Precise Forecasting of Sky Images Using Spatial Warping

TL;DR

This paper introduces a deep learning framework that uses spatial warping to improve the accuracy of long-term sky image forecasting, addressing horizon distortion issues in cloud movement prediction for solar irradiance estimation.

Contribution

The work presents a novel optimal warping technique combined with deep learning to enhance sky image resolution and improve cloud evolution prediction over longer time horizons.

Findings

Better long-term sky image prediction accuracy

Improved cloud motion modeling near the horizon

Enhanced solar irradiance forecasting capabilities

Abstract

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.