Comparison of methodologies for TMY generation using15 years data for Chillan, Chile

Abstract

Typical meteorological year (TMY) is often used in the solar thermal field. Several methods to generate TMYs have been developed. However, results vary depending on the method used, which has a direct effect on the design of solar thermal systems. Because of this, it is necessary to establish a methodology for determining the TMY to be used. In this study were compared, five methods to generate TMYs with different weighting factors. TMYs were generated with weather data of a 15-year period (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012) for Chillán, Chile (latitude 36.6° south). A solar thermal hot water system was evaluated in TRNSYS, using all TMYs generated. It was calculated Solar factor (SF), namely the fraction of solar energy used by the system throughout the year, for each TMYs and the years of data. Mean and median were then calculated for each month to all years. These values were compared to values obtained of SF from the TMYs. The TMY that presented the lowest difference in the 12 months with respect to those values was determined as the most representative. The selected TMY was generated by using ASRHAE based on dew point temperatures, and it is recommended for future studies for Chillán.

The effects of different typical meteorological year data on the heating and cooling demand of buildings: Case study of Turkiye

“…WCs can be seen in Table 1 [3]. Various researchers have also used the relative humidity in TMY studies [7,8,25,26], however, Sepulveda et al stated that more accurate results could be obtained when dew point temperature was selected [27]. Different weather files valid for Türkiye were investigated and files provided by Energyplus and Meteonorm were chosen by Ganiç [28].…”

The Generation of Typical Meteorological Year for Tripoli, Libya

“…In the past, several methodologies for generating TMYs have been reported, such as Sandia method, Festa-Ratto method, Danish method and others [6], all targeted at selecting single months or years from a long-term typical weather condition. Among the different TMY generation methods, the Sandia method is widely adopted [1, [3][4][5][6][7][8][9][10][11][12][13][14] generated TMYs for different locations with different weather parameters and assigned weighting factors. This methodology has been adopted by different countries: for example, by date of publication, for Latvia [9], Athens, Greece [12], Oman [11], Damascus, Syria [6], Nigeria [3,[7][8], Chile [10], Istanbul, Turkey [14], and Armidale NSW, Australia [14].…”

“…Among the different TMY generation methods, the Sandia method is widely adopted [1, [3][4][5][6][7][8][9][10][11][12][13][14] generated TMYs for different locations with different weather parameters and assigned weighting factors. This methodology has been adopted by different countries: for example, by date of publication, for Latvia [9], Athens, Greece [12], Oman [11], Damascus, Syria [6], Nigeria [3,[7][8], Chile [10], Istanbul, Turkey [14], and Armidale NSW, Australia [14]. Methods in above literatures are in fact similar, the main differences lie in the numbers of daily indices (weather indices) to be included and their assigned weightings [3,13].…”