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Solar energy is now the cheapest form of electricity in history. Unfortunately, significantly increasing the electric grid's fraction of solar energy remains challenging due to its variability, which makes balancing electricity's supply and demand more difficult. While thermal generators' ramp rate---the maximum rate at which they can change their energy generation---is finite, solar energy's ramp rate is essentially infinite. Thus, accurate near-term solar forecasting, or nowcasting, is important to provide advance warnings to adjust thermal generator output in response to variations in solar generation to ensure a balanced supply and demand. To address the problem, this paper develops a general model for solar nowcasting from abundant and readily available multispectral satellite data using self-supervised learning. Specifically, we develop deep auto-regressive models using convolutional neural networks (CNN) and long short-term memory networks (LSTM) that are globally trained across multiple locations to predict raw future observations of the spatio-temporal spectral data collected by the recently launched GOES-R series of satellites. Our model estimates a location's near-term future solar irradiance based on satellite observations, which we feed to a regression model trained on smaller site-specific solar data to provide near-term solar photovoltaic (PV) forecasts that account for site-specific characteristics. We evaluate our approach for different coverage areas and forecast horizons across 25 solar sites and show that it yields errors close to that of a model using ground-truth observations. 

Developing accurate solar performance models, which infer solar power output in real time based on the current environmental conditions, are an important prerequisite for many advanced energy analytics. Recent work has developed sophisticated data-driven techniques that generate customized models for complex rooftop solar sites by combining well-known physical models with both system and public weather station data. However, inferring solar generation from public weather station data has two drawbacks: not all solar sites are near a public weather station, and public weather data generally quantifies cloud cover-the most significant weather metric that affects solar-using highly coarse and imprecise measurements.In this paper, we develop and evaluate solar performance models that use satellite-based estimates of downward shortwave (solar) radiation (DSR) at the Earth's surface, which NOAA began publicly releasing after the launch of the GOES-R geostationary satellites in 2017. Unlike public weather data, DSR estimates are available for every 0.5km 2 area. As we show, the accuracy of solar performance modeling using satellite data and public weather station data depends on the cloud conditions, with DSR-based modeling being more accurate under clear skies and station-based modeling being more accurate under overcast skies. Surprisingly, our results show that, overall, pure satellite-based modeling yields similar accuracy as pure station-based modeling, although the relationship is a function of conditions and the local climate. We also show that a hybrid approach that combines the best of both approaches can also modestly improve accuracy. 

Distributed solar generation is rising rapidly due to a continuing decline in the cost of solar modules. Nearly all of this solar generation feeds into the grid, since battery based energy storage is expensive to install and maintain. Unfortunately, accommodating unlimited intermittent solar power is challenging, since the grid must continuously balance supply and demand. Thus, governments and public utility commissions are increasingly limiting grid connections of new solar installations. These limitations are likely to become more restrictive over time in many areas as solar disrupts the utility business model. Thus, to employ solar without restrictions, users may increasingly need to defect from the grid. Unfortunately, batteries alone are unlikely to become cost-efficient at enabling grid defection for the foreseeable future. To address the problem, we explore using a mixture of solar, batteries, and a whole-home natural gas generator to shift users partially or entirely off the electric grid. We assess the feasibility and compare the cost and carbon emissions of such an approach with using grid power, as well as existing “net metered” solar installations. Our results show that the approach is trending towards cost-competitive based on current prices, reduces carbon emissions relative to using grid power, and enables users to install solar without restriction.