The present study proposes an improved method for generating better typical meteorological years( TMYs) used in building energy simulation in China. Modifications are made to the commonly used Sandia method,by optimiz...The present study proposes an improved method for generating better typical meteorological years( TMYs) used in building energy simulation in China. Modifications are made to the commonly used Sandia method,by optimizing the weightings of indices and thus giving more emphasis to dry bulb temperature and relative humidity and less to wind velocity. After analyzing the solar heat gain on the vertical envelop,an index of diffuse radiation rather than direct normal radiation is added for solar radiation. Using the improved method proposed,TMYs for 5 representative cities of 5 major climate zones in China are generated from the meteorological data recorded during the period 1981—2010. The results show that,compared with previous studies,the monthly diffuse solar radiation of typical meteorological months generated by the improved method are the"closest"to the 30-year average,and the comparison between annual diffuse radiation for the TMY and the 30-year annual average is improved,while little adverse effect is produced on global horizontal radiation comparisons,indicating that the improved method is more suitable to generate the TMY data than previous studies.展开更多
In the simulation of building overheating risks,the use of typical meteorological years(TMY)can greatly reduce the simulation workload and accurately reflect the distribution of simulation results according to the wea...In the simulation of building overheating risks,the use of typical meteorological years(TMY)can greatly reduce the simulation workload and accurately reflect the distribution of simulation results according to the weather conditions over a given period.However,all meteorological parameters in most current TMY methods use a uniform weighting factor which may make the simulation results against the actual simulation results of the period and negatively affect the accuracy of the evaluation results.In addition to differences in climate characteristics between climate zones,the sensitivity of different simulation results to external parameters will also be different.Therefore,a TMY method based on the Finkelstein-Schafer statistical method is proposed,which considers the climatic characteristics of different regions and the correlation with the output parameters of indoor simulation to select the typical month.The proposed method is demonstrated in the three future scenarios for the three cities in different climate zones in China.The results show that the traditional TMY method has an overestimated weight of solar radiation and wind speed and an undervalued weight of dry bulb temperature when indoor temperature-related indicators are the output target.Compared with the traditional TMY method,the TMY generated by the improved method is closer to the distribution characteristics of the long-term outdoor weather data.Furthermore,when using the improved TMY data to evaluate the overheating performance of the passive residential buildings,the difference of the results of the unmet degree hours,indoor overheating degree,and the overheating escalation factor between the long-term projected data and the TMY data can be reduced by 63%–67%compared with the traditional TMY data.展开更多
基金Sponsored by the National Natural Science Foundation of China(Grant No.51278349)Foundation of Fujian Provincial Education Department(Grant No.JA13185)
文摘The present study proposes an improved method for generating better typical meteorological years( TMYs) used in building energy simulation in China. Modifications are made to the commonly used Sandia method,by optimizing the weightings of indices and thus giving more emphasis to dry bulb temperature and relative humidity and less to wind velocity. After analyzing the solar heat gain on the vertical envelop,an index of diffuse radiation rather than direct normal radiation is added for solar radiation. Using the improved method proposed,TMYs for 5 representative cities of 5 major climate zones in China are generated from the meteorological data recorded during the period 1981—2010. The results show that,compared with previous studies,the monthly diffuse solar radiation of typical meteorological months generated by the improved method are the"closest"to the 30-year average,and the comparison between annual diffuse radiation for the TMY and the 30-year annual average is improved,while little adverse effect is produced on global horizontal radiation comparisons,indicating that the improved method is more suitable to generate the TMY data than previous studies.
基金The first author gratefully acknowledges the financial support from the Chinese Scholarship Council(CSC No.202007000086).
文摘In the simulation of building overheating risks,the use of typical meteorological years(TMY)can greatly reduce the simulation workload and accurately reflect the distribution of simulation results according to the weather conditions over a given period.However,all meteorological parameters in most current TMY methods use a uniform weighting factor which may make the simulation results against the actual simulation results of the period and negatively affect the accuracy of the evaluation results.In addition to differences in climate characteristics between climate zones,the sensitivity of different simulation results to external parameters will also be different.Therefore,a TMY method based on the Finkelstein-Schafer statistical method is proposed,which considers the climatic characteristics of different regions and the correlation with the output parameters of indoor simulation to select the typical month.The proposed method is demonstrated in the three future scenarios for the three cities in different climate zones in China.The results show that the traditional TMY method has an overestimated weight of solar radiation and wind speed and an undervalued weight of dry bulb temperature when indoor temperature-related indicators are the output target.Compared with the traditional TMY method,the TMY generated by the improved method is closer to the distribution characteristics of the long-term outdoor weather data.Furthermore,when using the improved TMY data to evaluate the overheating performance of the passive residential buildings,the difference of the results of the unmet degree hours,indoor overheating degree,and the overheating escalation factor between the long-term projected data and the TMY data can be reduced by 63%–67%compared with the traditional TMY data.
