Title: DeepSnow: Modeling the Impact of Snow on Solar Generation
The falling cost of solar energy deployments has resulted in ever-increasing growth in solar capacity worldwide. The primary challenge posed by increasing grid-tied solar capacity is handling its variability due to continuously changing conditions. Thus, prior work has developed highly sophisticated models to estimate and forecast solar power output based on many characteristics, including location, elevation, time, weather, shading, module type, wiring, etc. These models are highly accurate for estimating solar power, especially over long periods, for sites at low latitudes, i.e., closer to the equator. However, models for sites at higher latitudes are less accurate due to the effect of snow on solar output, since even a small amount of snow can cover panels and reduce power to zero. Improving the accuracy of these models for annual solar output by even 2--3% is significant, as power translates directly into revenue, which compounds over the system's lifetime. Thus, if a site produces 2--3% less power on average per year due to snow than a model predicts, it can mean the difference between a positive or negative return-on-investment.
To address the problem, we develop DeepSnow, a data-driven approach that models the effect of snow on solar power generation. DeepSnow integrates with existing solar modeling frameworks, and uses publicly available snow data to learn its effect on solar generation. We leverage deep learning to quantify the effect of different snow variables on solar power using 4 million hourly readings from 40 solar sites. We evaluate our approach on 10 solar sites, and show that it yields a higher accuracy than the current approach for modeling snow effects used by the U.S. Department of Energy's System Advisor Model (SAM), a popular solar modeling framework. more »« less
Bansal, Akansha Singh; Irwin, David(
, 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm))
null
(Ed.)
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.
Bashir, Noman; Chen, Dong; Irwin, David; Shenoy, Prashant(
, IEEE 16th International Conference on Mobile Ad Hoc and Sensor Systems (MASS))
Solar energy capacity is continuing to increase. The key challenge with integrating solar into buildings and the electric grid is its high power generation variability, which is a function of many factors, including a site's location, time, weather, and numerous physical attributes. There has been significant prior work on solar performance modeling and forecasting that infers a site's current and future solar generation based on these factors. Accurate solar performance models and forecasts are also a pre-requisite for conducting a wide range of building and grid energy-efficiency research. Unfortunately, much of the prior work is not accessible to researchers, either because it has not been released as open source, is time-consuming to re-implement, or requires access to proprietary data sources. To address the problem, we present Solar-TK, a data-driven toolkit for solar performance modeling and forecasting that is simple, extensible, and publicly accessible. Solar-TK's simple approach models and forecasts a site's solar output given only its location and a small amount of historical generation data. Solar-TK's extensible design includes a small collection of independent modules that connect together to implement basic modeling and forecasting, while also enabling users to implement new energy analytics. We plan to release Solar-TK as open source to enable research that requires realistic solar models and forecasts, and to serve as a baseline for comparing new solar modeling and forecasting techniques. We compare Solar-TK's simple approach with PVlib and show that it yields comparable accuracy. We present three case studies showing how Solar-TK can advance energy-efficiency research.
Conventional lithium-ion batteries are unable to meet the increasing demands for high-energy storage systems, because of their limited theoretical capacity. 1 In recent years, intensive attention has been paid to enhancing battery energy storage capability to satisfy the increasing energy demand in modern society and reduce the average energy capacity cost. Among the candidates for next generation high energy storage systems, the lithium sulfur battery is especially attractive because of its high theoretical specific energy (around 2600 W h kg-1) and potential cost reduction. In addition, sulfur is a cost effective and environmentally friendly material due to its abundance and low-toxicity. 2 Despite all of these advantages, the practical application of lithium sulfur batteries to date has been hindered by a series of obstacles, including low active material loading, poor cycle life, and sluggish sulfur conversion kinetics. 3 Achieving high mass loading cathode in the traditional 2D planar thick electrode has been challenged. The high distorsion of the traditional planar thick electrodes for ion/electron transfer leads to the limited utilization of active materials and high resistance, which eventually results in restricted energy density and accelerated electrode failure. 4 Furthermore, of the electrolyte to pores in the cathode and utilization ratio of active materials. Catalysts such as MnO 2 and Co dopants were employed to accelerate the sulfur conversion reaction during the charge and discharge process. 5 However, catalysts based on transition metals suffer from poor electronic conductivity. Other catalysts such as transition metal dopants are also limited due to the increased process complexities. . In addition, the severe shuttle effects in Li-S batteries may lead to fast failures of the battery. Constructing a protection layer on the separator for limiting the transmission of soluble polysulfides is considered an effective way to eliminate the shuttle phenomenon. However, the soluble sulfides still can largely dissolve around the cathode side causing the sluggish reaction condition for sulfur conversion. 