查看更多>>摘要：In the United States,the buildings sector consumes about 76％of electricity use and 40％of all primary energy use and associated greenhouse gas emissions.Occupant behavior has drawn increasing research interests due to its impacts on the building energy consumption.However,occupant behavior study at urban scale remains a challenge,and very limited studies have been conducted.As an effort to couple big data analysis with human mobility modeling,this study has explored urban scale human mobility utilizing three months Global Positioning System(GPS)data of 93,000 users at Phoenix Metropolitan Area.This research extracted stay points from raw data,and identified users'home,work,and other locations by Density-Based Spatial Clustering algorithm.Then,daily mobility patterns were constructed using different types of locations.We propose a novel approach to predict urban scale daily human mobility patterns with 12-hour prediction horizon,using Long Short-Term Memory(LSTM)neural network model.Results shows the developed models achieved around 85％average accuracy and about 86％mean precision.The developed models can be further applied to analyze urban scale occupant behavior,building energy demand and flexibility,and contributed to urban planning.
查看更多>>摘要：The pollutant control performance of exhaust hoods plays a crucial role in the indoor air quality and energy consumption of ventilation systems in industrial buildings.To better understand the impact of local ventilation on the industrial indoor environment,this paper presents a literature review of exhaust hood performance and its improvement technologies.To create an index for evaluating the performance of exhaust hoods,the capture velocity,capture efficiency,flow ratio of pollutant emissions and exhaust airflow and energy consumption are first introduced.A number of factors affecting exhaust hood performance are assessed such as hood type,hood opening size,exhaust rate,installation distance,pollution source emission and environmental disturbance.Compared to structural improvement methods,the use of active airflow is a more effective way to improve the exhaust hood performance.The most commonly used methods for determining the exhaust rate are the controlled speed method and the flow ratio method.The use of an exhaust hood with an appropriate exhaust rate and jet parameters(for an active air-assisted hood)can effectively improve the pollutant control performance and reduce the energy consumption that would be wasted on the redundant exhaust rate.With more information focused on exhaust hood performance,this work suggests more effective strategies for improving indoor air quality and reducing energy consumption in industrial buildings.
Aleksandar Ne?ovi?Neboj?a Juri?evi?Robert KowalikIvana Terzi?...
41-52页
查看更多>>摘要：This study investigates the potential of contemporary Earth-Sheltered Buildings to contribute to sustainable societies by achieving Plus Energy Building status during the heating season.The buildings analyzed are intended to house a four-member family(102.5 m2).The house is equipped with a ground source heat pump,geothermal vertical probes,floor heating panels,an energy recovery ventilator,and building-integrated photovoltaic panels installed on the exposed building wall.The numerical analysis(using EnergyPlus software)was carried out for nine European sites covering five different climatic zones(namely:southern,Atlantic,continental,alpine,and northern climate)and three latitude belts spanning Europe from south to north.Buildings above 65° N(northern and alpine climate)could not achieve Plus Energy status with 20％efficient BIPVs.With 15％efficient BIPVs,the building above 59° N(northern climate)was incapable of achieving this as well.Regarding the monthly time step,the buildings in the southern and Atlantic climates with 15％efficient BIPVs were able to produce an energy surplus every month,as was the building in the Alps with 20％efficient BIPVs.
查看更多>>摘要：The screw air-source heat pump can cause incessant high noise levels during operation,which might hinder adoption of this energy-efficient heat pump.First,acoustic measurements and comparison testing were performed in this research.The measurements revealed that the compressor is the main noise source of the heat pump,and it shows a multipeak frequency distribution and a wide frequency spectrum under different work conditions,with multiple peaks at 63,250,and 1000 Hz.Then,a compressor sound insulation cover with broadband absorption was proposed,and it was experimentally proven that the insulation cover can reduce the maximum sound pressure level of one unit from 89.8 dBA to 79.1 dBA.Third,we proposed several noise reduction strategies and compared their noise reduction effects using computer simulation.The results showed that the noise problem can be effectively improved through the rational design of the sound barrier and the layout and opening options of heat pump.The distance between the sound barrier and heat pump and the sound attenuation due to diffraction ΔLd exhibit a U-shaped relation.For buildings of different heights,the optimal heights of noise barrier are proposed.The 5.5-meter is the optimal height of the sound barrier for single-story buildings.The conclusions can be applied to other building projects for heat pump noise reduction.
查看更多>>摘要：Model predictive control(MPC)is an advanced control technique.It has been deployed to harness the energy flexibility of a building.MPC requires a dynamic model of the building to achieve such an objective.However,developing a suitable predictive model is the main challenge in MPC implementation for flexibility activation.This study focuses on the application of key performance indicators(KPIs)to evaluate the suitability of MPC models via feature selection.To this end,multiple models were developed for two houses.A feature selection method was developed to select an appropriate feature space to train the models.These predictive models were then quantified based on one-step ahead prediction error(OSPE),a standard KPI used in multiple studies,and a less-often KPI:multi-step ahead prediction error(MSPE).An MPC workflow was designed where different models can serve as the predictive model.Findings showed that MSPE better demonstrates the performance of predictive models used for flexibility activation.Results revealed that up to 57％of the flexibility potential and 48％of energy use reduction are not exploited if MSPE is not minimized while developing a predictive model.
