Power Prediction in Photovoltaic Systems with Neural Networks: A Multi-Parameter Approach

Abstract

In this study, a neural network-based power prediction for a photovoltaic system was conducted using a multi-parameter approach, considering radiation, temperature, wind speed, humidity, and cloud cover. Photovoltaic systems are highly popular renewable energy sources due to their robust, modular, and environmentally friendly characteristics. Although photovoltaic systems offer many advantages, their dependency on irradiation for energy generation and their sensitivity to meteorological parameters pose a significant disadvantage, leading to intermittent energy production. Since these parameters affect the quality of power generated at the plant, they introduce uncertainty in power systems. Therefore, it is crucial to consider these factors in energy planning and management. In this study, to mitigate uncertainty in power systems and contribute to energy planning by predicting power production, power data obtained from a power plant, along with meteorological data, were used in Single Layer Perceptron Neural Network. The predicted power values obtained from the proposed model were compared with the actual values, and the results of this comparison were presented. Furthermore, to demonstrate the model's performance, the R and MSE values were provided as 0.98 and 0.03, respectively, indicating a strong correlation between predicted and actual values and a low prediction error.