查看更多>>摘要：Connecting occupants to the outdoor environment and incorporating biophilic design principles are challenging in extreme Arctic climatic conditions.Existing Arctic housing models do not provide efficient thermal and daylight transitions which are essential for the well-being and cultural needs of their occupants.To address these challenges,this research develops free-running biophilic intermediate spaces,integrated into an existing Arctic housing model.Numerical simulation methods are employed to optimize the primary and secondary architectural design variables for 26 case studies of intermediate spaces.Primary variables include volume,transparency ratio,and orientation.Secondary variables include materials and physical adjacency.Temperature,Daylight Factor/Autonomy,and Energy Use are evaluated as performance indicators.Results reveal that free-running intermediate spaces with 6 meters depth and a transparency ratio above 50%provide efficient indoor-outdoor transitions regarding thermal,visual,and energy performance.Such architectural configurations contribute to an approximately 5%reduction in energy consumption within the housing unit compared to the baseline.Opening side windows prevents the risk of overheating during the summer by reducing the average indoor temperature of intermediate spaces by 7 ℃ but increases the overall energy consumption.As a potential alternative to double-glazing,polycarbonate sheets enable efficient thermal performance by increasing the average indoor temperature of intermediate spaces by approximately 15 ℃ during the cold Arctic seasons.Using polycarbonate sheets results in a 16.6%reduction in energy consumption compared to using double-glazing material in intermediate space,and a 26%reduction from the baseline.Research outcomes contribute to efficient indoor-outdoor connections and energy efficiency in Arctic housing.
查看更多>>摘要：Building-level loads and load schedules prescribed by current modeling rules save modelers time and provide standards during whole building performance modeling.However,recent studies show that they sometimes insufficiently capture the entire building performance due to the varied loads and load schedules for different space types.As a solution to this issue,this paper presents a database of default building-space-specific loads and load schedules for use in energy modeling,and in particular code compliance modeling for commercial buildings.The existing sets of default loads and load schedules are reviewed and the challenges behind using them for specific research topics are discussed.Then,the proposed method to develop the building-space-specific loads and load schedules is introduced.After that,the database for these building-space-specific loads and load schedules is presented.In addition,one case is studied to demonstrate the applications of these loads and load schedules.In this case study,three methods are used to develop building energy models:space-specific(using knowledge of the distribution and location of space types and applying the space-specific data in the developed database),building-level(assuming a lack of knowledge of the space types and using the building-level data in the developed database),and calculated-ratio(assuming knowledge of the distribution of space types but not their locations and calculating weighted average values based on the space-specific data in the developed database).The energy results simulated by using these three methods are compared,which shows building-level methods can produce significantly different absolute energy and energy savings results than the results using space-specific methods.Finally,this paper discusses the application scope and maintenance of this new database.
查看更多>>摘要：Changing climate intensifies heat stress,resulting in a greater risk of workplace productivity decline in timber office buildings with low internal thermal mass.The impact of climate change induced heat exposure on indoor workplace productivity in timber office buildings has not been extensively researched.Therefore,further investigation to reduce the work capacity decline towards the end of the century is needed.Here,heat exposure in a net zero-carbon timber building near Brussels,Belgium,was evaluated using a reproducible comparative approach with different internal thermal mass levels.The analysis indicated that strategies with increased thermal mass were more effective in limiting the effects of heat exposure on workplace productivity.The medium and high thermal mass strategies reduced workplace productivity loss to 0.1%in the current,0.3%and 0.2%in the midfuture,and 4.9%and 3.9%for future scenarios.In comparison,baseline with low thermal mass yielded a decline of 2.3%,3.3%,and 8.2%.The variation in maximum and minimum wet-bulb globe temperatures were also lower for medium and high thermal mass strategies than for low thermal mass baseline.The study findings lead to the formulation of design guidelines,identification of research gaps,and recommendations for future work.
