Self-Supervised Learning on Multispectral Satellite Data for Near-Term Solar Forecasting (Papers Track)

Akansha Singh Bansal (University of Massachusetts Amherst); Trapit Bansal (University of Massachusetts Amherst); David Irwin (University of Massachusetts Amherst)

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With the unprecedented increase in distributed photovoltaic (PV) capacity across the globe, there is an increasing need for reliable and accurate forecasting of solar power generation. While PV output is affected by many factors, the atmosphere, i.e., cloud cover, plays a dominant role in determining the amount of downwelling solar irradiance that reaches PV modules. This paper demonstrates that self-supervised learning of multispectral satellite data from the recently launched GOES-R series of satellites can improve near-term (15 minutes) solar forecasting. We develop deep auto-regressive models using convolutional neural networks (CNN) and long short-term memory networks (LSTM) that are globally trained across many solar sites on the raw spatio-temporal data from GOES-R satellites. This self-supervised model provides estimates of future solar irradiance that can be fed directly to a regression model trained on smaller site-specific solar data to provide near-term solar PV forecasts at the site. The regression implicitly models site-specific characteristics, such as capacity, panel tilt, orientation, etc, while the self-supervised CNN-LSTM implicitly captures global atmospheric patterns affecting a site's solar irradiation. Results across 25 solar sites show the utility of such self-supervised modeling by providing accurate near-term forecast with errors close to that of a model using current ground-truth observations.

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