KARPAGA VINAYAGA COLLEGE OF ENGINEERING & TECHNOLOGY
DEPARTMENT OF BIOMEDICAL ENGINEERING



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Predicted weekly a.m. and p.m. temperature and precipitation data for the entire Poland region between the years 1990 and 2012 were obtained from the SWPC Climate Data Acquisition and Distribution Center ([www.swpc.noaa.gov]( The prediction data are collected over land, sea, and space during the meteorological forecast time period. Predicted hourly temperature and precipitation data for the 30-year period (1987--2016) are converted to daily data for the PVSYST simulation ([@bib50]). The validation process of the climate data used for the PV performance modeling was done using the following validated PV system monitoring and control data sets. i)Solar Installation Planning Tool (SIAT) data; (ii)Solar Monitor Simulator (SMS) data; (iii)Solar Terrestrial Environment Team (STEP) data. The validation results of the solar data for the prediction period of 1987--2016 are presented in [Table 5](#tbl5){ref-type="table"}. The stepwise elimination process ([Fig. 1](#fig1){ref-type="fig"}) was applied to the measured PV installation planning data for the validation period. This was used to determine the solar thermal performance monitoring dataset to be used in the model. The objective of the validation was to ensure the data represent the photovoltaic system performance in Poland. The solar thermal performance metrics were chosen to reflect physical and electrical parameters for a representative PV system. Therefore, PV system-related performance metrics such as the DC current, DC voltage, AC voltage and AC current, DC resistance, AC resistance, and DC resistance values in the 24 h period were used.Table 5Validation results of solar data for the prediction period of 1987--2016.Table 5ParameterRangeMeanStandard deviationSLP (%)RMSE (%)R^2^ (%)PV Installation Planning Tool (SIAT)Measured0.049--0.0180.0040.0020.02212.42798.9Predicted0.049--0.0260.0040.0020.02118.82498.7Solar Monitor Simulator (SMS)Measured0.017--0.0530.0110.0050.01095.61596.9Predicted0.017--0.0520.0110.0050.01114.23
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