## 3 Results  
First, it is important to conduct a sensitivity analysis in order to correlate input parameters, irradiance, and back panel temperature with the output parameter (power output) and--consequently--energy production. An additional, important parameter that assists this sensitivity analysis procedure is the airmass [45], which defines the direct optical path length of the Sun's rays through the Earth's atmosphere, expressed as a ratio relative to the minimum path length vertically upwards at the solar zenith. This coefficient is useful for characterizing the modification of the solar spectrum after traveling through the atmosphere. It is also intensively investigated as a valuable predictor in meteorological models [46]. For our purposes, airmass is routinely calculated for the available system's performance data according to Appendix B. Figure 2 classifies the electricity produced by the PV park according to the respective, dimensionless airmass values, which was found convenient to divide among six classes (AM1-AM2, AM2-AM3, AM3-AM4, AM4-AM5, AM5-AM10, AM10, and beyond) [47]. According to the results of Figure 2, more than 90% of electricity production corresponds to conditions with Airmass values in the range of 1 to 4. Moreover, 99% of the production is achieved for airmass values <10. For this reason, data for higher values are rejected as discussed in Section 2. Data with values of irradiance smaller than 50 W/m\({}^{2}\) are also rejected. Figure 3 shows that only 7.5% of electrical energy production is achieved for irradiance values smaller than 200 W/m\({}^{2}\). As already mentioned in Section 2, records that correspond to irradiance values lower than 50 W/m\({}^{2}\) are rejected. These points represent in total, less than 1% of the annual electricity generation. Exclusion of the above-mentioned records with low information quality significantly improves the prediction accuracy of the neural networks since we avoid training the NN with noisy data with low information value. The percentages of electricity production in the different backsheet temperature classes defined in the pre-processing are presented in Figure 4. More than 99% of the total electricity production occurs in the four backsheet temperature classes between 15 and 55 \({}^{\circ}\)C.  
Figure 3: Percentage of energy production in the various irradiance classes during the 8-year period. Figure 2: Percentage of electricity production in the six airmass classes defined, during the 8-year period. Figure 5 shows the observed linear trend in the evolution of AC power output with irradiance. On the other hand, backsheet temperature has also a general linear trend with irradiance. However, according to the technical datasheet of the PV panels, there is a negative effect of backsheet temperature on the panel's efficiency. These two conflicting trends produce the correlation in Figure 6 between power output and temperature, characterized by a wide margin of possible backsheet temperatures associated with each AC output level. The situation is more clearly depicted in Figure 7, where the correlation between power output, temperature, and irradiance is apparent. The PV panels' manufacturers describe the correlation of these three parameters with characteristic curves and temperature coefficients. STCs include irradiance (1000 W/m\({}^{2}\)), temperature (25 \({}^{\circ}\)C), and spectrum corresponding to AM 1.5. The lack of availability of spectral measurements is adjusted by the introduction of the airmass factor. Air mass quantifies the length of trail of the Sun's beams through the Earth's atmosphere [48]. Figure 4: Percentage of energy production in the different backsheet temperature classes during the 8-year period. Figure 5: Correlation between irradiance and AC power output. Figure 8 shows the correlation between power output and airmass in different classes of irradiance. It is observed that power output has an exponential trend.