Search Results (539)

To support national carbon neutrality goals, enhancing the thermal insulation of building envelopes has emerged as a crucial strategy in reducing building energy consumption. This study conducted a detailed quantitative analysis of energy performance improvements achieved through enhanced insulation levels in four representative non-residential building types: office, accommodation, educational, and sales facilities. Based on four scenarios—Baseline (2019), Insulation Reinforced, Passive House, and Zero Energy Building (ZEB)—EnergyPlus simulations were performed to calculate end-use energy demand and consumption. The results revealed that office buildings achieved the highest improvement, with up to 34.7% energy reduction, while educational and sales facilities showed moderate and limited improvements, respectively. These findings provide quantitative evidence for prioritizing insulation-based policies and differentiated ZEB strategies tailored to each building type. The proposed RB models and scenario-based methodology offer a robust foundation for establishing future ZEB regulations and performance-based energy policies in South Korea. To ensure clarity, the study explicitly referenced verified data sources and field measurements. The IdealLoadsAirSystem used in the simulation assumes 100% system efficiency; thus, the reported outcomes represent building system loads rather than final energy consumption. The ZEB-level scenario analyzed in this study focuses on envelope and lighting improvements only, not on HVAC system optimization. Full article

This study presents the design and implementation of a prototype dual-function photovoltaic window system that integrates flexible solar panels for dynamic shading and a compact lithium battery for local energy storage. The methodology involves developing an experimental setup where translucent, flexible photovoltaic panels are retrofitted onto a standard residential window. The system is connected to a charge controller and a small-capacity lithium-ion battery pack. Key performance metrics, including solar irradiance, power generation efficiency, reduction in thermal transmittance, and battery state of charge, are continuously monitored under varying real-world environmental conditions. The integrated panels can significantly reduce solar heat gain, thereby lowering indoor ambient temperature and reducing the building’s cooling load. Simultaneously, the system will generate sufficient electricity to be stored in the lithium battery, providing a self-contained power source for low-draw applications such as lighting or charging personal devices. This research highlights the viability of developing cost-effective, multi-functional building components that transform passive architectural elements into active energy-saving and power-generating systems in terms of green environment goals. Full article

Aligned with China’s “Dual Carbon” goals, this study addresses carbon emissions in the building sector. Existing research predominantly focuses on single-stage carbon emission assessment or separately examines the benefits of BIM applications and photovoltaic (PV) technology. There is a notable lack of studies that deeply integrate the BIM platform with dynamic assessment of building life cycle carbon emissions and PV carbon reduction strategies, particularly under the specific context of the hot-summer/cold-winter climate in Central China and a regional grid primarily reliant on thermal power. Moreover, localized and in-depth analyses targeting residential buildings in this region remain scarce. To address this gap, this study takes a residential building in Central China as a case study and establishes a BIM-based life cycle carbon emission assessment model to systematically quantify the carbon footprint across all stages. Results show total life cycle carbon emissions of 12600 tCO₂, with embodied carbon (4590 tCO₂, 36.6%) and the operational phase identified as the main emission sources. Through PV system integration and multi-scenario simulations, the study demonstrates significant carbon reduction potential: systems with 40–80 kW capacity can achieve annual carbon reductions ranging from 26 to 52 tCO₂. The 60 kW system shows the optimal balance with an annual reduction of 38.7 tCO₂ and a payback period of 3.53 years. The primary novelty of this work lies in its development of a dynamic BIM-LCA framework that enables real-time carbon footprint tracking, and the establishment of a first-of-its-kind quantitative model for PV strategy optimization under the specific climatic and grid conditions of Central China, providing a replicable pathway for region-specific decarbonization. Full article

