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  1. Long-term spatial and temporal solar resource variability over America using the NSRDB version 3 (1998–2017)

    The study assesses the long-term spatial and temporal solar resource variability in America using the 20-year National Renewable Energy Laboratory's (NREL's) National Solar Radiation Database (NSRDB). Specifically, the coefficient of variation (COV) is used to analyze the spatial and temporal (interannual and seasonal) variability. Further, both spatial and temporal long-term variability are analyzed using the Köppen-Geiger climate classification. The temporal variability is found that, on average, the continental United States (CONUS) COV reaches up to 5% for global horizontal irradiance (GHI) and 10% for direct normal irradiance (DNI), and that the NSRDB domain's COV is roughly twice that of CONUS.more » For the seasonal variability analysis, the winter months are found to exhibit higher COV than the other seasons. In particular, December exhibits the highest variability, reaching on average 30% for DNI and 20% for GHI over various areas. On the other hand, the summer months demonstrate significantly lower variability, reaching only less than 20% for DNI and 10% for GHI, on average. Similarly, the spatial variability is analyzed by comparing each pixel to its neighbors. The long-term spatial variability is found to increase with the number of neighboring pixels being considered, which is equivalent to an increase in distance (within a 100-km x 100-km square grid). As expected, the DNI spatial variability is higher than that of GHI. Moreover, the annual solar irradiance anomalies are found to reach ±25% for both GHI and DNI (and even exceed those value in some instances) during each year of the 20-year period.« less
  2. A Physics-Based DNI Model Assessing All-Sky Circumsolar Radiation

    By investigating the long-term observations at Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP), we find that the routinely used Beer-Bouguer-Lambert law and the models that empirically separate direct normal irradiance (DNI) from measurements of global horizontal irradiance (GHI) have dramatic and unexpected bias in computing cloudy-sky DNI. This bias has led to tremendous uncertainty in estimating the electricity generation by solar energy conversion systems. To effectively reduce the bias, this study proposes a physical solution of all-sky DNI that computes solar radiation in the infinite-narrow beam along the sun direction and the scattered radiation falls within the circumsolar region.more » In sharp contrast with the other DNI models, this method uses a finite-surface integration algorithm that computes solar radiation in differential solid angles and efficiently infers its contribution to a surface perpendicular to the sun direction. The new model substantially reduces the uncertainty in DNI by a factor of 2-7.« less
  3. On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events

    Abstract Machine learned force fields typically require manual construction of training sets consisting of thousands of first principles calculations, which can result in low training efficiency and unpredictable errors when applied to structures not represented in the training set of the model. This severely limits the practical application of these models in systems with dynamics governed by important rare events, such as chemical reactions and diffusion. We present an adaptive Bayesian inference method for automating the training of interpretable, low-dimensional, and multi-element interatomic force fields using structures drawn on the fly from molecular dynamics simulations. Within an active learning framework,more » the internal uncertainty of a Gaussian process regression model is used to decide whether to accept the model prediction or to perform a first principles calculation to augment the training set of the model. The method is applied to a range of single- and multi-element systems and shown to achieve a favorable balance of accuracy and computational efficiency, while requiring a minimal amount of ab initio training data. We provide a fully open-source implementation of our method, as well as a procedure to map trained models to computationally efficient tabulated force fields.« less
  4. CERES MODIS Cloud Product Retrievals for Edition 4--Part I: Algorithm Changes

    The Edition 2 (Ed2) cloud property retrieval algorithm system was upgraded and applied to the MODerate-resolution Imaging Spectroradiometer (MODIS) data for the Clouds and the Earth's Radiant Energy System (CERES) Edition 4 (Ed4) products. New calibrations for solar channels and the use of the 1.24-μm channel for cloud optical depth (COD) over snow improve the daytime consistency between Terra and Aqua MODIS retrievals. Use of additional spectral channels and revised logic enhanced the cloud-top phase retrieval accuracy. A new ice crystal reflectance model and a CO₂-channel algorithm retrieved higher ice clouds, while a new regional lapse rate technique produced moremore » accurate water cloud heights than in Ed2. Ice cloud base heights are more accurate due to a new cloud thickness parameterization. Overall, CODs increased, especially over the polar (PO) regions. The mean particle sizes increased slightly for water clouds, but more so for ice clouds in the PO areas. New experimental parameters introduced in Ed4 are limited in utility, but will be revised for the next CERES edition. As part of the Ed4 retrieval evaluation, the average properties are compared with those from other algorithms and the differences between individual reference data and matched Ed4 retrievals are explored. Part II of this article provides a comprehensive, objective evaluation of selected parameters. More accurate interpretation of the CERES radiation measurements has resulted from the use of the Ed4 cloud properties.« less
  5. Solar Irradiance Capturing in Cloudy Sky Days–A Convolutional Neural Network Based Image Regression Approach

