# Power Prediction in Photovoltaic Systems with Neural Networks: A Multi-Parameter Approach
## 5 Conclusions  
Prioritizing environmentally friendly renewable energy sources not only helps reduce dependence on external energy sources but also contributes to sustainability. However, the intermittent nature of renewable energy due to meteorological conditions poses challenges for ensuring continuous energy production. For this reason, accurately estimating the amount of energy that can be generated from renewable sources is essential. Since the fuel used for electricity generation in PV systems is solar irradiation, energy can only be produced when sunlight is available. Thus, when daylight is absent or insufficient, the system either fails to generate energy or operates at low efficiency. The significance of solar irradiation implies that factors such as shading, cloud cover, dust, and humidity, which influence the amount of irradiation received by the panel, are also crucial. Additionally, temperature, which negatively affects the system, and wind, which influences both temperature and the removal of dust from PV panels, must be considered in power forecasting models. Therefore, when estimating the power output of a PV system, variations in all these parameters should be taken into account. For these reasons, forecasting the energy production of PV systems is crucial for energy planning and management. Accurate power predictions facilitate the proper alignment of supply and demand through effective energy policies, ultimately leading to more reliable and cost-effective energy access. In this study, an ANN-based model was selected for PV power forecasting due to its ability to model complex nonlinear relationships. Power data obtained from a PV power plant for the year 2024, along with meteorological data, were used to train the ANN model. The results of the power predictions made using the ANN model were presented in both tabular and graphical formats. The graphs illustrate that the ANN-based predicted power closely follows the actual power throughout the year. Additionally, data from various days were selected and analyzed in table form to examine the effects of irradiation, temperature, wind speed, humidity, and cloud cover on PV power generation. Moreover, meteorological data and both actual and ANN-predicted power values for July and December were also presented in graphical form. According to the obtained results, irradiation and wind speed have a positive impact on PV power generation, whereas temperature exhibits a negative effect. Since cloud cover reduces irradiation, it also has a detrimental impact on PV power generation. However, the findings indicate that humidity does not have a significant effect on PV system performance. Finally, the performance of the proposed ANN model was evaluated using R and MSE metrics. The obtained results suggest that the R and MSE values were 0.98 and 0.03, respectively. The R value being close to 1 and the MSE value being close to 0 indicates that the model provides a high level of predictive accuracy. Despite the promising results of the proposed ANN model, this study has certain limitations. First, the model was trained and tested using data from a single PV power plant located in Elazg, Turkiye. The generalizability of the results to different geographical locations with varying climate conditions should be further examined using data collected from those regions. Second, although multiple meteorological parameters were considered, additional factors such as atmospheric pressure, precipitation, and panel soiling effects were not incorporated, which might contribute to further improving forecasting accuracy. Future research should focus on expanding the dataset by including data from multiple locations and longer time periods to improve model robustness. Additionally, incorporating real-time weather forecast data into the model could enhance its practical applicability in energy management systems. **Author Contributions:** Conceptualization, Z.B.D. ; Methodology, Z.B.D. ; Validation, H.G. ; Formal analysis, Z.B.D. ; Investigation, Z.B.D. ; Resources, H.G.