1980s–1990s brick–wood structures, and post-1990s brick–concrete houses. Tailored sunspace design strategies were proposed through simulation: low-cost plastic film sunspaces for adobe dwellings (dynamic payback period: 2.8 years; net present value: CNY 2343), 10 mm hollow polycarbonate (PC) panels for brick–wood structures (cost–benefit ratio: 1.72), and high-efficiency broken bridge aluminum Low-e sunspaces for brick–concrete houses (annual natural gas savings: 345.24 m³). Economic analysis confirmed the feasibility of the selected strategies, with positive net present values and cost–benefit ratios exceeding 1. The findings demonstrate that classification-based retrofitting strategies effectively balance energy-saving benefits with economic costs, providing a scientific hierarchical implementation framework for rural residential energy efficiency improvements in cold regions. Full article

Facing rapid urbanization, rising temperatures, and a residential sector that accounted for 38% of Egypt’s electricity use in 2022, middle-income housing presents a critical yet underexplored opportunity for energy efficiency improvements. This study investigates how the integration of passive design strategies and rooftop photovoltaic (PV) systems can enhance energy performance in this segment, using the Sakan Masr housing project in New Cairo as a case study. Addressing a research gap—namely the limited analysis of combined strategies in Egypt’s middle-income housing—the study follows a four-phase methodology: identifying dominant building orientations; simulating electricity demand and thermal comfort using DesignBuilder; optimizing the building envelope with passive measures; and evaluating PV system performance across south-facing and east–west configurations using PV-SOL. Results reveal that passive strategies such as improved glazing and shading can enhance thermal comfort by up to 10% and reduce cooling loads. Also, east–west PV arrays outperform south-facing ones, producing over 14% more electricity, reducing costs by up to 50%, and avoiding up to 168 tons of CO₂ emissions annually. The findings highlight that passive improvements with smart PV integration—offer a cost-effective pathway toward Net Zero Energy goals, with significant implications for national housing policy and Egypt’s renewable energy transition. Full article

As the population increases, the growing demand for residential housing escalates construction activities, significantly impacting global warming by contributing 42% of primary energy use and 39% of global greenhouse gas (GHG) emissions. This study addresses a gap in research on lifecycle assessment (LCA) for Indian residential buildings by evaluating the full cradle-to-grave carbon footprint of a typical single-family house in Northern India. A BIM-based LCA framework was applied to a 110 m² single-family dwelling over a 60-year life span. Operational use performance and climate analysis was evaluated via cove tool. The total carbon footprint over a 60-year lifespan was approximately 5884 kg CO₂e, with operational energy use accounting for about 87% and embodied carbon approximately 11%. Additional impacts came from maintenance and replacements. Energy usage was calculated as 71.76 kWh/m²/year and water usage as 232.2 m³/year. Energy consumption was the biggest driver of emissions, but substantial impacts also stemmed from material production. Cement-based components and steel were the largest embodied carbon contributors. Under the business-as-usual (BAU) scenario, the operational emissions reach approximately 668,000 kg CO₂e with HVAC and 482,000 kg CO₂e without HVAC. The findings highlight the necessity of integrating embodied carbon considerations alongside operational energy efficiency in India’s building codes, emphasizing reductions in energy consumption and the adoption of low-carbon materials to mitigate the environmental impact of residential buildings. Future work should focus on the dynamic modeling of electricity decarbonization, improved regional datasets, and scenario-based LCA to better support India’s transition to net-zero emissions by 2070. Full article

Retrofitting the existing housing stock to a high level of energy efficiency will not be limited to achieving the decarbonisation of 80.3 MtCO2e residential emissions and reducing fuel poverty in 4.16 million households, but also improving the health and well-being of UK residents and their overall quality of life. The current progress of housing retrofitting is poor, at less than 1%. The UK expects to achieve net zero by 2050, and the challenge is immense as there are more than 30 million houses. The challenge is similar in other global contexts. Even if the required technology, supply chain, skilled labour, and finance could have been provided, the retrofitting would not move forward without positive engagement from the clients. Proper strategies are required to retrofit at scale. Focusing on the challenges of stakeholder engagement in housing retrofitting, this study focused on developing a hybrid one-stop shop solution through design science research. A theoretical artefact and an empirical system requirement specification document were developed to propose a one-stop shop solution. This was tested through retrofit industry stakeholders. Findings reveal that the one-stop shop model will be a good answer to retrofitting at scale, providing the resident engagement of 30.1 million households. The model can support residents with or without computer literacy due to its hybrid approach. The proposed theoretical and industrial models can be used as base models for developing one-stop shops for housing retrofitting by adapting them for context-specific requirements. Full article

