查看更多>>摘要：Using the behind-meter data,this study applied a comparison and optimization-based framework to evaluate the energy flexibility and resilience of distributed energy resources within existing houses during cold wave event.Comparative analysis demonstrates the effectiveness of high envelope insulation level in improving energy resilience,identifies impacts of distributed energy resources on variations of household electricity demand.Specifically,a 14.6％reduction in the median value of the normalized load of building group with low U-values,implementations of cogeneration system effectively suppressed variations of electricity load.Dynamic energy performances of on-site generators are evaluated based on high resolution data,energy flexibility of domestic hot water and thermostatically controlled loads were investigated through built demand response model.Results reveal that electrifying hot water demand offers additional power flexibility,the integration of fuel cell cogeneration system has proven to be an efficient energy resource,enabling on-site generation of both electricity and hot water,substantially reducing grid import.The extreme cold event resulted in significant spikes in space heating power consumption.The optimization results demonstrate that reducing the indoor setpoint temperature effectively decreases daily power consumption by approximately 5.0％per degree Celsius.These findings help acquire better understanding of interconnections between energy efficiency and resilience of residential energy-efficient measures.
查看更多>>摘要：While the grid-connected capacity of rural household photovoltaics is increasing rapidly,achieving dynamic supply-demand matching despite fluctuations in solar energy is challenging.With the rapid development of rural electrification,battery-powered technologies,such as electric vehicles and electric agricultural machinery,are becoming increasingly popular in rural areas.In this context,utilizing idle mobile batteries to assist in energy storage for rural residential buildings offers a new way to solve the problem of dynamic supply-demand matching.In this study,a field survey was conducted on several typical fruit-growing villages in the Central Shaanxi Plain in Shaanxi Province of China.Typical rural households were selected to calculate the electricity loads of the residential buildings,with due consideration to the intervention of mobile batteries.Under the premise of installing 3 kW household photovoltaic systems in rural households,an economical efficiency-oriented model was built for the optimal regulation of flexible loads.The results were compared in the context of two patterns of electricity consumption,i.e.,unidirectional charging of mobile batteries from buildings and bidirectional charging and discharging between mobile batteries and buildings.The bidirectional pattern significantly increased the photovoltaic consumption of typical rural households on various typical days.Specifically,during both scenarios of not implementing time-of-use and implementing time-of-use,the typical day of the winter slack farming season exhibited the best photovoltaic consumption effect among all types of typical days.Additionally,the bidirectional pattern also result in a significant increase in the annual electricity sales revenues for typical rural households.
查看更多>>摘要：The emergence of building condenser water systems with all-variable speed pumps and tower fans allows for increased efficiency and flexibility of chiller plants in partial load operation but also increases the control complexity of condenser water systems.This study aims to develop an integrated modeling technique for evaluating and optimizing the energy performance of such a condenser water system.The proposed system model is based on the semi-physical semi-empirical chiller,pump,and cooling tower models,with capabilities of fully considering the hydraulic and thermal interactions in the condenser water loop,being solved analytically and much faster than iterative solvers and supporting the explicit optimization of the pump and tower fan frequency.A mathematical approach,based on the system model and constrained optimization technique,is subsequently established to evaluate the energy performance of a typical dual setpoint-based variable speed strategy and find its energy-saving potential and most efficient operation by jointly optimizing pumps and tower fans.An all-variable speed chiller plant from Wuhan,China,is used for a case study to validate the system model's accuracy and explore its applicability.The results showed that the system model can accurately simulate the condenser water system's performance under various operating conditions.By optimizing the frequencies of pumps and tower fans,the total system energy consumption can be reduced by 12％-13％compared to the fixed dual setpoint-based strategy with range and approach setpoints of 4 ℃ and 2 ℃.In contrast,the energy-saving potential of optimizing the cooling tower sequencing is insignificant.A simple joint speed control method for optimizing the pumps and tower fans emerged,i.e.,the optimal pump and fan frequency are linearly correlated(if both are non-extremes)and depend on the chiller part load ratio only,irrespective of the ambient wet-bulb temperature and chilled water supply temperature.It was also found that the oversizing issue has further limited the energy-saving space of the studied system and results in the range and approach setpoints being inaccessible.The study's findings can serve as references to the operation optimization of all-variable speed condenser water systems in the future.
