Search Results (333)

Heating, ventilation, and air conditioning (HVAC) systems are essential for indoor air quality and thermal comfort but can simultaneously act as vectors for microbial contamination, particularly bacteria and fungi. While the COVID-19 pandemic intensified focus on airborne viral transmission, bacterial and fungal contamination in indoor environments remains a persistent and significant health risk. This study presents a detailed case study of a restaurant HVAC system, analysing the impact of different ventilation strategies on bacterial contamination, infection transmission risk, energy consumption, and thermal comfort. By focusing on a real-world application, the research evaluates practical challenges and trade-offs associated with HVAC operation modifications aimed at mitigating microbial risks while maintaining acceptable energy and comfort levels. The research compares three operational scenarios: normal operation with air recirculation, 24 h operation with 100% outdoor air, and extended operation periods. Results demonstrate that while strategies emphasizing outdoor air intake and extended operation reduce infection probability by up to 60–65%, they simultaneously increase energy consumption by over 1700% and compromise thermal comfort parameters. In the h24 case, the pre-heat coil rises from 2421.7 to 43,923.7 kWh and the post-heat coil from 24,812.8 to 152,970.4 kWh, while the Plus 2 h strategy reduces the energy penalty by roughly 42–51% with respect to the h24 case. The findings are contextualized within current research on bacterial and fungal risks in HVAC systems, highlighting the critical need for balanced ventilation strategies that integrate health protection, energy efficiency, and comfort considerations. Full article

The solar-assisted air source heat pump (SAHP) is a key technology of low carbon heating. However, the SAHP is still inefficient and unstable at low temperatures. Cascaded latent heat storage (CLHS) can store multi-stage thermal energy, which provides the possibility for the multiple utilization of solar energy. Hence, this paper proposed the SAHP integrated with CLHS for building heating. The high-temperature and medium-temperature latent heat storage (LHS) units are used for direct heating, and the low-temperature LHS unit preheats the air for the air source heat pump (ASHP). The thermal performance of the CLHS device is evaluated through combined numerical simulations and experimental tests. Results show that the average heat storage rate of the cascaded system is 61.1% higher than that of a conventional single-stage LHS unit. The heat storage uniformity of CLHS gradually improves with increasing inlet flow rate, but shows a trend of first increasing and then decreasing with the increase in fluid inlet temperature. Among the three tested levels, 80 °C was found to be the most uniform heat storage of the CLHS device. The performance of the system was further analyzed using TRNSYS to assess seasonal building heating performance. The overall efficiencies of the high/middle/low temperature LHS units are 93.6%, 81.6% and 94.3%, respectively. And the solar heat supply accounts for 70.8% of the total heat supply of the system. Compared with the non-preheating system where the low-temperature LHS unit is removed, the COP of the graded heating system is increased by 18.3%, and the energy consumption is reduced by 16.6%. Further parametric optimization based on the Hooke–Jeeves method reduces total system energy consumption by 20.7% and associated pollutant emissions by 20.6% compared with the pre-optimization system. The findings provide practical insights into the application of CLHS in solar-assisted heat pump systems for building heating. Full article

Heat stress is a major challenge for broiler production worldwide and is expected to intensify with more frequent heatwaves. This study focuses on mechanically ventilated broiler houses in South Korea, where heatwaves have become increasingly frequent. Three regression meta-models were developed to predict the indoor temperature–humidity index (THI) directly from weather forecast data, using simulated results from a validated building energy simulation (BES) model. A TRNSYS-based BES model was validated against field measurements from four rearing cycles in a commercial broiler house (RMSE 1.31–2.16; MAPE < 2.00%). Using 3072 simulation cases that combined multiple sites, thermal-transmittance levels, cooling conditions, building sizes, and broiler body weights, three regression meta-model approaches were evaluated: a condition-specific regression meta-model for each condition set, a unified regression meta-model with categorical predictors, and a single variable meta-model using only external THI as a predictor. All three showed strong predictive performance, and the unified regression meta-model achieved R² = 0.978, RMSE = 0.817, and MAPE = 0.829, providing the best balance between accuracy and simplicity. This unified model offers a practical tool to link weather forecasts with broiler-house design and environmental-control decisions for heat-stress risk management. Full article

