Impact Of Atypical Climate On Building Performance Based On Historical Meteorological Data

Meteorological year data reflect the characteristics of climate change and are often used to represent typical or atypical local meteorological environments.The selection of meteorological year data is critical to building performance assessment and associated energy security,and the use of long-term average meteorological year data may diminish the impact of extreme climate change and the corresponding building energy response.Existing studies and engineering designs have mostly evaluated building performance under conventional meteorological conditions,and the results are somewhat different from the expected performance under actual meteorological conditions.On the other hand,individual studies on extreme meteorological conditions often deviate from the regular fluctuation range of meteorology.To address these issues,this paper creates a series of atypical meteorological year data for characterizing building meteorological environments with different degrees of extremes based on 37 years of ground-based meteorological observations in hot-summer and warm-winter regions,and load simulations for two types of typical buildings based on their frequency distribution of differences from long-term meteorological data.This paper focuses on the following aspects:1.Analyzing the current progress of weather year for building energy consumption simulation at home and abroad,several weather year methodologies are introduced in detail and the typical meteorological year TMY1,extreme meteorological year EMY,extreme warm year EWY and extreme cold year ECY weather data sets are generated respectively,and the weather data are compared and analyzed from the perspective of frequency and intensity.The results show that: the quantified mean values of temperature and solar radiation distribution differences between the atypical and typical meteorological year data can reach 8.1% and24.4%,respectively,and the mean values of peak occurrence frequency differences can reach7.6% and 6.6%,and the existence of rare extreme meteorological conditions can exacerbate such differences;each extreme meteorological year data represents the boundary conditions of extreme meteorological environments,which can reflect different degrees of atypical meteorological environments.2.The Filkenstein-Schafer statistical method was used to quantify the statistical characteristics of the meteorological parameters,and a series of weather data with different degrees of cold and warm weather were generated by combining the deviation coefficients DW and their absolute values |DW| weighted by the distribution characteristics of the meteorological parameters based on the observed values for each candidate year.The general cold year CY and general warm year WY corresponding to the closest|DW|median DW values,respectively,represent weather conditions that generally deviate from the long-term average meteorological environment;the near-extreme cold year N-XCY and near-extreme warm year N-XWY,which round off the anomalous values in the DW distribution,represent weather conditions that exclude the rarely seen extreme meteorological environment.Meanwhile,comparing the deviations of single-year and long-term meteorological conditions,it is found that there is a general difference between the meteorological environment of a single natural year and its overall statistical characteristics,so that the building performance in the actual meteorological environment and the TMY-based assessment results are somewhat different,and the relative magnitude of the difference is inversely proportional to its probability of occurrence.The characteristics of the month in which the anomalies are located are investigated,and the results show that the month in which the anomalies are located has low probability,prevalence,and great variability compared to the long-term climate.The solar radiation,dry bulb temperature,wind speed,dew point temperature,and relative humidity in the developed atypical weather year data are compared with the typical weather year data,and it is found that the atypical weather year data set distinguishes the warm and cold degrees of the atypical environment within the distribution of the conventional weather data,and better characterizes the different degrees of the atypical weather environment.3.Based on load simulations of a typical commercial building in Hong Kong and a typical residential building in the Guangdong-Hong Kong-Macao Greater Bay Area,the impact of the selection logic of seven sets of meteorological data for C/WY,N-XC/WY,EC/WY,and TMY on building performance assessment is compared.The simulation results for the two types of buildings show that for some months,the impact of extreme weather conditions can produce 10% and 20% difference in load calculation for N-XC/WY and EC/WY without excluding abnormal cooling and heating conditions,respectively;meanwhile,C/WY and N-XC/WY can also produce 8% and 17% difference in load calculation,respectively;C/WY and TMY can produce 10% and 20% difference in load calculation for mainly cooling months,respectively.The difference between C/WY and TMY can be 4%-10%and 1%-13%,respectively,in June-October,when cooling is the main supply.Analyzing the frequency of different levels of cold and warm weather conditions is important to construct their corresponding weather data sets and thus to comprehensively assess the impact of weather uncertainty on building performance.