5 To mitigate the issues above, herein we demonstrate a novel sulfur electrode design strategy enabled by additive manufacturing and oxidative vapor deposition (oCVD). Specifically, the electrode is strategically designed into a hierarchal hollow structure via stereolithography technique to increase sulfur usage. The active material concentration loaded to the battery cathode is controlled precisely during 3D printing by adjusting the number of printed layers. Owing to its freedom in geometry and structure, the suggested design is expected to improve the Li ions and electron transport rate considerably, and hence, the battery power density. The printed cathode is sintered at 700 °C at N 2 atmosphere to achieve carbonization of the cathode during which intrinsic carbon defects (e.g., pentagon carbon) as catalytic defect sites are in-situ generated on the cathode. The intrinsic carbon defects equipped with adequate electronic conductivity. The sintered 3D cathode is then transferred to the oCVD chamber for depositing a thin PEDOT layer as a protection layer to restrict dissolutions of sulfur compounds in the cathode. Density functional theory calculation reveals the electronic state variance between the structures with and without defects, the structure with defects demonstrates the higher kinetic condition for sulfur conversion. To further identify the favorable reaction dynamic process, the in-situ XRD is used to characterize the transformation between soluble and insoluble polysulfides, which is the main barrier in the charge and discharge process of Li-S batteries. The results show the oCVD coated 3D printed sulfur cathode exhibits a much higher kinetic process for sulfur conversion, which benefits from the highly tailored hierarchal hollow structure and the defects engineering on the cathode. Further, the oCVD coated 3D printed sulfur cathode also demonstrates higher stability during long cycling enabled by the oCVD PEDOT protection layer, which is verified by an absorption energy calculation of polysulfides at PEDOT. Such modeling and analysis help to elucidate the fundamental mechanisms that govern cathode performance and degradation in Li-S batteries. The current study also provides design strategies for the sulfur cathode as well as selection approaches to novel battery systems. References: Bhargav, A., (2020). Lithium-Sulfur Batteries: Attaining the Critical Metrics. Joule 4 , 285-291. Chung, S.-H., (2018). Progress on the Critical Parameters for Lithium–Sulfur Batteries to be Practically Viable. Advanced Functional Materials 28 , 1801188. Peng, H.-J.,(2017). Review on High-Loading and High-Energy Lithium–Sulfur Batteries. Advanced Energy Materials 7 , 1700260. Chu, T., (2021). 3D printing‐enabled advanced electrode architecture design. Carbon Energy 3 , 424-439. Shi, Z., (2021). Defect Engineering for Expediting Li–S Chemistry: Strategies, Mechanisms, and Perspectives. Advanced Energy Materials 11 . Figure 1
Singh Bansal, Akansha; Irwin, David(
, Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys))
null
(Ed.)
Developing accurate solar performance models, which infer solar output from widely available external data sources, is increasingly important as the grid's solar capacity rises. These models are important for a wide range of solar analytics, including solar forecasting, resource estimation, and fault detection. The most significant error in existing models is inaccurate estimates of clouds' effect on solar output, as cloud formations and their impact on solar radiation are highly complex. In 2018 and 2019, respectively, the National Oceanic and Atmospheric Administration (NOAA) in the U.S. began releasing multispectral data comprising 16 different light wavelengths (or channels) from the GOES-16 and GOES-17 satellites every 5 minutes. Enough channel data is now available to learn solar performance models using machine learning (ML). In this paper, we show how to develop both local and global solar performance models using ML on multispectral data, and compare their accuracy to existing physical models based on ground-level weather readings and on NOAA's estimates of downward shortwave radiation (DSR), which also derive from multispectral data but using a physical model. We show that ML-based solar performance models based on multispectral data are much more accurate than weather- or DSR-based models, improving the average MAPE across 29 solar sites by over 50% for local models and 25% for global models.
Bansal, Akansha Singh; Bansal, Trapit; Irwin, David(
, Proceedings of the Thirteenth ACM International Conference on Future Energy Systems (e-Energy))
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.
Bashir, Noman, Irwin, David, and Shenoy, Prashant. DeepSnow: Modeling the Impact of Snow on Solar Generation. Retrieved from https://par.nsf.gov/biblio/10298238. Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys) . Web. doi:10.1145/3408308.3427620.
Bashir, Noman, Irwin, David, & Shenoy, Prashant. DeepSnow: Modeling the Impact of Snow on Solar Generation. Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys), (). Retrieved from https://par.nsf.gov/biblio/10298238. https://doi.org/10.1145/3408308.3427620
Bashir, Noman, Irwin, David, and Shenoy, Prashant.
"DeepSnow: Modeling the Impact of Snow on Solar Generation". Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys) (). Country unknown/Code not available. https://doi.org/10.1145/3408308.3427620.https://par.nsf.gov/biblio/10298238.
@article{osti_10298238,
place = {Country unknown/Code not available},
title = {DeepSnow: Modeling the Impact of Snow on Solar Generation},
url = {https://par.nsf.gov/biblio/10298238},
DOI = {10.1145/3408308.3427620},
abstractNote = {The falling cost of solar energy deployments has resulted in ever-increasing growth in solar capacity worldwide. The primary challenge posed by increasing grid-tied solar capacity is handling its variability due to continuously changing conditions. Thus, prior work has developed highly sophisticated models to estimate and forecast solar power output based on many characteristics, including location, elevation, time, weather, shading, module type, wiring, etc. These models are highly accurate for estimating solar power, especially over long periods, for sites at low latitudes, i.e., closer to the equator. However, models for sites at higher latitudes are less accurate due to the effect of snow on solar output, since even a small amount of snow can cover panels and reduce power to zero. Improving the accuracy of these models for annual solar output by even 2--3% is significant, as power translates directly into revenue, which compounds over the system's lifetime. Thus, if a site produces 2--3% less power on average per year due to snow than a model predicts, it can mean the difference between a positive or negative return-on-investment. To address the problem, we develop DeepSnow, a data-driven approach that models the effect of snow on solar power generation. DeepSnow integrates with existing solar modeling frameworks, and uses publicly available snow data to learn its effect on solar generation. We leverage deep learning to quantify the effect of different snow variables on solar power using 4 million hourly readings from 40 solar sites. We evaluate our approach on 10 solar sites, and show that it yields a higher accuracy than the current approach for modeling snow effects used by the U.S. Department of Energy's System Advisor Model (SAM), a popular solar modeling framework.},
journal = {Proceedings of the 7th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys)},
author = {Bashir, Noman and Irwin, David and Shenoy, Prashant},
editor = {null}
}
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