Abstract
In this study, typical meteorological years (TMYs) of eight cities in three provinces of Northeast China are generated by the recorded weather data (dry-bulb temperature, relative humidity, wind velocity and global solar radiation) in the period of 1994-2009, using Finkelstein-Schafer (FS) statistical method. The cumulative distribution function (CDF) for each weather index and for each year is compared with the CDF for the long-term years. The TMY database are essential in the applications of solar energy systems. Moreover, such database can also be applied in many engineering applications such as meteorology and building simulations. Ill. 4, bibl. 11, tabl. 6 (in English; abstracts in English and Lithuanian).DOI: http://dx.doi.org/10.5755/j01.eee.123.7.2386
Highlights
To relieve the dual pressure from rising energy demand and growing environmental problems, renewable energy sources like solar energy are more favored
Based on the above Typical meteorological year (TMY) method and the data of the eight stations listed in Table 1, the TMYs of the eight stations in three provinces of Northeast China are formed and analyzed in the following
With mean dry-bulb temperature and daily global solar radiation as example, the comparison between the short term cumulative distribution function (CDF) and the long term CDFs for Shenyang station is given in Fig. 1 and 2
Summary
To relieve the dual pressure from rising energy demand and growing environmental problems, renewable energy sources like solar energy are more favored. In this respect, solar radiation data, typical solar radiation data, are the most basic and important parameters in many solar energy applications. Several approaches for generating TMYs have been proposed. These methods are similar–the main differences lie in the number of daily indices (weather parameters) to be included and their assigned weightings [1]. Marion and Urban [5], Wilcox and Marion [6], Petrakis et al [7] made attempts to generate TMYs for different locations with respective weather parameters and assigned weighting factors
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