# Power Prediction in Photovoltaic Systems with Neural Networks: A Multi-Parameter Approach
## 1 Introduction
Accurate power forecasting contributes to improving the performance of PV systems. Efficiently determining the number of panels and, consequently, other equipment, reduces costs and helps manage energy production, transmission, and storage efficiency by ensuring that the forecasted energy production values are close to the actual values. For these reasons, many studies have already been conducted on power forecasting in PV systems and improving the accuracy of these predictions [4; 5; 6; 7; 8; 9; 10; 11]. In these studies, the accuracy of energy forecasts has been improved using artificial intelligence (AI) techniques. In one of the studies in the literature [5], a deep Convolutional Neural Network (CNN) architecture was combined with Empirical Mode Decomposition for PV power generation forecasting, and power forecasting was performed for a 1000 kW PV power plant in Turkiye [6] employing an autoregressive neural network model optimized with the Levenberg-Marquardt algorithm to forecast PV power generation for the upcoming weekend. Additionally, ref. [7] used a single axis tracking PV module and Artificial Neural Network (ANN) architecture to predict energy production on sunny and cloudy days. The effectiveness of this model was validated through experimental studies. In [8], a hybrid model consisting of CNN and Long Short-Term Memory was proposed for PV energy production forecasting at a solar power plant in Busan, South Korea. Another study employed a new CNN model, SolarNet, for short-term PV power forecasting [9]. Apart from these studies, in [10], various machine learning algorithms (including Support Vector Machines (SVM), ANN, and Linear Regression (LR)) were used for day-ahead prediction PV power forecasting, and in [11], various ensemble learning models (such as AdaBoost Trees, Bagged Trees, Random Forests, and Generalized Random Forests) were used for short-term solar radiation forecasting. Despite the effectiveness of existing forecasting models, there is a significant gap in the literature in the lack of comprehensive studies that integrate multiple meteorological parameters simultaneously. Most existing studies primarily focus on a limited set of meteorological variables, often neglecting the combined influence of multiple parameters. This simplification reduces the accuracy and generalizability of the models, particularly under diverse and dynamic weather conditions. This study addresses this gap by proposing a multi-parameter ANN-based model that combines temperature, radiation, wind speed, cloud cover, and humidity to improve forecast accuracy. In this study, power forecasting is performed using ANN methodology with power and meteorological data collected from a solar power plant in Elazg, Turkiye. The ANN technique was chosen due to its ability to model complex nonlinear relationships for PV power forecasting. The accuracy and complexity of power forecasting methods vary depending on the technique used. Traditional statistical models, such as LR and SVM, offer simplicity and interpretability, but may struggle to capture the nonlinear relationships between meteorological variables and PV power output. More advanced machine learning models, such as ANNs and CNNs, have demonstrated higher predictive accuracy due to their ability to model complex nonlinear dependencies. However, these models require more computational resources and careful hyper-parameter tuning to achieve high performance. In this study, an ANN was selected as it provides a balance between predictive accuracy and computational efficiency, making it suitable for PV power forecasting where both precision and processing speed are crucial. Since meteorological factors affect the power output of PV systems in solar power plants, the amount of energy produced varies depending on weather conditions, which complicates the energy system. Therefore, accurate power forecasting is crucial for energy planning, and energy planning must be carried out in a way that ensures economic solutions while maintaining grid stability.