Probabilistic Forecasting of PV Power Using Artificial Neural Networks with Confidence Intervals

This paper outlines an innovative approach to enhance the predictability of solar photovoltaic power. By employing advanced machine learning techniques, specifically artificial neural networks, this study addresses the challenges posed by the intermittent nature of solar energy. The employed models incorporate probabilistic forecasting to provide not only precise power output predictions but also confidence intervals that signify the uncertainty in these predictions. This approach supports more effective integration of solar energy into power grids, facilitating better energy management and planning. The results indicate that our models can significantly improve the accuracy of solar power forecasting, crucial for optimizing grid operations and enhancing renewable energy adoption.