SunCast: Solar Irradiance Nowcasting from Geosynchronous Satellite Data

• URL: http://arxiv.org/abs/2201.06173v1
• Date: Mon, 17 Jan 2022 01:55:26 GMT
• Title: SunCast: Solar Irradiance Nowcasting from Geosynchronous Satellite Data
• Authors: Dhileeban Kumaresan, Richard Wang, Ernesto Martinez, Richard Cziva, Alberto Todeschini, Colorado J Reed, Hossein Vahabi
• Abstract summary: We propose a Convolutional Long Short-Term Memory Network model that treats solar nowcasting as a next frame prediction problem. Our models can predict solar irradiance for entire North America for up to 3 hours in under 60 seconds on a single machine without a GPU.
• Score: 2.285928372124628
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: When cloud layers cover photovoltaic (PV) panels, the amount of power the panels produce fluctuates rapidly. Therefore, to maintain enough energy on a power grid to match demand, utilities companies rely on reserve power sources that typically come from fossil fuels and therefore pollute the environment. Accurate short-term PV power prediction enables operators to maximize the amount of power obtained from PV panels and safely reduce the reserve energy needed from fossil fuel sources. While several studies have developed machine learning models to predict solar irradiance at specific PV generation facilities, little work has been done to model short-term solar irradiance on a global scale. Furthermore, models that have been developed are proprietary and have architectures that are not publicly available or rely on computationally demanding Numerical Weather Prediction (NWP) models. Here, we propose a Convolutional Long Short-Term Memory Network model that treats solar nowcasting as a next frame prediction problem, is more efficient than NWP models and has a straightforward, reproducible architecture. Our models can predict solar irradiance for entire North America for up to 3 hours in under 60 seconds on a single machine without a GPU and has a RMSE of 120 W/m2 when evaluated on 2 months of data.

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