Rural households in cold regions still rely heavily on coal for cooking and domestic hot water, while single renewable energy sources suffer from intermittency and limited system-level assessment. This study proposes a solar–biogas complementary energy supply system integrating evacuated-tube solar collectors, an above-ground anaerobic digester, thermal storage, and biogas utilization for rural residential applications in Minqin, Northwest China. A dynamic system-wide model was developed by coupling TRNSYS with nonlinear representations of anaerobic fermentation and biogas boilers, enabling hour-by-hour simulation of energy production, conversion, storage, and consumption. Field measurements were used for validation, and the root mean square deviation between simulated and measured temperatures and gas production remained below 10%. During the heating season, the solar subsystem supplied 10% of the digester heating demand and 90% of the domestic hot-water load, while the biogas subsystem contributed 9.29% and 90.71%, respectively. The system delivered 4728.96 MJ of heat against a seasonal demand of 4636.22 MJ, fully meeting user requirements. A comprehensive 3E (energy–environment–economic) assessment shows that, compared with traditional rural energy supply modes, the proposed system reduces CO₂ and NOx emissions by 65.85% and 98.13%, respectively, and demonstrates favorable economics with a benefit–cost ratio of 2.41 and a discounted payback period of 3.27 years. The proposed modeling and evaluation framework provides a replicable solution for clean energy substitution and circular waste utilization in rural areas. Full article

The high expansion rate of industrial-scale photovoltaic (PV) systems in emerging economies requires proper performance prediction models that consider particular climatic variabilities. Although the theoretical potential of solar energy in South Asia is well documented, there still exists a gap in the validation of simulation models to operational data over long periods in subtropical monsoon climates. Unlike prior studies, this work combines multi-year operational data with dynamic TRNSYS simulations to quantify both technical and environmental performance of a 1 MW PV system under subtropical monsoon conditions. This paper provides a detailed performance evaluation of a 1 MW grid-connected PV system located in Punjab, Pakistan. The actual performance of the system is compared with a dynamic simulation model that is created in the Transient System Simulation Tool (TRNSYS) using three years of operational data. Four different scenarios are analyzed: (1) Ideal Theoretical Operation, (2) Actual Field Data, (3) Simulated Operation with Maximum Power Point Tracking (MPPT), and (4) Simulated Operation without MPPT. The results reveal that the real system produced an average of 1342 MWh/year, whereas the MPPT-enabled simulation predicted 1664 MWh/year, indicating a performance difference of 19.3%. Statistical validation revealed a strong correlation ($R^2=0.84$) between the model and reality, yet identified a normalized Root Mean Square Error (nRMSE) of 26.8%. This deviation represents a performance gap which is deconvoluted into agricultural soiling losses and grid curtailment. The research work quantifies the technical effect of MPPT where a 27% operational advantage is realized in comparison to fixed-voltage cases, proving its necessity in climates with high diffuse radiation during monsoon seasons. Economic analysis demonstrates a Levelized Cost of Energy (LCOE) of $0.0378/kWh of the existing system, and a Simple Payback Time (SPBT) of 4.74 years at the current industrial tariffs. Sensitivity analysis also indicates that in case of an increase in grid tariffs to 50 PKR/kWh, Internal Rate of Return (IRR) increases to 18.8%. Environmental analysis confirms a carbon emission reduction of 765 tons/year. These results validate the techno-economic feasibility of large-scale PV in the area and provide an important understanding of the critical yield losses in monsoon seasons, which offers an effective robust benchmark for future industrial energy policy in developing economies. Full article