As urbanization accelerates, façade defects in existing residential buildings have become increasingly prominent, posing serious threats to structural safety and residents’ quality of life. In the high-density built environment of Shenzhen, traditional manual inspection methods exhibit low efficiency and high susceptibility to omission errors. This study proposes an integrated framework for façade defect detection that combines unmanned aerial vehicle (UAV)-based visible-light and thermal infrared imaging with deep learning algorithms and parametric three-dimensional (3D) visualization. Three representative residential communities constructed between 1988 and 2010 in Shenzhen were selected as case studies. The main findings are as follows: (1) the fusion of visible and thermal infrared images enables the synergistic identification of cracks and moisture intrusion defects; (2) shooting distance significantly affects mapping efficiency and accuracy—for low-rise buildings, 5–10 m close-range imaging ensures high mapping precision, whereas for high-rise structures, medium-range imaging at approximately 20–25 m achieves the optimal balance between detection efficiency, accuracy, and dual-defect recognition capability; (3) the developed Grasshopper-integrated mapping tool enables real-time 3D visualization and parametric analysis of defect information. The Knet-based model achieves an mIoU of 87.86% for crack detection and 79.05% for leakage detection. This UAV-based automated inspection framework is particularly suitable for densely populated urban districts and large-scale residential areas, providing an efficient technical solution for city-wide building safety management. This framework provides a solid foundation for the development of automated building maintenance systems and facilitates their integration into future smart city infrastructures. Full article

This study presents an integrated thermo-economic framework for evaluating the impact of daily overload on the aging and cost performance of oil-immersed distribution transformers. The methodology combines international transformer thermal aging models, widely accepted in transformer loading guides such as those established by IEEE and IEC, with an equivalent annual cost (EAC) model, enabling a unified assessment of insulation degradation and operational expenditures. Using a residential load profile with 15 min resolution and climate data from three Ecuadorian regions (Quito, Guayaquil, and the Amazon), we analyze the influence of varying overload levels, peak durations, cooling methods Oil Natural Air Natural (ONAN), Oil Natural Air Forced (ONAF), and Oil Forced Air Forced (OFAF), and installation environments (indoor/outdoor) on transformer lifetime and ownership costs. Parametric simulations reveal that ambient temperature is the dominant factor in thermal degradation, with Guayaquil showing service life reductions of up to 70% compared to Quito under identical loading conditions. While larger transformers with forced cooling exhibit enhanced thermal resilience, the economic performance deteriorates non-linearly beyond 120–130% loading due to compounding losses and replacement costs. The results demonstrate that (i) overload tolerance is climate dependent, (ii) indoor installations incur systematic thermal penalties, and (iii) the IEC and IEEE models yield similar outcomes under moderate conditions but diverge under severe stress. The proposed approach provides utilities with a robust decision-support tool to optimize transformer loading strategies, replacement planning, and cooling system upgrades in geographically diverse power systems. Full article

Earth–Air Heat Exchangers (EAHEs) exploit stable subsurface temperatures to pre-condition supply air. To address limitations of conventional systems in hot–arid climates, this study investigates the performance of a hybrid EAHE prototype combining a serpentine subsurface pipe with a buried water tank. Installed in a residential building in Lichana, Biskra (Algeria), the system was designed to enhance land compactness, thermal stability, and soil–water heat harvesting. Experimental monitoring was conducted across 13 intervals strategically spanning seasonal transitions and extremes and was complemented by calibrated numerical simulations. From over 30,000 data points, outlet trajectories, thermal efficiency, Coefficient of Performance (COP), and energy savings were assessed against a straight-pipe baseline. Results showed that the hybrid EAHE delivered smoother outlet profiles under moderate gradients while the baseline achieved larger instantaneous ΔT. Thermal efficiencies exceeded 90% during high-gradient episodes and averaged above 70% annually. COP values scaled with the inlet–soil gradient, ranging from 1.5 to 4.0. Cumulative recovered energy reached 80.6 kWh (3.92 kWh/day), while the heat pump electricity referred to a temperature-dependent ASHP totaled 34.59 kWh (1.40 kWh/day). Accounting for the EAHE fan yields a net saving of 25.46 kWh across the campaign, only one interval (5) was net-negative, underscoring the value of bypass/fan shut-off under weak gradients. Overall, the hybrid EAHE emerges as a footprint-efficient option for arid housing, provided operation is dynamically controlled. Future work will focus on controlling logic and soil–moisture interactions to maximize net performance. Full article