查看更多>>摘要：To investigate the impact of building heat transfer on roof snow loads,roof snow loads and snow load thermal coefficients from 61 Chinese sites over a period of 50 years are simulated based on basic meteorological data such as temperature,humidity,wind speed,and precipitation,and a multi-layer snowmelt model considering the building heat transfer.Firstly,the accuracy of the multi-layer snowmelt model is validated using the data of observed ground snow load and roof snow melting tests.The relationship between meteorological conditions,snow cover characteristics,and thermal coefficients of snow loads in three representative sites is then studied.Furthermore,the characteristics of thermal coefficients in each zone are analyzed by combining them with the statistical results of meteorological data from 1960 to 2010,and the equations of thermal coefficients in different zones on indoor temperatures and roof heat transfer coefficients are fitted separately.Finally,the equations in this paper are compared with the thermal coefficients in the main snow load codes.The results indicate that the snowmelt model using basic meteorological data can effectively provide samples of roof snow loads.In the cold zone where the snow cover lasts for a long time and does not melt easily,the thermal coefficients of the snow loads on the heating buildings are lower than those in the warm zone due to the long-term influence of the heat from inside the buildings.Thermal coefficients are negatively correlated with indoor temperatures and roof heat transfer coefficients.When the indoor temperature is too low or the roof insulation is good,the roof snow load may exceed the ground snow load.The thermal coefficients for heated buildings in the main snow load codes are more conservative than those calculated in this paper,and the thermal coefficients for buildings with lower indoor temperatures tend to be smaller.
查看更多>>摘要：Retrofitting a historic building under different national goals involves multiple objectives,constraints,and numerous potential measures and packages,therefore it is time-consuming and challenging during the early design stage.This study introduces a systematic retrofitting approach that incorporates standard measures for the building envelope(walls,windows,roof),as well as the heating,cooling,and lighting systems.Three retrofit objectives are delineated based on prevailing Chinese standards.The retrofit measures function as genes to optimize energy-savings,carbon emissions,and net present value(NPV)by employing a log-additive decomposition approach through energy simulation techniques and NSGA-II,yielding 185,163,and 8 solutions.Subsequently,a weighted sum method is proposed to derive optimal solutions across multiple scenarios.The framework is applied to a courtyard building in Nanjing,China,and the outcomes of the implementation are scrutinized to ascertain the optimal retrofit package under various scenarios.Through this retrofit,energy consumption can be diminished by up to 63.62%,resulting in an NPV growth of 151.84%,and maximum rate of 60.48%carbon reduction.These three result values not only indicate that the optimal values are achieved in these three aspects of energy saving,carbon reduction and economy,but also show the possibility of possible equilibrium in this multi-objective optimization problem.The framework proposed in this study effectively addresses the multi-objective optimization challenge in building renovation by employing a reliable optimization algorithm with a computationally efficient reduced-order model.It provides valuable insights and recommendations for optimizing energy retrofit strategies and meeting various performance objectives.
查看更多>>摘要：Bifacial PV modules capture solar radiation from both sides,enhancing power generation by utilizing reflected sunlight.However,there are difficulties in obtaining ground albedo data due to its dynamic variations.To address this issue,this study established an experimental testing system on a rooftop and developed a model to analyze dynamic albedo variations,utilizing specific data from the environment.The results showed that the all-day dynamic variations in ground albedo ranged from 0.15 to 0.22 with an average of 0.16.Furthermore,this study evaluates the annual performance of a bifacial PV system in Beijing by considering the experimental conditions,utilizing bifacial modules with a front-side efficiency of 21.23%and a bifaciality factor of 0.8,and analyzing the dynamic all-day albedo data obtained from the numerical module.The results indicate that the annual radiation on the rear side of bifacial PV modules is 278.90 kWh/m2,which accounts for only 15.50%of the front-side radiation.However,when using the commonly default albedo value of 0.2,the rear-side radiation is 333.01 kWh/m2,resulting in an overestimation of 19.40%.Under dynamic albedo conditions,the bifacial system is predicted to generate an annual power output of 412.55 kWh/m2,representing a significant increase of approximately 12.37%compared to an idealized monofacial PV system with equivalent front-side efficiency.Over a 25-year lifespan,the bifacial PV system is estimated to reduce carbon emissions by 8393.91 kgCO2/m2,providing an additional reduction of 924.31 kgCO2/m2 compared to the idealized monofacial PV system.These findings offer valuable insights to promote the application of bifacial PV modules.