Regional Integrated Energy Systems (RIESs) are pivotal in the low-carbon transition of energy-intensive hospital campuses. However, traditional multi-objective optimization for RIES planning often suffers from the subjective selection of weights, leading to configurations that lack robustness against varying decision-maker preferences. To address this, this paper proposes a robust optimization methodology integrating shadow cost theory and multi-scenario weight scanning. A high-fidelity dynamic simulation model of a hospital in Beijing was constructed using TRNSYS. By monetizing environmental externalities into shadow costs, a comprehensive objective function, including annual cost savings rate, primary energy savings rate, and environmental shadow cost savings rate, was established, and the Hooke–Jeeves algorithm was employed to scan ten distinct weight scenarios, ranging from profit-driven to eco-centric preferences. The results reveal that solar collectors lack economic competitiveness under current boundary conditions due to cooling–heating coupling constraints. Instead, a configuration featuring a large-capacity gas turbine (2790 kW) coupled with a moderate GSHP was identified as the optimal solution in over 80% of the scenarios, demonstrating high preference robustness. Crucially, this configuration achieves net-negative emissions by maximizing clean power exports to displace carbon-intensive grid electricity. Compared to the reference system, the optimized RIES reduces primary energy consumption by 82.7% and achieves substantial environmental benefits, subject to grid emission factors. These findings confirm that prioritizing clean power export is a resilient pathway for hospitals to balance economic feasibility with environmental goals under current policy frameworks, providing scientific guidance for policymakers and engineers. Full article

This study investigates a Proxy-Calibration method for modeling Variable Refrigerant Flow (VRF) systems in TRNSYS, addressing the absence of a dedicated simulation component. The approach approximates part-load behavior through indoor-unit combination mapping, utilizing empirical data from a public office building in Seoul. Simulation results were compared with one year of monitored data. While indoor temperature trends showed moderate agreement (R² = 0.68), electricity consumption diverged significantly from actual measurements. The coefficient of variation in the root mean square error (CVRMSE) ranged from 95% to 118% for the boiler and 153% to 590% for the VRF system, indicating a substantial discrepancy well beyond standard calibration thresholds. These findings underscore the limitations of using static performance maps without explicit control logic. Consequently, this study defines the proposed method as an exploratory investigation; while it establishes a procedural framework for approximating VRF operation, rigorous energy prediction requires further refinement through empirical curve fitting and detailed control representation. Full article

Growing demand for computing resources is increasing electricity use and cooling needs in data centres (DCs). Simultaneously, it creates opportunities for decarbonisation through the integration of waste heat (WH) into district heating (DH) systems. Such integration reduces primary energy (PE) consumption and emissions, particularly in low-temperature DH networks. In this study, the possibility for utilisation of WH from DC hybrid cooling system into third generation (3G), fourth generation (4G), and fifth generation (5G) DH systems is investigated. The work is based on the dynamic simulations in TRNSYS. The model of the hybrid cooling system consists of a direct liquid cooling (DLC) loop (25–30 °C) and a chilled water rack coolers (CRCC) loop (10–15 °C). For 3G DH, a high-temperature water-to-water heat pump (HP) is applied to ensure the required water temperature in the system. Measured meteorological and equipment data are used to reproduce real DC operating conditions. Relative to the reference system, integrating WH into 5G DH reduces PE consumption and CO₂ emissions by 88%. Results indicate that integrating WH into 5G DH and 4G DH minimises global cost and achieves a payback period of less than one year, whereas 3G DH, requiring high-temperature HPs, achieves 14 years. This approach to integrating waste heat from a hybrid DLC+CRCC DC cooling system is technically feasible, economically and environmentally viable for planning future urban integrations of waste heat into DH systems. Full article

Decarbonizing building energy use requires efficient heat pumps and low-impact geothermal exchangers. A novel standalone TRNSYS Type was developed for a patented shallow horizontal ground heat exchanger (HGHE), called flat-panel (FP), designed at the University of Ferrara. Beyond simulating the FP in isolation, the Type enables coupling with other components within heat-pump configurations, allowing performance assessments that reflect realistic operating conditions. The Type was implemented in TRNSYS models of a ground-source heat pump (GSHP) and of a dual air and ground source heat pump (DSHP) to verify Type reliability and evaluate potential DSHP advantages over GSHP in terms of efficiency and ground-loop downsizing. The performance of the system was analyzed under varying HGHE lengths and DSHP control strategies, which were based on onset temperature differential $DT$. The results highlighted that shorter HGHE lines yielded higher specific HGHE performance, while higher $DT$ reduced HGHE operating time. Concurrently, the total energy extracted from the ground decreased with increasing $DT$ and reduced length, thus supporting long-term thermal preservation and allowing HGHE to operate under more favorable conditions. Exploiting air as an alternative or supplemental source to the ground allows significant reduction of the HGHE length and the related installation costs, without compromising the system performance. Full article

