WeatherNet: Nowcasting Net Radiation at the Edge

Abstract

In addition to natural processes such as photosynthesis and evapotranspiration, net radiation affects industrial applications such as photovoltaic energy management and solar thermal collection. We propose a deep learning approach for nowcasting net radiation within subhourly and intrahour horizons to better understand and control processes influenced by net radiation. Specifically, we developed a deep-learning-based CNN-LSTM, named WeatherNet, that combines multiple local ground-based cameras and weather sensor data to predict net radiation. Unlike previous methodologies, our approach involves images from three different cameras: a sky-facing RGB camera, a horizon-facing RGB camera, and a horizon-facing forward-looking infrared camera. Further, WeatherNet was designed to run "at the edge" using the Waggle edge computing framework to reduce the data bandwidth, improve the latency of predictions, and eliminate centralized data collection. With our proposed dataset and model, WeatherNet, we present a novel methodology using relatively inexpensive equipment for nowcasting net radiation precisely between a 15- and 90-minute horizon.