查看更多>>摘要：Due to the fast-modeling speed and high accuracy,deep learning has attracted great interest in the field of fault diagnosis in building energy systems in recent years.However,the black-box nature makes deep learning models generally difficult to interpret.In order to compensate for the poor interpretability of deep learning models,this study proposed a fault diagnosis method based on interpretable graph neural network(GNN)suitable for building energy systems.The method is developed by following three main steps:(1)selecting NC-GNN as a fault diagnosis model for building energy systems and proposing a graph generation method applicable to the model,(2)developing an interpretation method based on InputXGradient for the NC-GNN,which is capable of outputting the importance of the node features and automatically locating the fault related features,(3)visualizing the results of model interpretation and validating by matching with expert knowledge and maintenance experience.Validation was performed using the public ASHRAE RP-1043 chiller fault data.The diagnosis results show that the proposed method has a diagnosis accuracy of over 96％.The interpretation results show that the method is capable of explaining the decision-making process of the model by identifying fault-discriminative features.For almost all seven faults,their fault-discriminative features were correctly identified.
查看更多>>摘要：The dynamic characteristics of different airflows on micro-scales have been explored from many perspectives since the late 1970s.On the one hand,most analytical tools and research subjects in previous contributions vary significantly:some only focus on fluctuant velocity features,while others pay attention to directional features.On the other hand,despite the wide variety of existing analytical methods,they are not systematically classified and organized.This paper aims to establish a system including state-of-the-art tools for airflow analysis and to further design a holistic toolkit named Airflow Analytical Toolkit(AAT).The AAT contains two tools,responsible for analyzing the velocity and direction characteristics of airflows,each of which is integrated with multiple analytical modules.To assess the performance of the developed toolkit,we further take typical natural and mechanical winds as cases to show its excellent analytical capability.With the help of this toolkit,the great differences in velocity and directional characteristics among different airflows are identified.The comparative results reveal that not only is the velocity of natural wind more fluctuating than that of mechanical wind,but its incoming flow direction is also more varying.The AAT,serving as a powerful and user-friendly instrument,will hopefully offer great convenience in data analysis and guidance for a deeper understanding of the dynamic characteristics of airflows,and further remedy the gap in airflow analytical tools.
查看更多>>摘要：Gas leakage accidents occur frequently in confined spaces,and heavy gases with a relative density greater than 1.15 among hazardous gases and greenhouse gases are commonly stored in confined spaces.However,atmospheric pollutant emission standards are becoming more stringent,and it is essential to remove heavy gas after accidents while reducing emissions to the atmosphere.This study proposes using a heavy gas collection tank(HGCT)to safeguard the internal environment and minimize emissions to the atmosphere.The capture efficiencies applicable to heavy-gas environments under different ventilation strategies are derived.This research analyzes the impact of the exhaust rate,leakage rate,density of heavy gas,and air supply modes on the indoor concentration distribution.The results demonstrate that the mass flow rate of heavy gas into the exhaust is positively correlated with the exhaust rate,but the gas from the exhaust system contains more air.The exhaust rate should be greater than four times the space volume per hour;otherwise,heavy gas above 1000 ppm accumulates to a height of 0.67 m at ground level.Finally,attachment ventilation as make-up air helps to reduce upstream heavy gas accumulation and reduces the extension of heavy gas along the room width.Combining an HGCT with floor slope and attachment ventilation achieves an efficiency of 96.28％.This study provides valuable insights and references for preventing hazardous heavy gas leakage.