With the energy consumption of data centers continuously increasing in recent years, green data centers as a transformative solution have grown increasingly significant. In this paper, a proton exchange membrane fuel cell-based combined cooling, heating, and power (PEMFC-CCHP) system coupled with wind and solar energy is proposed to ensure an energy supply that matches the dynamic load requirements of data centers. Taking a data center located in Guiyang, China, as a case study, a TRNSYS 18 simulation model for the integrated energy system is developed, and the analysis on the energy, economic, and environmental performance of the system is performed. The results demonstrate that the integrated energy system can effectively accommodate the load fluctuations of data centers through multi-energy complementarity. The PEMFC-CCHP system achieves a high energy utilization efficiency of 0.85–0.90. Furthermore, the payback period of the integrated energy system is estimated to be between 8.2 and 13.1 years, yielding an annual reduction in CO₂ emissions of 1847 t. Full article

To address the poor thermal insulation and freeze resistance of rural outdoor toilets in cold regions—key obstacles to achieving the UN Sustainable Development Goal (SDG) 6.2 and popularizing rural sanitary toilets—this study fills the literature gap of insufficient research on passive solar heating systems tailored for rural toilets in cold climates. Using TRNSYS simulation, Plackett–Burman key factor screening, single-factor experiments, and Box–Behnken response surface methodology, we optimized the system with building envelope thermal parameters and Beijing’s typical meteorological year data as inputs, taking January’s average indoor temperature as the core evaluation index. Results indicated six parameters (solar wall area, air cavity thickness, vent area ratio, vent spacing, exterior wall insulation thickness, and heat-gain window-to-wall ratio) significantly influence indoor temperature (p < 0.05). The optimal configuration was as follows: solar wall area 3.45 m², window-to-wall ratio 30%, exterior wall insulation thickness 200 mm, vent spacing 1800 mm, air cavity thickness 43 mm, and vent area ratio 5.7%. Post-optimization, the average temperature during the heating season reached 10.81 °C (79.5% higher than baseline), with January’s average, maximum, and minimum temperatures at 7.95 °C, 20.47 °C, and −1.42 °C, respectively. This solution effectively prevents freezing of flushing fixtures due to prolonged low temperatures, providing scientific support for the application of passive rural toilets in China’s cold regions. Full article

Building energy consumption is a major source of carbon emissions, with the heating energy demand of rural buildings in the hot summer and cold winter (HSCW) zone having increased 575-fold over the past 15 years. This research investigated an optimized solar–air source heat pump (SASHP) system to meet the heating demand of rural residences in this region. First, a typical rural building model was developed using SketchUp, and its heating load was simulated using TRNSYS, revealing an average load of 3.38 kW and a peak load of 5.9 kW. Based on the latest technical standards, the SASHP system was designed and simulated using TRNSYS, achieving an overall coefficient of performance (COP) of 3.67 while maintaining indoor thermal comfort within ISO 7730 Category II. Subsequently, the system was optimized through GenOpt to minimize the annual equivalent cost, yielding key parameters: a 15 m² solar collector at a 40.75° tilt, a 0.35 m³ water tank, and a 10.16 kW air source heat pump. Compared with the initial design, the optimized configuration achieved reductions of 35.60% in initial investment and 32.68% in annual equivalent costs. By ensuring thermal comfort and overcoming the economic barrier, this study provides a viable pathway for adoption and promotion of renewable heating technology in rural areas. Full article

