Exploiting Satellite Data for Solar Performance Modeling

Bansal, Akansha Singh; Irwin, David

doi:10.1109/smartgridcomm47815.2020.9302984

Cited by 2 publications

(3 citation statements)

References 10 publications

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“…While there has been recent work on analyzing images from ground-based sky cameras [30,36,43,44] for near-term solar forecasting, it requires installing additional infrastructure at each site. Another alternative is based on estimating cloud motion vectors [15,25,26] from satellite images, however ML approaches that more directly model solar irradiance tend to perform better [9,23]. Our approach diers from recent approaches in solar nowcasting by forecasting solar Using the predictions from the CNN-LSTM model leads to solar output forecasting with error close to that of using the current satellite observations.…”

Section: Related Workmentioning

confidence: 96%

“…Recent work [6,[9][10][11]13] has focused narrowly on solar performance modeling-inferring current solar output from current environmental conditions-but not forecasting. Solar forecasting is a much more challenging modeling problem, since it must infer, not only how spectral data correlates with a site's solar energy output, but also how the spectral data will change over time based on the movements of clouds.…”

Section: Prior Workmentioning

confidence: 99%

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A moment in the sun

Bansal

Irwin

2022

Proceedings of the Thirteenth ACM International Conference on Future Energy Systems

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Solar energy is now the cheapest form of electricity in history. Unfortunately, signicantly increasing the electric grid's fraction of solar energy remains challenging due to its variability, which makes balancing electricity's supply and demand more dicult. While thermal generators' ramp rate-the maximum rate at which they can change their energy generation-is nite, solar energy's ramp rate is essentially innite. 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.Specically, 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-specic solar data to provide near-term solar photovoltaic (PV) forecasts that account for site-specic characteristics. We evaluate our approach for dierent 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. CCS CONCEPTS• Computing methodologies ! Machine learning approaches.

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Section: Related Workmentioning

confidence: 96%

Section: Prior Workmentioning

confidence: 99%

A moment in the sun

Bansal

Irwin

2022

Proceedings of the Thirteenth ACM International Conference on Future Energy Systems

Self Cite

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“…However, installing sky cameras requires additional infrastructure making the approach less scalable. Instead, ML approaches that model solar irradiance tend to perform better (Lago et al, 2018; Bansal and Irwin, 2020) in comparison to the other approaches. Bansal et al (2022) propose an approach for solar nowcasting by forecasting solar irradiance values from multispectral visible bands from GOES satellite data using the approach shown in Figure 5.…”

Section: Sample Applications Illustrating the Importance Of Spatiotem...mentioning

confidence: 99%

Leveraging spatiotemporal information in meteorological image sequences: From feature engineering to neural networks

Bansal

Lee

Hilburn

et al. 2023

Environ. Data Science

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Atmospheric processes involve both space and time. Thus, humans looking at atmospheric imagery can often spot important signals in an animated loop of an image sequence not apparent in an individual (static) image. Utilizing such signals with automated algorithms requires the ability to identify complex spatiotemporal patterns in image sequences. That is a very challenging task due to the endless possibilities of patterns in both space and time. Here, we review different concepts and techniques that are useful to extract spatiotemporal signals from meteorological image sequences to expand the effectiveness of AI algorithms for classification and prediction tasks. We first present two applications that motivate the need for these approaches in meteorology, namely the detection of convection from satellite imagery and solar forecasting. Then we provide an overview of concepts and techniques that are helpful for the interpretation of meteorological image sequences, such as (a) feature engineering methods using (i) meteorological knowledge, (ii) classic image processing, (iii) harmonic analysis, and (iv) topological data analysis; (b) ways to use convolutional neural networks for this purpose with emphasis on discussing different convolution filters (2D/3D/LSTM-convolution); and (c) a brief survey of several other concepts, including the concept of “attention” in neural networks and its utility for the interpretation of image sequences and strategies from self-supervised and transfer learning to reduce the need for large labeled datasets. We hope that presenting an overview of these tools—many of which are not new but underutilized in this context—will accelerate progress in this area.

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Exploiting Satellite Data for Solar Performance Modeling

Cited by 2 publications

References 10 publications

A moment in the sun

A moment in the sun

Leveraging spatiotemporal information in meteorological image sequences: From feature engineering to neural networks

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