 |
Typical Meteorological Year |
A problem of units ? of terminology ? Consult the Education service. |
- Definition
- The HelioClim3 TMY method (Sendia) Kalogirou 2003: step by step
- Improvements of TMY: the EU-funded project ENDORSE
Definition
A TMY of a climatological parameter dataset is a year which should be representative of the dataset, in order to give the most likely/plausible/typical/normal scenario for this parameter or group of parameters.
Scientific debate:
The time step (monthly, daily, hourly...) and the plane orientation are the choices of the User. Most common methods used statistical approaches. As a consequence, there is as much different TMY as statistical approaches. As no obvious reference exists to assess the quality of a TMY, it is hard for developers to know the quality of their new implementations. As well, it is also hard for Users to select one TMY among all the TMY available in the litterature.
(Sendia) Kalogirou 2003: step by step
Summary of the method: Let us admit that you need a time series of radiation data as an input to PVSYST. As a consequence, you need one year of hourly Global and Diffuse irradiation values over the horizontal plane (GHI_h and DHI_h). The main assumption of the Sendia (Kalogirou 2003) method )is to select a month (eg. January) of hourly values among all the months (of January) available in the time series of data (in the HC3v3 database, 7 months of January are available) according to a given criterion. The criterion is the minimum of the difference between the Cumulative Distribution Function (CDF) of the hourly values of the month of a given year (eg. January 2005) and the CDF of the hourly values of all the months simulateously (all the months of January between 2005 and 2011).
NB: Please note that the same procedure can be applied on the Direct Normal Incidence component (BNI_h) in order to create a TMY based on the BNI component for Concentrated Solar Power applications.
Let's enter the code, step by step:
| Case study | TMY compatible with PVSYST |  |
|---|---|---|
| Inputs |
|
|
| Outputs | A csv file with a header which describes the file and column content, and the computed/selected TMY hourly values. Column content: 1_ month 2_ day of the month 3_ hour of the day 4_ hourly ghi values 5_ hourly dhi values |
 |
| Method | Step1: First we perform a HelioClim3-version3 time series request of hourly values over the inclined plane. We remind that the service over the horizontal plane only retrieve the GHI component. Only the services over the inclined and normal plane return all the components of the irradiation. Step2: Let take the example of the month of January. We select all the hourly GHI values for all the months of January in the time series retrieved in Step1. Then build:
Step3: the month of January which is selected is the one which minimizes the FS (Filkenstein-Schafer) distance: Step4: fill the output csv file with the hourly GHI and DHI values of the selected month of January. Loop to Step 2 to process the month of February, and so on. |
Step1: request http://www.helioclim.org/pub/hc3v3_request.php? ...Step2: CDF_January_k (k=1:n), and CDF_January_All NB: please note that on this illustration, only 20 bins are used to build the CDF. We recommend in practice a larger number of bins (greater than 100). click on the image to magnify Step3: Select the distribution which is the closest from the brown one (the most on the right for each bin). For this example, the month of Jan. 2010 is selected for Toulouse. Step4: Fill the output file with the hourly GHI and DHI values of the month of Jan. 2010, then do the same for February, and so one. |
Recommendations and remarks:
- Case of the month of February (28 or 29 days available): in order to avoid the effect of the different number (nh) of hours available in the month on the CDF, a solution is to normalize the CDF by this number nh.
- Our main constraint is to return a fully complete hourly TMY to our Customers. If the selected month have missing data, the second closest CDF is selected.
- We recommend more than 100 bins to build the CDF.
- It will soon possible to complete our TMY with the temperature, using the MERRA database.
- If you need a TMY on the Beam Normal Irradiation (BNI) component, the same procedure should be applied on this component. The wrong procedure would be to create a BNI TMY based on the selection of the month on the GHI hourly values.
P10, P50, P90, radiation certificates for banks: As the HelioClim3 database starts in Feb. 2004 which represents approx. 8 years of data, it is not possible to return exactly a TMY P10 (Typical Meteorological Year Percentile 10), P50 or P90. The created TMY are pessimistic, more plausible, or optimistic. If you are looking for radiation certificates for banks, all this information must be taken into account. We insist that it doesn't exist to day any mean to assess the quality of a given TMY. As a consequence, be really careful of the source of the TMY data you are using.
Important references
S. Wilcox and W. Marion 2008. "Users Manual for TMY3 Data Sets Users Manual for TMY3 Data Sets," Renewable Energy, no. May, p. 51,.
Kalorigou. A. S., 2003. "Generation of typical meteorological year (TMY-2) for Nicosia, Cyprus", Renewable Energy, Vol. 28, pp: 2317-2334.
Hall I. J., R. R. Prairie, H. E. Anderson, and E. C. Boes, 1978. "Generation of typical meteorological years for 26 SOLMET stations", Sandia Laboratories Report SAND 78-1601, Albuquerque, NM.
Improvements of TMY: the EU-funded project ENDORSE
In the framework of the European Community ENDORSE project, we are currently working to enhance the quality of our TMY methods, as well as on the development of tools and procedures to assess the quality of a TMY.
TMY and System Advisor Model (SAM) of NREL => coming soon
Last update: 2/23/12 5:20 PM