The growing energy demand in the residential sector, driven by the extensive use of air conditioning systems, poses serious environmental and economic challenges. A sustainable alternative is the use of efficient insulating materials derived from waste resources. This study presents the synthesis of glass–ceramic foams produced from recycled glass (90 wt%), pumice (5 wt%), and limestone (5 wt%), sintered at 800 °C for 10 min. The resulting foams exhibited a low apparent density of 684 kg/${\mathrm{m}}^3$ and thermal conductivity of 0.09 W/m·K. These were incorporated into composite insulating panels composed of 70 wt% ceramic pellets and 30 wt% Portland cement, achieving a thermal conductivity of 0.18 W/m·K. The panels were evaluated in a 64.8 ${\mathrm{m}}^2$ social housing model located in Chihuahua, Mexico, using TRNSYS v.17 to simulate annual energy performance. Results showed that applying a 1.5-inch ceramic foam panel reduced the annual energy demand by 16.9% and the total energy cost by 14.7%, while increasing the panel thickness to 2 in improved savings to 18.4%. Compared with expanded polystyrene (EPS), which achieved 24.9% savings, the proposed ceramic panels offer advantages in fire resistance, durability, local availability, and environmental sustainability. This work demonstrates an effective, low-cost, and circular-economy-based solution for improving thermal comfort and energy efficiency in social housing. Full article

Heating, ventilation, and air conditioning (HVAC) systems are the primary energy consumers in modern office buildings, with chillers consuming the most energy. As critical components of building air conditioning, the effective functioning of HVAC systems holds substantial importance for energy preservation and emission mitigation. To enhance the operational performance of HVAC systems and accomplish energy conservation objectives, precise cooling load forecasting is essential. This research employs an office facility in Binzhou City, Shandong Province, as a case investigation and presents a day-ahead scheduling-based model predictive control (MPC) approach for HVAC systems, which targets minimizing the overall system power utilization. An attention mechanism-based long short-term memory (LSTM) neural network forecasting model is developed to predict the building’s cooling demand for the subsequent 24 h. Based on the forecasting outcomes, the MPC controller adopts the supply–demand equilibrium between cooling capacity and cooling demand as the central constraint and utilizes the particle swarm optimization (PSO) algorithm for rolling optimization to establish the optimal configuration approach for the chiller flow rate and temperature, thereby realizing the dynamic control of the HVAC system. To verify the efficacy of this approach, simulation analysis was performed using the TRNSYS simulation platform founded on the actual operational data and meteorological parameters of the building. The findings indicate that compared with the conventional proportional–integral–derivative (PID) control approach, the proposed day-ahead scheduling-based MPC strategy can attain an average energy conservation rate of 9.23% over a one-week operational period and achieve an energy-saving rate of 8.25% over a one-month period, demonstrating its notable advantages in diminishing building energy consumption. Full article

To address issues such as unstable heating loads, uneven heat consumption, and precise heating in solar heating systems, efficient and accurate heating load forecasting is essential. A suitable solar heating system model was established using the TRNSYS18 thermodynamic simulation platform. Taking a building in Alar City, Xinjiang, as the research subject, ultra-short-term prediction data parameters for the area were obtained. Using the acquired data parameters and historical heating load data as inputs, the particle swarm optimization (PSO) algorithm was employed to optimize the LSTM neural network, establishing a prediction model based on the PSO-LSTM neural network. For load forecasting in 7 min ultra-short-term time series, both the LSTM neural network model and the PSO-LSTM neural network prediction model underwent optimization. Through simulation experiments verifying indoor temperature, heat collection, and energy consumption, two model error evaluation metrics were used as results. Comparative analysis revealed that the PSO-LSTM model achieved a 3.3–86.7% increase in R² compared to the LSTM model, a 38.2–84.8% reduction in RMSE, a 57.8–91.1% decrease in MAE, and a 58–90.3% reduction in MAPE. The research results demonstrate the PSO-LSTM model’s effectiveness in southern Xinjiang, confirming its superiority as a forecasting model. This provides data support for operational adjustments and load forecasting in solar heating systems. Full article