We use a full year (2018) of GOES-R satellite data to produce 5-minute resolved information on cloud coverage for 7 Surface Radiation Budget Network (SURFRAD). The remote sensing images are then processed using the Spectral Cloud Optical Property Estimation (SCOPE) method in conjunction with Convolution Neural Networks (CNNs) for nowcasts and 1-hour ahead forecasts. We propose and compare two CNN based models for GHI now-casting with a 1-hour forecast horizon and a time resolution that processes data every 5 minutes: one enhanced with the SCOPE method for cloud estimation and another without it. The inclusion of SCOPE-derived information significantly improves model performance, yielding an average RMSE of 44.7 versus 68.9 W/m2 2 for the model without SCOPE information. The present work underscores the efficacy of even basic CNNs in interpreting satellite imagery combined with atmospheric models of the atmosphere to estimate ground irradiance accurately. The hybrid SCOPE-CNN model outperforms the basic CNN model that relies solely on 10 longwave channels, indicating the relevance of SCOPE's physical features in enhancing predictions across various solar micro-climates. Further advancements using Convolutional Long Short-Time Memory (ConvLSTM) schemes lead to the development of a SCOPE-CNN-ConvLSTM model, showcasing significant enhancements over smart persistence across all conditions.

The aluminum minichannel solar collector is a novel technology for solar water heating. Minichannel-based solar collectors have higher thermal efficiency than conventional flat plate collectors and do not suffer from potential loss of vacuum as evacuated-tube collectors. This technology can play a significant role in reducing natural gas consumption that translates into lower greenhouse gas emissions to the atmosphere. However, the performance of solar collectors depends on the geographical location of the installation due to solar resource availability and weather pattern. The potential reduction in natural gas consumption using aluminum minichannel solar collectors is assessed using solar irradiance, ambient temperature, and wind data obtained from ground weather station and satellite-derived data. A data-driven numerical analysis is performed using a validated solar water heater (SWH) model, population, and natural gas consumption data for the entire state of California to assess the best locations to install these systems. The SWH model is validated based on data collected from an actual SWH system installed at a single-family house in Northridge, California. A K-means clustering method is then applied to select the best regions for installation of this technology. Based on performance, population density, and natural gas consumption, the regions of Southern California and the Central Valley are chosen as having the highest potential for reduction of natural gas consumption. The analysis was performed from weather data obtained based on two full years (2020 and 2022), where the effect of COVID-19 (year 2020) is observed as having higher water tank temperatures and higher solar fractions, which could be associated with lower hot water consumption.

The stationary behavior of freely moving spherical particles under harmonic flow forcing from a Newtonian fluid ranging from low to high particle Reynolds (Re-p) numbers and from low to high Strouhal (Sl) numbers is studied numerically. This study extends the classical studies on harmonic Stokes flows by exploring particle dynamics in regimes where the convective contributions can no longer be neglected. High-order finite-element numerical simulations determine the order of the derivative that satisfies the long term (stationary) solution for the particle velocities. We propose a new history drag expression that correlates well (R-2 > 0.995) with the numerical results for (radius-based) particle Reynolds numbers up to 10, and for dimensionless frequency (S = SlRe(p)) values up to 10. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Several studies have focused on the analytical modeling of centrifugal compressors. These models are commonly used in design, optimization and evaluation of rotor geometry. Existing models, which rely on either the meanline method (1D model) or the streamline curvature-based throughflow method (2D model) combined with semi-empirical pressure loss relations, are closed-loop models that do not account for pressure loss distribution along the blade channel. Additionally, these models simplify the rotor’s three-dimensional geometry, focusing primarily on the β angle and meanline length. To address these limitations, we introduce an open-loop analytical approach called the’loss-integrated throughflow method’ (LITM). This method computes pressure losses step-by-step along the blade’s channel geometry using the three-dimensional cross-section of the rotor as input data. The motivation behind this new approach is to develop an alternative tool for the preliminary design of supercritical CO2 (sCO2) compressors, providing insight into the relation between losses and rotor geometry beyond just the β angle and meanline length. In the present work we compare simulation results with a one-dimensional (1D) mathematical model that has been validated against experimental for sCO2. We compare three sets of rotors, where each set shares identical β angles and meanline lengths. When compared against the 1D model, our model yields deviations ranging from 2.8% to 21.9% for rotors with matching meanline lengths and β angles. This suggests that factors other than the variables previously considered influence pressure losses. As a result of this study, we conclude that the proposed analytical model offers potential to refine the preliminary design for sCO2 rotors.