    Global horizontal irradiance (GHI) is a critical index to indicate the output power of the photovaltaic (PV). In traditional approaches, the local GHI can be measured with very expensive instruments, and the large-area GHI collection depends on complex satellite-based models, solargis algorithms, and the high-performance computers (HPC). In this paper, a novel approach is proposed to capture the GHI conveniently and accurately. Considering the nonstationary property of the GHI on cloudy days, the GHI capturing is cast as an image regression problem. In traditional approaches, the image regression problem is treated as two parts, feature extraction (for the images) andmore » regression model (for the regression targets), which are optimized separately and blocked the interconnections. Considering the nonlinear regression capability, a convolutional neural network (CNN) based image regression approach is proposed to provide an End-to-End solution for the cloudy day GHI capturing problem in this paper. The multilayer CNN is based on the AlexNet and VGG. The L2 (least square errors) with regularization is used as the loss function in the regression layer. For data cleaning, the Gaussian mixture model with Bayesian inference is employed to detect and eliminate the anomaly data in a nonparametric manner. The purified data are used as input data for the proposed image regression approach. In the experiments, three-month sky images and GHI data (with 1-min resolution) are provided by the National Renewable Energy Laboratory (NREL) with the HPC system. The numerical results demonstrate the feasibility and effectiveness of the proposed approach.« less
  6. A Fast All-sky Radiation Model for Solar applications with Narrowband Irradiances on Tilted surfaces (FARMS-NIT): Part II. The cloudy-sky model

    The Fast All-sky Radiation Model for Solar applications with Narrowband Irradiances on Tilted surfaces (FARMS-NIT) reported in Part I of this report is enhanced to include the requirements for cloudy-sky conditions. Surface radiances in 2002 narrow-wavelength bands from 0.28 to 4.0 um are analytically computed by solving the radiative transfer equation for five independent photon paths accounting for clear-sky absorption, Rayleigh scattering, and cloud absorption and scattering. The Simple Model of the Atmospheric Radiative Transfer of Sunshine (SMARTS) is used to show the optical thickness of the clear-sky atmosphere. Unlike Part I, which approximates the computation of aerosol scattering usingmore » the single-scattering phase function, the cloud transmittance and reflectance are efficiently retrieved from a comprehensive look-up table pre-computed by a 32-stream DIScrete Ordinates Radiative Transfer (DISORT) model for possible cloud conditions as well as solar and viewing geometries. A resolution assessment is performed to observe the optimal balance between the computational efficiency and accuracy in the development of the look-up table. Model simulations by DISORT and TMYSPEC are used to evaluate the performance of FARMS-NIT under cloudy-sky conditions. Compared to DISORT, FARMS-NIT yields 2-3% uncertainties on average, but it substantially reduces the computational time because of the independent computation of cloud properties and the implementation of the look-up table. In contrast to TMYSPEC, which uses successive steps to empirically compute plane-of-array (POA) irradiances and spectral irradiances, FARMS-NIT directly solves spectral radiances from the radiative transfer equation, which profoundly increases the accuracy in surface irradiances, especially over inclined photovoltaics (PV) panels.« less
  7. Computational Discovery and Design of MXenes for Energy Applications: Status, Successes, and Opportunities

    MXenes (Mn+1Xn, e.g., Ti3C2) are the largest 2D material family developed in recent years. They exhibit significant potential in the energy sciences, particularly for energy storage. In this review, we summarize the progress of the computational work regarding the theoretical design of new MXene structures and predictions for energy applications including their fundamental, energy storage, and catalytic properties. We also outline how high-throughput computation, big data, and machine-learning techniques can help broaden the MXene family. Finally, we present some of the major remaining challenges and future research directions needed to mature this novel materials family.
  8. A Physics-based Smart Persistence model for Intra-hour forecasting of solar radiation (PSPI) using GHI measurements and a cloud retrieval technique

