The Method And Application Of Determining The Weight Factors Of Typical Meteorological Year In Different Regions

Typical meteorological year(TMY)is an outdoor meteorological data set used for building energy consumption simulation.The accuracy of TMY will directly affect the accuracy of building energy consumption simulation and building energy efficiency design.At present,the generation method of TMY in our country mainly refers to the Finkelstein-Schafer statistical method proposed by the Sandia Laboratory in the United States,but this method has two flaws: 1.The weighting factors of different meteorological parameters are fixed,which ignores the climatic differences in different regions;2.In recent years,the research field of new energy buildings has developed rapidly,and the traditional Sandia method is not well applicable.The method of TMY generation for new energy buildings is still blank.Improving the generation method of TMY will be the main research content of this paper.This paper mainly conducts the following three aspects of research:(1)Research on the construction method of TMY weighting factors under different regional climate characteristics.Sandia method assigns fixed weighting factors to different meteorological parameters,but the distribution of meteorological resources in different regions is quite different.Whether using fixed weighting factors is scientific and reasonable remains to be verified.Therefore,this paper first adopts the random weighting method to verify that the difference of the weight factors will directly affect the selection results of TMY,and then proposes a method of reasonably correcting the weight factors of the meteorological parameters of TMY according to the distribution characteristics of meteorological resources,and takes Guangzhou as an example.The validity of the method is verified,and the research on the regional applicability of the traditional Sandia method is improved.(2)A new method of TMY generation.The more commonly used TMY generation methods include Sandia method,principal component analysis method and standard deviation method.This paper intends to develop a new theory of TMY generation-using the entropy-based TOPSIS theory.Entropy-based TOPSIS theory is an objective method.The determination of its weights and decision-making results does not involve any subjective preference,but completely relies on objective data sets,which is helpful to improve the existing method that cannot effectively reflect the shortcomings of climate differences between different regions,and effectively improve the accuracy of TMY selection results.Taking five typical representative cities in China as examples,comparing Sandia method and TPCY method,using single parameter deviation analysis and building energy consumption simulation to evaluate the accuracy of TMY.The results show that the TMY generated by the entropy-based TOPSIS method is closer to the longterm average.(3)Preliminary study on TMY generation method of new energy type buildings.At present,the construction industry is paying more and more attention to new energy type buildings,but the basic theoretical research on new energy buildings is relatively scarce,and the TMY section is still blank.In this paper,the solar building as a starting point to explore the new energy building TMY generation method.Based on Sandia method,from the perspective of production capacity,three candidate months are preliminarily determined by direct influencing factors;From the perspective of energy consumption,a new weight factor is constructed with correlation coefficient,and then the most suitable typical month is determined from the candidate months to form a typical year.To solve the current lack of TMY generation method for new energy buildings.According to the demand of building energy-saving design in China,this paper completes the generation method of TMY.Research on the basic scientific issues of TMY generation mehod will help buildings make rational use of climate resources.Design buildings according to local conditions,effectively reduce energy demand,and provide theoretical support for sustainable development of low energy consumption and reduction of greenhouse gas emissions.