Clear sky emittance models provide critical information for the determination of downwelling longwave irradiance at the Earth's surface. This study updates existing calculations which relate clear sky longwave emissivity with the main (and most variable) greenhouse gas in the atmosphere, water vapor. Impacts of station elevation and data quality control are quantified. Empirical results are used to validate highly resolved spectral models, and the resultant simplified calculates provide accurate estimations of clear sky emissivity without the need for extensive computation. Results show that correlation coefficients are mostly robust to nuanced data processing choices when regressed from sufficiently large data sets (>= 104 samples) with the exceptions of altitude adjustment and measurement bias corrections. The empirical results from this study are compared to results from other leading empirical, physics-based, and hybridized phenomenological models. Correlations for effective clear sky emissivity, transmissivity and optical depth are provided, based on parameterized line-by-line (LBL) model results, for the broadband 0-2,500 cm-1 and for seven wavenumber wideband of interest. Results for the (b3) wideband 580-750 cm-1 are particularly relevant because of its disaggregated and combined carbon dioxide-water vapor contributions. The broadband effective optical depth (delta) of water vapor is found to be delta H2O=0.628+54.756pw ${\delta }_{{\mathrm{H}}_{2}\mathrm{O}}\,=\,0.628\,+\,54.756\,\,{p}_{w}$, where pw is the dimensionless partial pressure of water vapor at the surface. Equivalently, the broadband effective optical depth of carbon dioxide in the presence of water vapor is found to be delta CO2=0.269-10.229pw ${\delta }_{{\text{CO}}_{2}}=0.269-10.229\,{p}_{w}$. Processed training data sets are provided as supplementary content for comparative studies. Greenhouse gasses affect how Earth's atmosphere absorbs and re-emits heat to the Earth's surface. This absorption and re-emission can be studied with models over the entire infrared spectrum (broadband) and over discrete sections of the spectrum (spectral wideband) that assume a cloudless state of the atmosphere. This work builds on previous models and suggests new ways to handle data from ground observations to prepare well-formed data sets (called training sets) to which the models are fitted. Results show that the numerical coefficients found with these training sets are robust when corrected for relevant factors such as altitude or scale height. Additionally, effective radiative atmospheric properties like emissivity, transmissivity, and optical depth are modeled for individual gasses in the atmosphere (in the cloudless state) using a combination of both physical models and ground data. These properties are important for understanding the effect greenhouse gasses have on surface temperature, net radiation, and other factors that impact planetary climate. A clear methodology to parameterize clear sky emissivity is provided with context for the sensitivity of results to data preparation The proposed model shows good agreement with existing empirical and physics-based models Broadband and spectral wideband correlations for the optical depth values of individual atmospheric constituents are proposed

This work addresses challenges and opportunities in the evaluation of solar power plant impacts, with a particular focus on thermal effects of solar plants on the environment and vice-versa. Large-scale solar power plants are often sited in arid or desert habitats, which tend to include fauna and flora that are highly sensitive to changes in temperature and humidity. Our understanding of both shortwave (solar) and longwave (terrestrial) radiation processes in solar power plants is complete enough to render the modeling of radiation fluxes with high confidence for most applications. In contrast to radiation, the convective environment in large-scale solar power plants is much more difficult to characterize. Wind direction, wind speed, turbulence intensity, dust concentration, ground condition, panel configuration density, orientation and distribution throughout the solar field, all affect the local environment, the balance between radiation and convection, and in turn, the performance and thermal impact of solar power plants. Because the temperatures of the two sides of photovoltaic (PV) panels depend on detailed convection–radiation balances, the uncertainty associated with convection affects the heat and mass transfer balances as well. Those balances are critically important in estimating the thermal impact of large-scale solar farms on local habitats. Here we discuss outstanding issues related with these transfer processes for utility-scale solar generation and highlight potential pathways to gain useful knowledge about the convective environment directly from solar farms under operating conditions.