    Short-term solar forecasting models based solely on global horizontal irradiance (GHI) measurements are often unable to discriminate the forecasting of the factors affecting GHI from those that can be precisely computed by atmospheric models. Here, we introduce a Physics-based Smart Persistence model for Intra-hour forecasting of solar radiation (PSPI) that decomposes the forecasting of GHI into the computation of extraterrestrial solar radiation and solar zenith angle and the forecasting of cloud albedo and cloud fraction. The extraterrestrial solar radiation and solar zenith angle are accurately computed by the Solar Position Algorithm (SPA) developed at the National Renewable Energy Laboratory (NREL).more » A cloud retrieval technique is used to estimate cloud albedo and cloud fraction from surface-based observations of GHI. With the assumption of persistent cloud structures, the cloud albedo and cloud fraction are predicted for future time steps using a two-stream approximation and a 5-min exponential weighted moving average, respectively. Our model evaluation using the long-term observations of GHI at NREL's Solar Radiation Research Laboratory (SRRL) shows that the PSPI has a better performance than the persistence and smart persistence models in all forecast time horizons between 5 and 60 min, which is more significant in cloudy-sky conditions. Finally, compared to the persistence and smart persistence models, the PSPI does not require additional observations of various atmospheric parameters but is customizable in that additional observations, if available, can be ingested to further improve the GHI forecast. An advanced technology of cloud forecast is also expected to improve the future performance of the PSPI.« less
  9. A Fast All-sky Radiation Model for Solar applications with Narrowband Irradiances on Tilted surfaces (FARMS-NIT): Part I. The clear-sky model

    The solar energy industry often uses individual steps to empirically compute plane-of-array (POA) irradiance from horizontal irradiance and decompose it to narrow-wavelength bands. Conventional radiative transfer models designed for meteorological applications requires significant computing efforts in practice; however, they provide a physics-based solution of radiance and therefore are capable of computing spectral POA irradiances in a single step. In this study, we integrate the advantages of the current models and develop an innovative radiative transfer model, the Fast All-sky Radiation Model for Solar applications with Narrowband Irradiances on Tilted surfaces (FARMS-NIT), to efficiently compute irradiances on inclined photovoltaics (PV) panelsmore » for 2002 narrow-wavelength bands from 0.28 to 4.0 um. This study is reported in two parts. Part I presents the methodology and performance evaluation of the new model under clear-sky conditions. The Simple Model of the Atmospheric Radiative Transfer of Sunshine (SMARTS), which was designed to compute clear-sky irradiances, is employed to rapidly provide the optical properties of a given clear-sky atmosphere. The clear-sky radiances in the narrow-wavelength bands are computed by considering three paths of photon transmission and solving the radiative transfer equation with the single-scattering approximation. The Bi-directional Transmittance Distribution Function (BTDF) of aerosols is given by their single-scattering phase function with a correction using a two-stream approximation. The validation analysis confirms that FARMS-NIT has improved accuracy compared to TMYSPEC as evaluated by both surface observations and a state-of-the-art radiative transfer model. This model substantially improves computational efficiency compared to other radiative transfer models though it uses slightly more computing time than TMYSPEC. Part II of this study addresses the model in cloud-sky conditions and will be published as a companion paper.« less
  10. The National Solar Radiation Data Base (NSRDB)

    The National Solar Radiation Data Base (NSRDB), consisting of solar radiation and meteorological data over the United States and regions of the surrounding countries, is a publicly open dataset that has been created and disseminated during the last 23 years. This paper briefly reviews the complete package of surface observations, models, and satellite data used for the latest version of the NSRDB as well as improvements in the measurement and modeling technologies deployed in the NSRDB over the years. The current NSRDB provides solar irradiance at a 4-km horizontal resolution for each 30-min interval from 1998 to 2016 computed bymore » the National Renewable Energy Laboratory's (NREL's) Physical Solar Model (PSM) and products from the National Oceanic and Atmospheric Administration's (NOAA's) Geostationary Operational Environmental Satellite (GOES), the National Ice Center's (NIC's) Interactive Multisensor Snow and Ice Mapping System (IMS), and the National Aeronautics and Space Administration's (NASA's) Moderate Resolution Imaging Spectroradiometer (MODIS) and Modern Era Retrospective analysis for Research and Applications, version 2 (MERRA-2). The NSRDB irradiance data have been validated and shown to agree with surface observations with mean percentage biases within 5% and 10% for global horizontal irradiance (GHI) and direct normal irradiance (DNI), respectively. The data can be freely accessed via or through an application programming interface (API). During the last 23 years, the NSRDB has been widely used by an ever-growing group of researchers and industry both directly and through tools such as NREL's System Advisor Model.« less

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