The rapid advancement of technologies such as artificial intelligence, big data, and cloud computing has driven continuous expansion of global data centers, resulting in increasingly severe energy consumption and carbon emission challenges. According to projections by the International Energy Agency (IEA), the global electricity demand of data centers is expected to double by 2030. The construction of green data centers has emerged as a critical pathway for achieving carbon neutrality goals and facilitating energy structure transition. This paper presents a systematic review of the role of waste heat recovery technologies in data centers for achieving low-carbon development. Categorized by aspects of waste heat recovery technologies, power production and district heating, it focuses on assessing the applicability of heat collection technologies, such as heat pumps, thermal energy storage and absorption cooling, in different scenarios. This study examines multiple electricity generation pathways, specifically the Organic Rankine Cycle (ORC), Kalina Cycle (KC), and thermoelectric generators (TEG), with comprehensive analysis of their technical performance and economic viability. The study also assesses the feasibility and environmental advantages of using data center waste heat for district heating. This application, supported by heat pumps and thermal energy storage, could serve both residential and industrial areas. The study shows that waste heat recovery technologies can not only significantly reduce the Power Usage Effectiveness (PUE) of data centers, but also deliver substantial economic returns and emission reduction potential. In the future, the integration of green computing power with renewable energy will emerge as the cornerstone of sustainable data center development. Through intelligent energy management systems, cascaded energy utilization and regional energy synergy, data centers are poised to transition from traditional “energy-intensive facilities” to proactive “clean energy collaborators” within the smart grid ecosystem. Full article

While community energy initiatives and pilot projects have demonstrated technical feasibility and economic benefits, their site-specific nature limits transferability to systematic, scalable investment models. This study addresses this gap by proposing a modular framework for Renewable Energy Valleys (REVs), developed from real-world Community Energy Lab (CEL) demonstrations in Crete, Greece, which is an island with pronounced seasonal demand fluctuation, strong renewable potential, and ongoing hydrogen valley initiatives. Four modular business schemes are defined, each representing different sectoral contexts by combining a baseline of 50 residential units with one representative large consumer (hotel, rural households with thermal loads, municipal swimming pool, or hydrogen bus). For each scheme, a mixed-integer linear programming model is applied to optimally size and operate integrated solar PV, wind, battery (BAT) energy storage, and hydrogen systems across three renewable energy penetration (REP) targets: 90%, 95%, and 99.9%. The framework incorporates stochastic demand modeling, sector coupling, and hierarchical dispatch schemes. Results highlight optimal technology configurations that minimize dependency on external sources and curtailment while enhancing reliability and sustainability under Mediterranean conditions. Results demonstrate significant variation in optimal configurations across sectors and targets, with PV capacity ranging from 217 kW to 2840 kW, battery storage from 624 kWh to 2822 kWh, and hydrogen systems scaling from 65.2 kg to 192 kg storage capacity. The modular design of the framework enables replication beyond the specific context of Crete, supporting the scalable development of Renewable Energy Valleys that can adapt to diverse sectoral mixes and regional conditions. Full article

The purpose of the study is to evaluate the environmental performance of two systems for space heating and hot water provision in a residential building. In both cases, a ground-source heat pump is used. In the baseline system, the heat pump is driven by electrical power from the grid. In the alternative system, photovoltaic thermal collectors are integrated into the building for domestic hot water preparation and the production of electricity. Excess heat produced in the summer is introduced to the borehole and extracted later, in the cooler part of the year. Environmental benchmarking of the two systems was conducted using the Life Cycle Assessment method. A cradle-to-grave approach was applied, taking into account all life cycle stages of the system and its operation over 20 years. Results show that the alternative system yields significantly lower impacts in terms of Global Warming Potential (36% decrease) and Resources (36% decrease). In terms of Human Health, the decrease is minor (6%). However, in terms of Ecosystem, the alternative system shows a 47% higher impact than the baseline system. This increase is primarily attributed to the additional components required in the alternative configuration. Full article