Several studies have focused on the analytical modeling of centrifugal compressors. These models are commonly used in design, optimization and evaluation of rotor geometry. Existing models, which rely on either the meanline method (1D model) or the streamline curvature-based throughflow method (2D model) combined with semi-empirical pressure loss relations, are closed-loop models that do not account for pressure loss distribution along the blade channel. Additionally, these models simplify the rotor's three-dimensional geometry, focusing primarily on the /i angle and meanline length. To address these limitations, we introduce an open-loop analytical approach called the 'loss-integrated throughflow method' (LITM). This method computes pressure losses step-by-step along the blade's channel geometry using the three-dimensional cross-section of the rotor as input data. The motivation behind this new approach is to develop an alternative tool for the preliminary design of supercritical CO2 2 (sCO2) 2 ) compressors, providing insight into the relation between losses and rotor geometry beyond just the /i angle and meanline length. In the present work we compare simulation results with a one-dimensional (1D) mathematical model that has been validated against experimental data for sCO2. 2 . We compare three sets of rotors, where each set shares identical /i angles and meanline lengths. When compared against the 1D model, our model yields deviations ranging from 2.8% to 21.9% for rotors with matching meanline lengths and /i angles. This suggests that factors other than the variables previously considered influence pressure losses. As a result of this study, we conclude that the proposed analytical model offers potential to refine the preliminary design for sCO2 2 rotors.

The response of spherical particles to oscillatory fluid flow forcing at finite Reynolds numbers exhibits significant deviations from classical analytical predictions due to nonlinear convective contributions. This study employs finite element simulations to explore the long-term (stationary) behavior of such particles across a wide range of conditions, including various external and particle Reynolds numbers, Strouhal numbers, and fluid-to-particle density ratios. Key contributions of this work include determining the range of validity of Tchen's equation of motion for infinitesimal and finite Reynolds numbers and correlating particle response for a wide range of density ratios and flow conditions at high frequency oscillations. This work introduces a modified form of the history drag term in a newly proposed Lagrangian equation of motion. The new equation incorporates a parameter-dependent fractional-order derivative tailored to accommodate nonlinearities due to convective effects. These novel correlations not only extend the operational range of existing model equations but also provide accurate estimates of particle response under a range of external flow conditions, as validated by comparison with numerical solutions of the Navier-Stokes flow around the particles.

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Current solar forecast verification processes place much attention on performance comparison of a group of competing methods. However, forecast verification ought to further answer how the best method within the group performs relative to the best-possible performance which one can attain under that forecasting situation, which makes the quantification of predictability and forecast skill immediately relevant. Unfortunately, the literature on the quantification of relative performance of solar irradiance has hitherto been lacking, and very few studies have focused on the spatial distributions of predictability and forecast skill of solar irradiance. The predictability and forecast skill of an atmospheric process depend on two concepts: (1) the growth of initial error in unresolved scale of motion, and (2) the forecast performance of the standard of reference. Based upon this formalism, predictability and forecast skill of solar irradiance in the United States are quantified and mapped. Through this study, a couple of common misconceptions in regard to irradiance predictability are refuted, and the original formulation of skill score revived.

