Prediction of Solar PV power output using neural networks

Accurately predicting solar photovoltaic (PV) power output is essential for optimizing energy management in smart grids and enhancing the integration of renewable energy sources. This study presents a neural network-based approach for predicting the power output of solar PV systems. Utilizing historical weather data and solar PV power output, we developed a predictive model capable of learning complex patterns and correlations between environmental factors and solar power generation. A multilayer feedforward neural network with backpropagation is trained on a dataset collected from a solar PV system, considering the non-linear characteristics of solar energy production. The model is evaluated using Mean Squared Error (MSE) and coefficient of determination (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> score). The results demonstrate that the neural network approach can predict solar PV power output with high accuracy of above 89%. This neural network-based forecasting model is a step forward in making the integration of renewable energy more reliable and efficient in smart grid applications. It can enhance energy scheduling, storage optimization, and grid stability, contributing to the efficiency operation of smart grids.