查看更多>>摘要：There is growing evidence of the high transmission potential of COVID-19 through virus-laden aerosols.Because aerosols are inhaled in various concentrations,an overall assessment of transmission risks at different indoor scales is crucial.However,a comprehensive risk assessment method that evaluates the direct link between short-range and room-scale zones is still lacking.In this paper,a risk assessment model combining both short-range and room-scale effects is developed to obtain effective reproduction number in confined indoor environments,called Turbulent Jet Wells Riley(TJWR)model.Combined with the viral load data and aerosol generation data of different human respiratory activities,the Monte Carlo simulation method is applied to calculate the quanta emission rate,which further provides the input parameters of the TJWR model.Three known outbreaks(Hangzhou banquet hall X,Guangzhou restaurant Y,and Hong Kong restaurant Z,China)are chosen to validate the TJWR model.Results show that the TJWR model is more efficient than the original Wells-Riley model.The average relative error of the TJWR model ranges between 9％and 44％,while for the Wells-Riley model,it ranges between 57％and 78％.The TJWR model also proves that infection risk assessments using the well-mixed assumption can systematically underestimate the transmission risk for those close to the source.Additionally,there is a significant positive linear correlation between the total number of exposed individuals at the short-range and the effective reproduction number.This newly developed TJWR model has great potential for rapid and real-time overall airborne transmission risk assessment in buildings and cities.
查看更多>>摘要：As infectious respiratory diseases are highly transmissible through the air,researchers have improved traditional total volume air distribution systems to reduce infection risk.Multi-vent module-based adaptive ventilation(MAV)is a novel ventilation type that facilitates the switching of inlets and outlets to suit different indoor scenarios without changing ductwork layout.However,little research has evaluated MAV module sizing and air velocity selection,both related to MAV system efficiency in removing contaminants and the corresponding level of protection for occupants in the ventilated room.Therefore,the module-source offset ratio(MSOR)is proposed,based on the MAV module size and its distance from an infected occupant,to inform selection of optimal MAV module parameters.Computational fluid dynamics simulations illustrated contaminant distribution in a two-person MAV equipped office.Discrete phase particles modelled respiratory contaminants from the infected occupant,and contaminant concentration distributions were compared under four MAV air distribution layouts,three air velocities,and three module sizes considered using the MSOR.Results indicate that lower air velocities favour rising contaminant levels,provided the ventilation rate is met.Optimal contaminant discharge can be achieved when the line of outlets is located directly above the infected occupant.Using this parameter to guide MAV system design,85.7％of contaminants may be rendered harmless to the human body within 120 s using the default air vent layout.A more appropriate supply air velocity and air vent layout increases this value to 91.4％.These results are expected to inform the deployment of MAV systems to reduce airborne infection risk.
查看更多>>摘要：Occupant-centric controls(OCC)is an indoor climate control approach whereby occupant feedback is used in the sequence of operation of building energy systems.While OCC has been used in a wide range of building applications,an OCC category that has received considerable research interest is learning occupants'thermal preferences through their thermostat interactions and adapting temperature setpoints accordingly.Many recent studies used reinforcement learning(RL)as an agent for OCC to optimize energy use and occupant comfort.These studies depended on predicted mean vote(PMV)models or constant comfort ranges to represent comfort,while only few of them used thermostat interactions.This paper addresses this gap by introducing a new off-policy reinforcement learning(RL)algorithm that imitates the occupant behaviour by utilizing unsolicited occupant thermostat overrides.The algorithm is tested with a number of synthetically generated occupant behaviour models implemented via the Python API of EnergyPlus.The simulation results indicate that the RL algorithm could rapidly learn preferences for all tested occupant behaviour scenarios with minimal exploration events.While substantial energy savings were observed with most occupant scenarios,the impact on the energy savings varied depending on occupants'preferences and thermostat use behaviour stochasticity.
查看更多>>摘要：Along with the improvement of social productivity and living standard,residential buildings generate a growing portion of carbon emissions,especially during the operation stage.However,energy use behaviors are usually ignored in carbon emission calculation.This study focuses on calculating carbon emissions during the operation stage for residential buildings based on the characteristics of energy use behaviors in different regions.Firstly,we investigated energy use behaviors in dwellings across three cities in China:Xi'an,Shanghai and Fuzhou.Then,we established calibrated carbon emission models and optimization models with different green building measures for residential buildings.The results of this research reveal a significant disparity between the energy usage habits of residents in different climate regions.The carbon emissions of residential electricity bills in Xi'an,Shanghai and Fuzhou are 13.6 kgCO2/(m2·a)(excluding central heating),29.3 kgCO2/(m2·a)and 17.2 kgCO2/(m2·a),respectively.Equipment carbon emissions account for 32.2％-64.1％of the total.In comparison to the model based on internal standard setting,the accuracy of the models using actual internal has improved by 25.9％-37.4％.The three-star green building methods have the highest carbon reduction rate among different star buildings,the emission reduction rates are around 30％.This study's findings are useful for carbon emission calculation and green building design of residential buildings in the future.
万方数据
维普