Effective decarbonization strategies employ both hard and soft measures to address climate change. Soft approaches can deliver carbon savings comparable to hard approaches, which are typically both infrastructure- and investment-intensive and are often postponed due to financial risks. As demonstrated in this study, the time variable can be used as a lever to reduce energy demand through an academic calendar shift that aligns the end of fall term with Thanksgiving break, thus reducing the need for redundant holiday travel among a significant population. If implemented at all undergraduate campuses of the University of California (UC) system, this strategy would produce a significant reduction (nearly 50,000 tCO2e) in the annual carbon footprint of the UC, an impact approximately equal to decarbonizing all UC-owned vehicles. This outcome is robust to many of its key assumptions and can be realized at any higher education institution that sees a significant portion of its population travel for Thanksgiving. The proposed academic calendar shift is a prime example of a soft decarbonization measure; it can be implemented within existing systems, provides numerous co-benefits, does not require new technologies, and augments ongoing hard decarbonization efforts that will lead to compounding benefits into the future.

A network of seven low-cost hemispheric sky-imaging cameras has been installed in the Los Angeles basin. This network of cameras provides wide sky coverage to perform spatial solar irradiance assessments. An Image to Irradiance algorithm (I2I) is proposed to simultaneously derive high-resolution diffuse, direct and global solar irradiance from sky images. Spatial interpolation using the Kriging method is used to derive the irradiance field for the whole basin area. The relatively inexpensive network of cameras can provide spatially resolved GHI that is more accurate than GHI derived from GOES-west satellite images provided by the Cooperative Institute for Meteorological Satellite Studies (CIMSS) when the distance to the nearest site is less than 40 km. This work successfully demonstrates that, with minor trade-off in accuracy, solar irradiance monitoring can be achieved using off-the-shelf cameras in the absence of radiometers. (C) 2022 The Authors. Published by Elsevier Ltd.

The physics of air-water interface transport processes under very low mixing conditions is still poorly characterized. Consequently, correlations for mass transfer rates at very low Grashof numbers in weakly convective flows are not readily available, which often leads incorrect estimations of evaporation rates that are derived from simplified 1-D models. Here we investigate the evaporation from open tubes that results in complex massline patterns due to the interaction of the vertical walls with the buoyant flow in near isothermal conditions. Using numerical simulations validated by experimental data for typical Normal Conditions of Temperature and Pressure (NCTP), we analyze the water vapor transport dynamics for open tubes with aspect ratios ranging from 2 to 11 at the temperature of 290 K and 310 K, and relative humidity values from 0 to 99% , all at 1 atm. We show the dependence of the diffusion-driven and convection-driven processes as functions of the geometrical aspect ratio of the tubes. We propose a new Sherwood number correlations valid for both isothermal and near-isothermal processes in the range of Grashof numbers from 50 to 4,0 0 0. This correlation, which is optimized for air-water interfaces and is nearly invariant with temperatures near NCTP, expresses the Sherwood and Grashof numbers for varying aspect ratios. The proposed correlation estimates evaporation rates within 5% of values obtained from 3-D numerical simulations for the entire range of Grashof and Sherwood numbers under study.(c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )

Renewable energy resourcing and forecasting are enabling technologies for low-cost integration of increasingly higher market penetration of low-carbon power generation into the grid. The “Best Practices in Renewable Energy Resourcing and Integration” Special Collection Issue in the Journal of Renewable and Sustainable Energy covers best practices in solar and wind forecasting for renewable energy integration and includes datasets for testing, development, and for the augmented reproducibility of methods and results. This Special Collection focuses on manuscripts containing methodologies that substantially advance the state-of-the-art in renewable resourcing and forecasting.

The ever-growing installation of solar power systems imposes severe challenges on the operations of local and regional power grids due to the inherent intermittency and variability of ground-level solar irradiance. In recent decades, solar forecasting methodologies for intra-hour, intra-day and day-ahead energy markets have been extensively explored as cost-effective technologies to mitigate the negative effects on the power grids caused by solar power instability. In this work, the progress in intra-hour solar forecasting methodologies are comprehensively reviewed and concisely summarized. The theories behind the forecasting methodologies and how these theories are applied in various forecasting models are presented. The reviewed mathematical tools include regressive methods, stochastic learning methods, deep learning methods, and genetic algorithm. The reviewed forecasting methodologies include data-driven methods, local-sensing methods, hybrid forecasting methods, and application orientated methods that generate probabilistic forecasts and spatial forecasts. Furthermore, suggestions to accelerate the development of future intra-hour forecasting methods are provided.