The current evolution of the energy context and progress in sustainable energy technologies are enabling the development of new energy supply systems for the residential sector. However, the techno-economic assessment of such energy systems is not straightforward and depends, among others, on the building type, its thermal insulation rate, and user patterns, as well as on the climatic conditions or energy and technology prices. This study therefore aims to develop an investment model for a typical UK household energy system that is applied to a diversity of scenarios to highlight the sensibility of the output results over stochastic input data such as electricity and heat demands, ambient temperature, and global solar irradiation. This dwelling diversity dataset is generated using a thermal–electrical demand model that uses stochastic techniques to model uncertainty. This contribution concludes with a discussion on how end-users can effectively take part in the energy transition while minimizing their energy bill and potentially generate long-term revenues. The main results show stable economic performance, with capital expenditure (CAPEX) ranging from GBP 15,400 to GBP 17,000 and NPV from GBP 21,000 to GBP 26,000 over 2000 individual scenarios. This study also confirms the leveraging effect of policy instruments, such as subsidies, in shifting the optimal system design towards higher shares of renewable and storage technologies, further reducing the reliance on fossil fuels and the impact on distribution systems. Full article

Liquefied petroleum gas (LPG), composed mainly of propane and butane, is widely used as an energy source in residential, commercial, and industrial sectors; however, its high flammability poses a critical risk in the event of accidental leaks. In Peru, where LPG constitutes the main domestic energy source, leakage emergencies affect thousands of households each year. This pattern is replicated in developing countries with limited energy infrastructure. Early quantitative detection of propane, the predominant component of Peruvian LPG (~60%), is essential to prevent explosions, poisoning, and greenhouse gas emissions that hinder climate change mitigation strategies. This study presents PropNet-R, a convolutional neural network (CNN) designed to estimate propane concentrations (ppm) from thermal images. A dataset of 3574 thermal images synchronized with concentration measurements was collected under controlled conditions. PropNet-R, composed of four progressive convolutional blocks, was compared with SqueezeNet, VGG19, and ResNet50, all fine-tuned for regression tasks. On the test set, PropNet-R achieved MSE = 0.240, R² = 0.614, MAE = 0.333, and Pearson’s r = 0.786, outperforming SqueezeNet (MSE = 0.374, R² = 0.397), VGG19 (MSE = 0.447, R² = 0.280), and ResNet50 (MSE = 0.474, R² = 0.236). These findings provide empirical evidence that task-specific CNN architectures outperform generic transfer learning models in thermal image-based regression. By enabling continuous and quantitative monitoring of gas leaks, PropNet-R enhances safety in industrial and urban environments, complementing conventional chemical sensors. The proposed model contributes to the development of sustainable infrastructure by reducing gas-related risks, promoting energy security, and strengthening resilient, safe, and environmentally responsible urban systems. Full article

This study investigates the effect of rotation step size on the performance of flat-plate solar collectors (FPSC) equipped with single-axis tracking. Numerical simulations were carried out in EnergyPlus, coupled with a custom Python interface enabling dynamic control of collector orientation. The analysis was carried out for the city of Kragujevac in Serbia, located in a temperate continental climate zone, based on five representative summer days (3 July–29 September) to account for seasonal variability. Three collector types with different efficiency parameters were considered, and inlet water temperatures of 20 °C, 30 °C, and 40 °C were applied to represent typical operating conditions. The results show that single-axis tracking increased the incident irradiance by up to 28% and the useful seasonal heat gain by up to 25% compared to the fixed configuration. Continuous tracking (ψ = 1°) achieved the highest energy yield but required 181 daily movements, which makes it mechanically demanding. Stepwise tracking with ψ = 10–15° retained more than 90–95% of the energy benefit of continuous tracking while reducing the number of daily movements to 13–19. For larger steps (ψ = 45–90°), the advantage of tracking decreased sharply, with thermal output only 5–10% higher than the fixed case. Increasing the inlet temperature from 20 °C to 40 °C reduced seasonal heat gain by approximately 30% across all scenarios. Overall, the findings indicate that relative single-axis tracking with ψ between 10° and 15° provides the most practical balance between energy efficiency, reliability, and economic viability, making it well-suited for residential-scale solar thermal systems. This is the first study to quantify how discrete rotation steps in single-axis tracking affect both thermal and economic performance of flat-plate collectors. The proposed EnergyPlus–Python model demonstrates that a 10–15° step offers 90–95% of the continuous-tracking energy gain while reducing actuator motion by ~85%. The results provide practical guidance for optimizing low-cost solar-thermal tracking in continental climates. Full article

