Two-Stage Photovoltaic Forecasting: Separating Weather Prediction from Plant-Characteristics
This work addresses the need for more accurate photovoltaic generation forecasts for energy management applications, particularly by analyzing the source of prediction error.
This paper tackles the problem of photovoltaic generation forecasting by separating weather prediction from plant characteristics. It found that using weather forecasts instead of satellite-based ground-truth weather observations increased mean absolute error by 11% and 68% for two selected photovoltaic systems.
Several energy management applications rely on accurate photovoltaic generation forecasts. Common metrics like mean absolute error or root-mean-square error, omit error-distribution details needed for stochastic optimization. In addition, several approaches use weather forecasts as inputs without analyzing the source of the prediction error. To overcome this gap, we decompose forecasting into a weather forecast model for environmental parameters such as solar irradiance and temperature and a plant characteristic model that captures site-specific parameters like panel orientation, temperature influence, or regular shading. Satellite-based weather observation serves as an intermediate layer. We analyze the error distribution of the high-resolution rapid-refresh numerical weather prediction model that covers the United States as a black-box model for weather forecasting and train an ensemble of neural networks on historical power output data for the plant characteristic model. Results show mean absolute error increases by 11% and 68% for two selected photovoltaic systems when using weather forecasts instead of satellite-based ground-truth weather observations as a perfect forecast. The generalized hyperbolic and Student's t distributions adequately fit the forecast errors across lead times.