# Power Prediction in Photovoltaic Systems with Neural Networks: A Multi-Parameter Approach
## 1 Introduction
Deep learning techniques, such as CNN and LSTM, have shown superior performance in handling sequential and spatial data but may require large datasets and extended training times. In contrast, an ANN provides a flexible and efficient approach to power forecasting while maintaining a manageable computational cost. For this reason, the Levenberg-Marquardt algorithm was chosen for training due to its effectiveness in refining the ANN weights and achieving rapid convergence. A review of the existing literature reveals that most studies primarily consider radiation, temperature, and wind speed as the key parameters, often overlooking the combined effects of other crucial meteorological factors, such as humidity and cloud cover. As a result, comprehensive multi-parameter approaches remain scarce, limiting the reliability of power forecasting models. When evaluating the forecast, the more environmental factors that are considered together, the more accurate the prediction will be. For this purpose, this study proposes an ANN-based power forecasting model that takes multiple environmental factors, such as temperature, radiation, wind speed, cloud cover, and humidity, into account, aiming to enhance forecasting performance. Using the meteorological data along with the actual power values generated in the solar power plant, an ANN model was trained to develop a reliable PV power forecasting model. The main contributions of this study can be summarized as follows:* Unlike most existing studies that focus on a limited set of meteorological variables (such as radiation, temperature, and wind speed), this study integrates five meteorological parameters--temperature, radiation, wind speed, cloud cover, and humidity--into an ANN-based forecasting model to enhance accuracy. This comprehensive approach improves the model's robustness under varying weather conditions. * A detailed analysis is provided regarding the impact of each meteorological parameter on PV power generation. In particular, this study highlights the underexplored effects of humidity and cloud cover, contributing new insights into their influence on power output. * The model is tested using real meteorological and power generation data from a solar power plant in Elazig, Turkiye, demonstrating its practical applicability. * The effectiveness of the proposed model is rigorously validated through multiple performance metrics, including Coefficient of Regression (R) and Mean Squared Error (MSE), achieving high predictive accuracy, with R = 0.98 and MSE = 0.03. * A comparative evaluation using cross-validation is conducted to benchmark the ANN model against alternative AI techniques such as GPR, SVM, LR, DT, and Ensemble learning. The results confirm that the ANN model outperforms these methods, reinforcing its suitability for PV power forecasting. These contributions enhance the existing body of research by addressing the key limitations in previous studies and presenting a more holistic forecasting methodology. The findings underscore the importance of incorporating multiple meteorological factors to achieve more reliable and precise energy predictions, which is crucial for effective energy planning and management. The paper is organized as follows: In Section 2, PV systems and electricity generation, and factors affecting PV system performance are discussed, highlighting the importance of meteorological factors in power forecasting. In Section 3, information is provided about the proposed ANN model. Finally, in Section 4, the ANN-based power forecasting model developed in this study is presented, and the system's success is supported by experimental results.