Centralized heating systems in severe cold regions suffer from widespread load estimation deviations due to architectural heterogeneity and a lack of construction drawings, leading to substantial energy waste. This study proposes a lightweight load calculation method that facilitates efficient calculation of heating loads for heterogeneous building clusters via spatial thermal pattern decoupling and matrix reorganization. First, a 3 × 3 load characteristic matrix is developed to characterize the spatial variation in thermal demand across different building positions (corner vs. intermediate units × top, middle, and bottom floors), revealing that corner units exhibit higher thermal loads than intermediate units, while top and bottom floors show significantly higher loads than middle floors. Second, two complementary matrices are established: the load characteristic matrix, which represents the building’s thermal behavior, and the structural feature matrix, which encodes the architectural configuration in terms of unit count (a) and floor count (b). Together, they enable rapid hourly load synthesis using only lightweight input parameters. The method is validated on 56 heterogeneous residential buildings in Northeast China. Using a decoupled 4U/6F standard model, the synthesized cluster heating load achieves an R² of 0.88, an RMSE of 24.15 GJ, a MAPE of 4.94%, and a Mean Percentage Error (MPE) of −0.82% against actual heating supply data, demonstrating high accuracy and negligible systematic bias—particularly during cold waves. This approach allows the seasonal variation in heat demand across an entire residential area to be estimated even in the absence of detailed construction drawings, offering practical guidance for operational heating management. Full article

Buildings with electrified heat pump systems, onsite photovoltaic (PV) generation, and energy storage offer strong potential for demand flexibility. This study compares two storage configurations, thermal energy storage (TES) and battery energy storage (BESS), to evaluate their impact on cooling performance and cost savings. A Model Predictive Control (MPC) framework was developed to optimize system operations, aiming to minimize costs while maintaining occupant comfort. Results show that both configurations achieve substantial savings relative to a baseline. The TES system reduces daily operating costs by about 50%, while the BESS nearly eliminates them (over 90% reduction) and cuts grid electricity use by more than 65%. The BESS achieves superior performance because it can serve both the controllable heating, ventilation, and air conditioning (HVAC) system and the home’s broader electrical loads, thereby maximizing PV self-consumption. In contrast, the TES primarily influences the thermal load. These findings highlight that the choice between thermal and electrical storage greatly affects system outcomes. While the BESS provides a more comprehensive solution for whole-home energy management by addressing all electrical demands, further techno-economic evaluation is needed to assess the long-term feasibility and trade-offs of each configuration. Full article

In recent decades, China has witnessed remarkable growth in housing construction, yet housing-related complaints have not declined significantly, highlighting the gap between housing quality and public expectations. Against this background, this study analyzes 32,277 national surveys to unpack residential satisfaction with green-livable communities in China. Entropy and standard-deviation weighting identified 16 priority indicators; artificial neural networks revealed weak direct influence of basic demographics on satisfaction, highlighting non-linear demand patterns. While 65–75% of respondents are satisfied with most attributes, significant city-level gaps persist—Beijing peaks near 90%, Chongqing falls below 50%. Dissatisfaction converges on three domains: infrastructure (parking, barrier-free access), building performance (leakage, noise, thermal defects) and smart systems (security, energy, health monitoring). Residents’ improvement priorities have shifted from basic shelter to health safety, smart technology, humanistic care and ecological amenities. A “basic-security + quality-upgrade” strategy is proposed: short-term repairs of common defects, medium-term smart-sustainable upgrades and long-term participatory governance. The findings not only enrich the theoretical framework of community satisfaction research but also provide practical guidance for enhancing community quality and meeting residents’ expectations in the context of China’s rapid urbanization and housing development. Full article