The objective of this research is to develop a hybrid physics-based/data-driven forecast model to improve direct normal and global horizontal irradiance (DNI and GHI) prediction for horizons ranging from 1 to 72 hours. Project objectives also address key gaps in state-of-the-art solar forecasting: accurate probabilistic solar forecasts and the forecasting of large irradiance ramps (ramp onset and magnitude). The proposed model ensembles Numerical Weather Prediction (NWP) forecasts, determinist physics-based algorithms, and new-generation cloud cover products (high-resolution rapid refresh satellite images and Large Eddy Simulations). The result is the Hybrid Adaptive Input Model Objective Selection (HAIMOS) ensemble model. HAIMOS blends state of the art machine learning methodologies with physics-based models for cloud cover and cloud optical depth forecasts. The technical activities followed a two-pronged strategy. First, the preprocessing of data, the selection of inputs to the nonlinear approximators, the type of approximator and objective functions, and post-processing ensembling techniques included in HAIMOS were all optimized adaptively to find the best model for a specific goal (reduce DNI/GHI forecast error, improve the prediction of ramp onset, etc.). Second, a large effort was put in improving cloud identification and the forecast of cloud cover and cloud optical depth. To this end, new-generation cloud parametrization products were developed in this work. These include improved algorithms to assist in cloud identification, cloud classification and cloud parametrization from satellite images – three key factors in the accuracy of 1 to 6-hours irradiance forecasts and prediction of ramp onset. Furthermore, we also included cloud information extracted from high resolution rapid refresh satellite images (GOES-16) and Large Eddy Simulations (LES). LES was used to model the atmosphere in detail over locations of interest and produce cloud optical depth forecasts. Once these data streams were validated, they were used as input data to the HAIMOS forecast. The model was developed using data from several climatologically distinct locations with potential for high solar penetration. In the last year of the project, we conducted a validation campaign according to the guidelines stipulated by the Topic Area 1 project as described in the FOA. This effort brings, for the first time, proven machine-learning methodologies for generating state-of-the-art solar forecasts interweaved with detailed physics-based models for cloud detection, and cloud optical depth forecasts. HAIMOS will generate accurate irradiance probabilistic forecast to assist in reducing solar generation prediction error. Globally optimized solar forecast models are more likely to impact solar energy stakeholders. The goal of this project was to increase the state-of-the-art forecast skill from their present values of 10 to 35%. At the end of the project, we achieved between 30% and 50% forecast skill across a wide range of horizons for both GHI and DNI.

Cloud detection is an important task for remote sensing and solar resource modeling, and an initial step toward more complex tasks like solar forecasting. Recent advances in remote sensing have increased the spectral, spatial, and temporal resolution of observations. This study demonstrates the image analysis capacity of convolutional neural networks (CNNs) to identify the presence of clouds from remote sensing directly, without ancillary data and at relatively high temporal resolution. Cloud detection in images from the Geostationary Operational Environmental Satellite (GOES)-16 Advanced Baseline Imager (ABI) is validated against ground telemetry from a set of 12 locations of diverse geographic and climatic conditions across the continental U.S. (CONUS). As spatial coverage through ground station networks is typically sparse, transfer learning of baseline models is studied in order to assess the robustness and portability of baseline models through transfer learning scenarios. Performance of the CNN-based cloud mask (CCM) is discussed in three parts: (1) baseline training in comparison to a deterministic model, the ABI cloud mask (ACM), (2) transfer learning of baseline models, and (3) transfer learning of models trained on combined location pairs. In baseline models, the CCM showed average percent improvement over the ACM of 11% in accuracy (ACC) and 30% in Matthews correlation coefficient (MCC) score. In transfer learning scenarios, adding a second location to training samples improved aggregate performance by 18.8%. Results show that CCM transfer learning is highly asymmetric. A framework is proposed to characterize and predict transfer learning performance with respect to this asymmetry.