# Power Prediction in Photovoltaic Systems with Neural Networks: A Multi-Parameter Approach
## 4 Power Prediction in Photovoltaic Systems with Neural Networks
Although the success of the ANN model is evident from the overlap of the graphs, this is also quantitatively expressed in Figure 7 using the R and MSE metrics. The R values for  
Figure 5: The ANN model used in the study. Figure 6: The comparison of the estimated and the actual power for 2024.  
training, test, validation, and all data were obtained as 0.98, 0.99, 0.97, and 0.98, respectively. The MSE value for all data was obtained as 0.03. It is well known that an R value close to 1 signifies a strong correlation between predicted and actual values, while an MSE value approaching 0 indicates minimal prediction error. These metrics are widely used to assess the accuracy and reliability of predictive models. In this study, the ANN model yielded an R value of 0.98 and an MSE value of 0.03, indicating a strong agreement between the predicted and actual power values. These results suggest that the model provides a reliable performance in power prediction. The results presented in Figure 6 indicate that the proposed ANN-based model can successfully capture the nonlinear relationships between meteorological variables and PV power output. The strong correlation between predicted and actual values, as seen in Figure 7, is reflected in the high R values and low MSE, and demonstrates the model's reliability and robustness. Table 2 shows the presented meteorological data and the monthly averages of actual and estimated power values. As expected, the lowest irradiation values occur in winter months, while the highest irradiation values are recorded in summer months. Among the winter months with the lowest power generation, a comparison of December and January reveals that, although the irradiation level in December is lower, higher power generation was achieved due to lower temperature and cloudiness levels. Figure 7: The R values for training, test, validation, and all data. To interpret the impact of the meteorological data on power generation, the data utilized in developing the ANN model were examined in detail, and certain data values were shared in Table 3. When selecting the data presented in Table 3, data points where each parameter used in the study were most prominent were chosen. When Table 3 is examined, the impact of five parameters on PV power generation can be observed more clearly. For instance, to analyze the effect of irradiation, data from two different days were selected where the other parameters (temperature, wind speed, average humidity, and cloudiness) had the same or very similar values. The same approach was applied to all parameters. Looking at the table, while the other parameters are nearly identical, the irradiation values for the two days differ by almost 1.5 times. Although the positive effect of irradiation on PV power generation is already well known, it is clearly evident in the table as well. Similarly, for the datasets selected to observe the effect of temperature, the well-documented \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **Radiation** & **Temperature** & **Wind** & **Average** & **Cloudiness** & **Actual** & **Estimated** \\  & **(W/m2)** & **(\({}^{\circ}\)C)** & **Speed (m/s)** & **(\%)** & **(Oktas)** & **Power (kW)** \\ \hline January & 1.65 & 3.83 & 1.99 & 81.54 & 5.83 & 2359.29 & 2324 \\ February & 3.21 & 5.84 & 2.22 & 73.86 & 4.38 & 4187.19 & 4372 \\ March & 3.78 & 7.62 & 2.45 & 71.00 & 4.81 & 4536.33 & 4733 \\ April & 6.32 & 16.77 & 2.33 & 57.42 & 3.51 & 6551.68 & 6574 \\ May & 6.17 & 17.28 & 2.62 & 62.35 & 4.58 & 5833.97 & 6134 \\ June & 8.52 & 26.74 & 2.80 & 31.82 & 1.19 & 7503.52 & 7263 \\ July & 8.00 & 28.23 & 2.83 & 34.23 & 2.15 & 7134.05 & 6916 \\ August & 7.23 & 28.76 & 2.59 & 29.20 & 0.76 & 6860.42 & 6845 \\ September & 5.53 & 23.85 & 2.32 & 40.48 & 2.13 & 6012.58 & 5849 \\ October & 4.35 & 15.50 & 2.67 & 43.04 & 1.88 & 5531.54 & 5733 \\ November & 2.64 & 7.85 & 2.11 & 66.15 & 3.43 & 3598.08 & 3809 \\ December & 1.58 & 3.22 & 2.05 & 83.47 & 5.21 & 2404.52 & 2343 \\ \hline \end{tabular}
\end{table}