The application of SDF is presented for 29 stations in Turkey as given with their locations in Fig. 4.6 and geographic features in Table 4.3. The recorded monthly average solar radiation amounts are given in Table 6.5.
In order to apply and indicate the reliability of the proposed approaches, stations are considered one by one for cross-validation. Let us say that Ankara is chosen as the estimation site and January as the month of estimation. Figure 6.14 shows the CSV and thereof obtained SDF for January. Herein, on the horizontal axis the dimensionless distances are as the ratios of distances to the maximum distance between Izmir and Van, which is equal to 916.40 km.
Although according to Table 6.5 Ankara has an average January solar radiation record of 5.88 MJ/m, it will be assumed non-existent for the cross-validation.
The subsequent step is to apply the estimation process as explained in the previous section. For this purpose, it is necessary to consider the distances from Ankara to all other 28 stations. Table 6.6 includes these distances in the third column. For the sake of comparison, the fourth and fifth columns include ID and IDS values.
In the sixth column the dimensionless distances are given and they are necessary prerequisites for the SDF in Fig. 6.14b. Dimensionless distances are calculated by dividing each distance value in the third column by the maximum distance value. In the seventh column, the SDF weightings are included as found for January from Fig. 6.14b corresponding to the dimensionless distances.
In the application of SDF for global estimation the available measurement sites are considered in the weighting procedure according to Eq. 6.3 with all stations. The plots of these estimations and the actual measurements are presented in Fig. 6.15.
It is obvious that global estimation procedure appears to be successful on the average. This is tantamount to saying that consideration of all the measurement sites without any distinction causes smoothing in the solar radiation spatial estimation. Relative error percentages of more than 10% appeared excessively at almost all the sites.
In order to improve the situation, it is suggested to use adaptive estimation so that the spatial estimation error becomes minimum. For this purpose, during the
6.7 General Application 42
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