South Africa’s persistent energy shortages and high utility costs have led to increased interest in rooftop solar photovoltaic (PV) systems. However, understanding their economic and environmental viability in urban residential contexts remains limited. This study investigates the feasibility of integrating rooftop solar PV systems with local energy storage and grid electricity in residential housing complexes in Benoni, Gauteng Province. A hybrid energy system was proposed and modeled using detailed consumption data from a typical community in Benoni. The system includes rooftop PV installations, lithium-ion storage, and connection to the national grid. A techno-economic analysis was conducted over a 25-year project lifespan to evaluate energy cost, payback period, net present cost, and carbon dioxide emissions. The optimal system configuration—Solar PV + Storage + Grid—achieved average annual utility bill savings of USD 30,207, with a payback period of 1.0 year, a net present cost (NPC) of USD 40,782, and an internal rate of return (IRR) of 101.7%. Annual utility costs were reduced from USD 30,472 to USD 267, and the system resulted in a net reduction of 130 metric tons of CO₂ emissions per year. The levelized cost of energy (LCOE) was USD 0.0071/kWh. The integration of rooftop solar PV and energy storage with grid electricity presents a highly cost-effective and environmentally sustainable solution for residential communities in urban South Africa. The findings support policy initiatives aligned with Sustainable Development Goal (SDG) 7: “Affordable and Clean Energy”. Full article

This study investigates the generation of synthetic CT (sCT) images from zero echo time (ZTE) MRI to support MR-only radiotherapy, which can reduce image registration errors and lower treatment planning costs. Since MRI lacks the electron density data required for accurate dose calculations, generating reliable sCTs is essential. ZTE MRI, offering high bone contrast, was used with two deep learning models: attention deep residual U-Net (ADR-Unet) and derived conditional generative adversarial network (cGAN). Data from 17 head and neck cancer patients were used to train and evaluate the models. ADR-Unet was enhanced with deep residual blocks and attention mechanisms to improve learning and reconstruction quality. Both models were implemented in-house and compared to standard U-Net and Unet++ architectures using image quality metrics, visual inspection, and dosimetric analysis. Volumetric modulated arc therapy (VMAT) planning was performed on both planning CT and generated sCTs. ADR-Unet achieved a mean absolute error of 55.49 HU and a Dice score of 0.86 for bone structures. All the models demonstrated Gamma pass rates above 99.4% and dose deviations within 2–3%, confirming clinical acceptability. These results highlight ADR-Unet and cGAN as promising solutions for accurate sCT generation, enabling effective MR-only radiotherapy. Full article

Leonardite (LEO), a microbial derived product rich in humic and fulvic acids, has been tested, due to its beneficial properties for health and well-being, as a feed additive, mainly in non-ruminant species. Although there are some reports of LEO supplementation in ruminants fed with high-to medium-forage based diets, there is no information available of the potential effects of LEO in ruminants fed, under sub-tropical climate conditions, with high-energy diets during long-term fattening. For this reason, the objective of the present experiment was to evaluate the effects of LEO levels inclusion in diets for feedlot lambs finished over a long-term period. For this reason, 48 Pelibuey × Katahdin lambs (initial weight = 20.09 ± 3.55 kg) were fed with a high-energy diet (88:12 concentrate to forage ratio) supplemented with LEO (with a minimum of 75% total humic acids) for 130 days as follows: (1) diet without LEO, (2) diet supplemented with 0.20% LEO, (3) diet supplemented with 0.40% LEO, and (4) diet supplemented with 0.60% LEO. For each treatment, Leonardite was incorporated with the mineral premix. Lambs were blocked by weight and housed in 24 pens (2 lambs/pen). Treatment effects were contrasted by orthogonal polynomials. The average climatic conditions that occurred during the experimental period were 31.6 ± 2.4 °C ambient temperature and 42.2 ± 8.1% relative humidity (RH). Those values of ambient temperature and RH represent a temperature humidity index (THI) of 79.07; thus, lambs were finished under high heat load conditions. The inclusion of LEO in diet did not affect dry matter intake (p ≥ 0.25) and average daily gain (p ≥ 0.21); therefore, feed to gain ratio was not affected (p ≥ 0.18). The observed to expected dietary net energy averaged 0.96 and was not affected by LEO inclusion (p ≥ 0.26). The lower efficiency (−4%) of dietary energy utilization is an expected response given the climatic conditions of high ambient heat load presented during fattening. Lambs that were slaughtered at an average weight of 49.15 ± 6.00 kg did not show differences on the variables measured for carcass traits (p ≥ 0.16), shoulder tissue composition (p ≥ 0.59), nor in visceral mass (p ≥ 0.46) by inclusion of LEO. Under the climatic conditions in which this experiment was carried out, LEO supplementation up to 0.60% in diet (equivalent to 0.45% of humic substances) did not did not help to alleviate the extra-energy expenditure used to dissipate the excessive heat and did not change the gained tissue composition of the lambs that were fed with high-energy diets during long-term period under sub-tropical climate conditions. Full article

