Precise Forecasting of Sky Images Using Spatial Warping
This work addresses solar power intermittency for grid-connected photovoltaic systems, representing an incremental improvement in forecasting accuracy.
The paper tackles the problem of forecasting solar irradiance by predicting future sky images to address cloud occlusion, introducing a deep learning method that improves resolution and horizon cloud motion prediction for longer time horizons.
The intermittency of solar power, due to occlusion from cloud cover, is one of the key factors inhibiting its widespread use in both commercial and residential settings. Hence, real-time forecasting of solar irradiance for grid-connected photovoltaic systems is necessary to schedule and allocate resources across the grid. Ground-based imagers that capture wide field-of-view images of the sky are commonly used to monitor cloud movement around a particular site in an effort to forecast solar irradiance. However, these wide FOV imagers capture a distorted image of sky image, where regions near the horizon are heavily compressed. This hinders the ability to precisely predict cloud motion near the horizon which especially affects prediction over longer time horizons. In this work, we combat the aforementioned constraint by introducing a deep learning method to predict a future sky image frame with higher resolution than previous methods. Our main contribution is to derive an optimal warping method to counter the adverse affects of clouds at the horizon, and learn a framework for future sky image prediction which better determines cloud evolution for longer time horizons.