查看更多>>摘要：Fault detection and diagnosis are essential to the air conditioning system of the data center for elevating reliability and reducing energy consumption.This study proposed a convolutional neural network(CNN)based data-driven fault detection and diagnosis model considering temporal dependency for composite air conditioning system that is capable of cooling the high heat flux in data centers.The input of fault detection and diagnosis model was an unsteady dataset generated by the experimentally validated transient mathematical model.The dataset concerned three typical faults,including refrigerant leakage,evaporator fan breakdown,and condenser fouling.Then,the CNN model was trained to construct a map between the input and system operating conditions.Further,the performance of the CNN model was validated by comparing it with the support vector machine and the neural network.Finally,the score-weighted class mapping activation method was utilized to interpret model diagnosis mechanisms and to identify key input features in various operating modes.The results demonstrated in the pump-driven heat pipe mode,the accuracy of the CNN model was 99.14%,increasing by around 8.5%compared with the other two methods.In the vapor compression mode,the accuracy of the CNN model achieved 99.9%and declined the miss rate of refrigerant leakage by at least 61%comparatively.The score-weighted class mapping activation results indicated the ambient temperature and the actuator-related parameters,such as compressor frequency in vapor compression mode and condenser fan frequency in pump-driven heat pipe mode,were essential features in system fault detection and diagnosis.
查看更多>>摘要：Indoor environment quality(IEQ)is one of the most concerned building performances during the operation stage.The non-uniform spatial distribution of various IEQ parameters in large-scale public buildings has been demonstrated to be an essential factor affecting occupant comfort and building energy consumption.Currently,IEQ sensors have been widely employed in buildings to monitor thermal,visual,acoustic and air quality.However,there is a lack of effective methods for exploring the typical spatial distribution of indoor environmental quality parameters,which is crucial for assessing and controlling non-uniform indoor environments.In this study,a novel clustering method for extracting IEQ spatial distribution patterns is proposed.Firstly,representation vectors reflecting IEQ distributions in the concerned space are generated based on the low-rank sparse representation.Secondly,a multi-step clustering method,which addressed the problems of the"curse of dimensionality",is designed to obtain typical IEQ distribution patterns of the entire indoor space.The proposed method was applied to the analysis of indoor thermal environment in Beijing Daxing international airport terminal.As a result,four typical temperature spatial distribution patterns of the terminal were extracted from a four-month monitoring,which had been validated for their good representativeness.These typical patterns revealed typical environmental issues in the terminal,such as long-term localized overheating and temperature increases due to a sudden influx of people.The extracted typical IEQ spatial distribution patterns could assist building operators in effectively assessing the uneven distribution of IEQ space under current environmental conditions,facilitating targeted environmental improvements,optimization of thermal comfort levels,and application of energy-saving measures.
查看更多>>摘要：The air infiltration rate of buildings strongly influences indoor environment and energy consumption.In this study,several traditional methods for determining the air infiltration rate were compared,and their accuracy in different scenarios was examined.Additionally,a method combining computational flow dynamics(CFD)with the Swami and Chandra(S-C)model was developed to predict the influence of the surrounding environment on the air infiltration rate.Two buildings in Dalian,China,were selected:one with a simple surrounding environment and the other with a complex surrounding environment;their air infiltration rates were measured.The test results were used to validate the accuracy of the air infiltration rate solution models in different urban environments.For the building with a simple environment,the difference between the simulation and experimental results was 0.86%-22.52%.For the building with a complex environment,this difference ranged from 17.42%to 159.28%.We found that most traditional models provide accurate results for buildings with simple surrounding and that the simulation results widely vary for buildings with complex surrounding.The results of the method of combining CFD with the S-C model were more accurate,and the relative error between the simulation and test results was 10.61%.The results indicate that the environment around the building should be fully considered when calculating the air infiltration rate.The results of this study can guide the application of methods of determining air infiltration rate.
查看更多>>摘要：The global prevalence of infectious diseases has emerged as a significant challenge in recent years.Surface transmission is a potential transmission route of most gastrointestinal and respiratory infectious diseases,which is related to surface touch behaviors.Manual observation,the traditional method of surface touching data collection,is characterized by limited accuracy and high labor costs.In this work,we proposed a methodology based on machine learning technologies aimed at obtaining high-accuracy and low-labor-cost surface touch behavioral data by means of sensor-based contact data.The touch sensing device,primarily utilizing a film pressure sensor and Arduino board,is designed to automatically detect and collect surface contact data,encompassing pressure,duration and position.To make certain the surface touch behavior and to describe the behavioral data more accurately,six classification algorithms(e.g.Support Vector Machine and Random Forest)have been trained and tested on an experimentally available dataset containing more than 500 surface contacts.The classification results reported the accuracy of above 85%for all the six classifiers and indicated that Random Forest performed best in identifying surface touch behaviors,with 91.8%accuracy,91.9%precision and 0.98 AUC.The study conclusively demonstrated the feasibility of identifying surface touch behaviors through film pressure sensor-based data,offering robust support for the calculation of viral load and exposure risk associated with surface transmission.
NETL
NSTL
万方数据