Real-time tumor-tracked radiotherapy with a linear accelerator-magnetic resonance (linac-MR) hybrid system requires accurate tumor delineation at a fast MR imaging rate. Various autocontouring methods have been previously evaluated against “gold standard” manual contours by experts. However, manually drawn contours have inherent intra- and inter-observer variations. We aim to quantify these variations and evaluate our tumor-autocontouring algorithm against the manual contours. Ten liver, ten prostate, and ten lung cancer patients were scanned using a 3 tesla (T) magnetic resonance imaging (MRI) scanner with a 2D balanced steady-state free precession (bSSFP) sequence at 4 frames/s. Three experts manually contoured the tumor in two sessions. For autocontouring, an in-house built U-Net-based autocontouring algorithm was used, whose hyperparameters were optimized for each patient, expert, and session (PES). For evaluation, (A) Automatic vs. Manual and (B) Manual vs. Manual contour comparisons were performed. For (A) and (B), three types of comparisons were performed: (a) same expert same session, (b) same expert different session, and (c) different experts, using Dice coefficient (DC), centroid displacement (CD), and the Hausdorff distance (HD). For (A), the algorithm was trained using one expert’s contours and its autocontours were compared to contours from (a)–(c). For Automatic vs. Manual evaluations (Aa–Ac), DC = 0.91, 0.86, 0.78, CD = 1.3, 1.8, 2.7 mm, and HD = 3.1, 4.6, 7.0 mm averaged over 30 patients were achieved, respectively. For Manual vs. Manual evaluations (Ba–Bc), DC = 1.00, 0.85, 0.77, CD = 0.0, 2.1, 2.8 mm, and HD = 0.0, 4.9, 7.2 mm were achieved, respectively. We have quantified the intra- and inter-observer variations in manual contouring of liver, prostate, and lung patients. Our PES-specific optimized algorithm generated autocontours with agreement levels comparable to these manual variations, but with high efficiency (54 ms/autocontour vs. 9 s/manual contour). Full article

The renovation of buildings impacts various factors; one of them is the economic aspect, which has a significant influence on the decision-making process in building refurbishment, especially in social housing. An often-neglected aspect of renovation is the influence of climate change. Typically, historical climate data are used to estimate the building’s future energy needs. However, due to climate change, this approach may fail to accurately represent future environmental conditions, resulting in miscalculations in energy consumption and costs. This study analyzed a building archetype obtained from the TABULA webtool with the characteristics of a social house building located in Trieste. Dynamic simulations were performed using DesignBuilder and EnergyPlus software and future climate models (the GERICS_CNRM-CM5 and GERICS_IPSL-CM5A-MR models obtained from the EURO-CORDEX database). The projected energy needs of the renovated building and its economic effects were compared with current scenarios, and due to the uncertainties in economic parameters, the outcome is expressed in terms of percentiles of the Net Present Value (NPV). The results of this study show that since temperature increases in the future, the need for energy in the heating period reduces, while the need for cooling increases, directly affecting the statistical distribution of the NPV. Full article

The residential construction sector is critical to economic stability and housing availability. Residential construction demands often fluctuate due to demographic, economic, social, or market condition variables. This study seeks to investigate the significance of these external variables and produce a predictive model for residential construction demand using ElasticNet regression. Adopting New Zealand as a case study and leveraging datasets from Statistics New Zealand, this research identifies key demographic, economic, and market factors influencing four building categories: retirement villages, apartments, multiunit developments, and standalone houses. The research results indicate that age groups, particularly the 20−39 and 65+ age groups, and economic indicators, such as the house price index and unemployment rates, have high prediction powers. The models showed high accuracy for some categories, with R² values exceeding 0.87 for retirement villages and 0.91 for multi-units. Challenges were encountered with standalone houses and apartments due to residual variance. The research findings highlight the importance of targeted urban planning and policy adjustments to satisfy the requirements of specific age groups, address housing affordability and demographic shifts, and cater to prevailing market conditions. This research provides practical insights and guidance for urban planners, public housing agencies, residential developers, and residential contractors while offering a robust methodological framework for predictive modelling in the construction sector. Full article

South Korea is rapidly transitioning into an aging society, resulting in a growing demand for senior multi-family housing. Nevertheless, current façade designs remain limited in diversity and fail to adequately address the visual needs and preferences of the elderly population. This study presents a biophilic façade design approach for senior housing, utilizing Stable Diffusion (SD) fine-tuned with low-rank adaptation (LoRA) to support the implementation of differentiated biophilic design (BD) strategies. Prompts were derived from an analysis of Korean and worldwide cases, reflecting the perceptual and cognitive characteristics of older adults. A dataset focusing on key BD attributes—specifically color and shapes/forms—was constructed and used to train the LoRA model. To enhance accuracy and contextual relevance in image generation, ControlNet was applied. The validity of the dataset was evaluated through expert assessments using Likert-scale analysis, while model reliability was examined using loss function trends and Frechet Inception Distance (FID) scores. Our findings indicate that the proposed approach enables more precise and scalable applications of biophilic design in senior housing façades. This approach highlights the potential of AI-assisted design workflows in promoting age-inclusive and biophilic urban environments. Full article