查看更多>>摘要：Opening windows in coach buses is a practical approach to improving natural ventilation and mitigating infection risk(IR).Due to human behavior and weather conditions,the intermittent window opening strategy(IWOS)is a more common practice than keeping windows constantly open.Despite its prevalence,there are no studies exploring IWOS specifically in vehicles.We employed indoor-outdoor coupled CFD simulations to assess the effects of various IWOS on pathogen-laden droplet(PLD)dispersion and IR in a coach bus that occurred a COVID-19 outbreak in Hunan,China.Results reveal that after ventilating through two skylights for 600-1800 s,opening front and rear windows(FW+RW)or FW with a wind catcher(FW+WCH)for just 40 s can reduce PLD concentration(Cave)to 5％of its initial level and the intake fraction of the infector's neighbor(IFn)drops by 95％.Upon closing FW+RW or FW+WCH,Caveand IFn take over 580 s to return to the pre-opening level.Moreover,intermittent FW opening halves Cave and IFn within 7 min,but leads to rapid increases upon window closure.Therefore,opening FW+RW and FW+WCH intermittently have pronounced impacts on indoor PLD concentration and are applicable approaches to control respiratory disease transmission in vehicles.According to the inhaled viral dose,it is recommended to open windows when driving time is over 12 minutes to reduce infection risk.In scenarios like epidemiological surveys and risk assessments,where assessing passenger infection risk is vital,some behaviors of opening windows cannot be overlooked and necessitate extra attention.
查看更多>>摘要：Indoor volatile organic compound(VOC)concentrations are often dynamic because the ventilation and emission rates of VOC usually change.Adsorption filters used for air purification may operate with a capacity that fluctuates with unsteady VOC concentrations in buildings.Modeling the dynamic interactions between adsorption filters and indoor air is crucial for predicting their performance under real-world conditions.This study presents a numerical model of partially reversible adsorption equilibrium coupled with a mass transfer model to create a predictive model for adsorption efficiency in environments with dynamic VOC concentrations.A honeycomb adsorption filter for benzene adsorption was simulated and tested,including the breakthrough and purging curve and the long-term efficiency in an experimental chamber with dynamic concentrations.The results reveal that the curve generated with the partially reversible adsorption equilibrium model closely aligns with the measured one.Furthermore,the model was coupled with a chamber model and the simulation results were compared with those calculated using the filter model with a single adsorption isotherm.When VOCs were emitted intermittently in the chamber and there was sufficient ventilation,the concentration peaks in the chamber derived from the models with different assumptions on adsorption reversibility were significantly different from each other.Moreover,it was observed that the reversible adsorption capacity of the filter was crucial for long-term operation in rooms with dynamic concentration.Despite the reversible adsorption capacity constituting only 6.7％of the total adsorption capacity of the tested filter,it contributes to a significant"peak shaving and valley filling"effect,even when the irreversible adsorption capacity is saturated.The adsorption reversibility should be taken as an important parameter for selecting adsorbents for dynamic concentration conditions.
查看更多>>摘要：Accurately predicting the chiller coefficient of performance(COP)is essential for improving the energy efficiency of heating,ventilation,and air conditioning(HVAC)systems,significantly contributing to energy conservation in buildings.Traditional performance prediction methods often overlook the dynamic interaction among sensor variables and face challenges in using extensive historical data efficiently,which impedes accurate predictions.To overcome these challenges,this paper proposes an innovative on-site chiller performance prediction method employing a dynamic graph convolutional network(GCN)enhanced by association rules.The distinctive feature of this method is constructing an association graph bank containing static graphs in each operating mode by mining the association rules between various sensor variables in historical operating data.A real-time graph is created by analyzing the correlation between various sensor variables in the current operating data.This graph is fused online with the static graph in the current operating mode to obtain a dynamic graph used for feature extraction and training of GCN.The effectiveness of this method has been empirically confirmed through the operational data of an actual building chiller system.Comparative analysis with state-of-the-art methods highlights the superior performance of the proposed method.
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