A Simulation Method For The Effects Of Urban Microclimate On Building Cooling Energy Use

The microclimate around a building is a significant factor in building energyperformance. It’s an important direction for urban planning and building design to reduceenergy consumption by improving outdoor microclimate. However, the meteorological dataadopted in building energy simulation (BES) programs are usually derived from a localweather station or a nearby airport. These data do not reflect the modifying effect of thesurroundings of a building. In view of this, we developed a simulation method to assess theeffects of microlimate on building cooling energy use based on the3D microclimate modelENVI-met and the BES program EnergyPlus.Firstly, the microclimate model ENVI-met (version4.0) is validated by the urban flowfield model MISKAM and the field measurement data. A series of microclimatical variables,including wind velocity, wind pressure, soil temperature, ground surface temperature, airtemperature and air humidity, are analyzed. The validation results show that the ENVI-metmodel is capable of predicting the microclimate with good accuracy.Secondly, the ways that outdoor microclimate affects building energy performance areanalyzed from the heat balance processes of building rooms and envelops. Then a series ofcoupling strategies between ENVI-met and EnergyPlus are developed in terms of solarradiation, long-wave radiation, convection heat transfer, infiltration, ventilation andair-conditioner performance. A coupling module is developed in the software environment ofBuilding Controls Virtual Test Bed (BCVTB), which is used to transfer the simulation resultsof ENVI-met into the EnergyPlus model as outdoor meteorological boundary conditions.Through this method, the effects of microclimate on building energy performance can beincorporated into the EnergyPlus simulation.Thirdly, to solve the problem that ENVI-met is incapable of simulating microclimate forlong time scale (few months to a whole year), an indirect simulation method is developed toevaluate the effects of microclimate on building energy performance for long time scale. Themicroclimate data around a building is classified into two sub-datasets: the dataset of windspeed and wind pressure and the dataset of mean air temperature and air humidity. On theformer, the meteorological wind velocity is classified into16wind direction categories and18wind speed categories. Then the wind speed and wind pressure dataset can be generated bysimulating the flow field for each wind category. On the latter, the air temperature and airhumidity dataset for long time scale is generated by employing an objective synopticclimatological approach (based on principal component analysis and cluster analysis) and neural network ensembles prediction technique. Then, with the two sub-datasets, the effects ofmicroclimate on building energy performance for long time scale can be evaluated using theprevious coupling simulation method. The validation results show that the proposed long timescale simulation approach can significantly save computational resources with relatively littleloss in accuracy.Finally, a series of numerical experiments were carried out to show the characteristicsand potential applications of the proposed simulation method in urban planning and buildingdesign.