# Power Prediction in Photovoltaic Systems with Neural Networks: A Multi-Parameter Approach
## 4 Power Prediction in Photovoltaic Systems with Neural Networks  
Since many parameters have an impact on the energy production performance of PV systems, all these factors must be taken into account for an accurate power estimate. In this study, the ANN is used to make a multi-parameter power estimation in PV systems. For this purpose, the actual power values obtained from a PV power plant located in Elazg, Turkiye, together with the meteorological data of radiation, temperature, wind speed, humidity and cloudiness obtained between 1 January 2024 and 31 December 2024, are used. The PV power plant is installed on a 1000 m\({}^{2}\) area at the Latitude 38.59 Longitude 39.33 coordinates, as seen in Figure 3.  
The system, with a nominal power of 998 kW, uses 3564 PV panels (16.5 strings consisting of Mono-crystalline modules, 18 in parallel and 12 in series). Technical information on PV modules is presented in Table 1. \begin{table}
\begin{tabular}{l l} \hline \hline  & **Features** & **Values** \\ \hline Size & 1652 mm \(\times\) 997 mm \(\times\) 42 mm \\ Max Power & 320 W \\ Rated Voltage \& Current & 24 V \& 9.63 A \\ Short Circuit Current & 10.31 A \\ Open Circuit Voltage & 39.20 V \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Features of PV Module. Figure 3: The PV power plant from which data is obtained. As stated in previous sections, since electricity generation in PV systems depends on solar radiation, the most crucial parameter in power prediction for a PV system is the radiation level. Figure 4a,b illustrates the daily variations of average radiation and actual power values for the month of January, respectively. Analyzing Figure 4a,b reveals that fluctuations in the power output of the PV system are largely influenced by the increase or decrease in solar radiation levels. However, the variations in the two graphs are not identical. This discrepancy arises because, in addition to radiation levels, factors such as temperature, wind, humidity, and cloud cover also have direct or indirect, as well as positive or negative, effects on power generation. This observation highlights the importance of utilizing multiple parameters in power prediction studies for PV systems. As seen in Figure 5, the ANN architecture of the model used in this study consists of three layers: an input layer with five neurons (corresponding to radiation, temperature, wind speed, humidity, and cloudiness), a hidden layer with 20 neurons, and an output layer with a single neuron representing the estimated power. The number of hidden layers and neurons was determined empirically using a trial-and-error approach. Different configurations were tested, and the structure with one hidden layer and 20 neurons provided the  
Figure 4: For January, the changes of daily **(a)** average radiation, **(b)** power values. better trade-off between model accuracy and computational efficiency. The weights and biases of the network were initialized randomly and adjusted during training using the Levenberg-Marquardt algorithm to minimize the error function. In this study, a total of 35,136 data points (15-min interval data for each of 366 days) were used. The dataset was randomly split into 90% for training, 5% for validation, and 5% for testing to ensure a well-distributed representation of the data. The input variables were used in their original form without normalization. However, the power output values were normalized during the training phase to enhance model stability. As illustrated in Figure 5, the ANN model estimated that 8023 kW power will be produced in the power plant with the input data 8.5 W/m\({}^{2}\) radiation, 14 \({}^{\circ}\)C temperature, 2.4 m/s wind speed, 63% average humidity, and 1.8 Oktas cloudiness. The comparison of the daily power value estimated by the ANN model and the actual power value for 2024 is presented in Figure 6.  
When examining the figure, it is observed that the prediction results obtained using the ANN model are quite close to the actual power values recorded from the PV power plant.