Search NASA⌕ Search

SEARCH · Search NASA

Results for “Solar Training”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS2012

Computation of Solar Radiative Fluxes by 1D and 3D Methods Using Cloudy Atmospheres Inferred from A-train Satellite Data

The main point of this study was to use realistic representations of cloudy atmospheres to assess errors in solar flux estimates associated with 1D radiative transfer models. A scene construction algorithm, developed for the EarthCARE satellite mission, was applied to CloudSat, CALIPSO, and MODIS satellite data thus producing 3D cloudy atmospheres measuring 60 km wide by 13,000 km long at 1 km grid-spacing. Broadband solar fluxes and radiances for each (1 km)2 column where then produced by a Monte Carlo photon transfer model run in both full 3D and independent column approximation mode (i.e., a 1D model).

Barker, Howard W.↗
NASA NTRS1995

Geomagnetic Response to Large-amplitude Interplanetary Alfven Wave Trains

A review of the geomagnetic response to large-amplitude interplanetary Alfven wave trains is presented, highlighting its dependence on solar activity level and its role in the storm/substorm relationship problem. Also discussed are some recent observations obtained by the Ulysses spacecraft at high heliospheric latitudes dealing with the origin and dynamics of these wave trains.

Space Physics Geomagnetics Alfven Wave Trains Ulys↗
DOE OSTI2025

Applying AI to Help Predict Solar Power Output Over Time

Poster with results from ongoing research on re-training pv-vision to segment images of solar modules. Presenting at the Sandia Analytics for Climate, Energy, and Earth Sciences (ACEES) 2025 Symposium.

Sanghi, Ojas NMN [Sandia National Laboratories (SN↗
DOE OSTI2022

Ute Mountain Ute Tribe Community-Scale Solar Project (Final Technical Report)

Deployment of Community-Scale solar power has been a goal of the Ute Mountain Ute Tribe (Tribe) for several years. The 1 MW AC photovoltaic power facility was built to power Tribal facilities in a manner that would allow the Tribe to credit residents’ electric bills and Tribal government electric bills with the dollar equivalent of the power generated. Other accomplished project objectives, in addition to learning the intricacies of such an endeavor, included strengthening the relationship with the local electric cooperative and their primary power provider, analysis of the generation and operations and maintenance during a one-year test phase, workforce development and training, and using the project to plan and drive future community scale solar projects. The Tribe’s vision of a net-zero electrical independence for its communities became a clearer reality with this project.

14 SOLAR ENERGY↗
DOE OSTI2023

An Updated Review of the Solar PV Installation Workforce Literature

In order to develop a well-trained, equitable, and inclusive workforce with high quality jobs, the DOE Solar Energy Technologies Office (SETO) identified a need for analytical context around different PV project characteristics and labor aspects, and how they might impact workforce wellbeing and PV industry growth. To determine the most valuable novel analytical contributions, this work reviews existing literature on solar workforce topics to identify what areas have been studied previously and where gaps remain. Focus areas included the following topics: (1) metrics for solar workforce, deployment, costs, and associated studies capturing aspects such as demographics and regional distribution, (2) solar workforce wellbeing, including employee contracting mechanisms, compensation, occupational safety and health, and community impacts, (3) national and state policies most relevant to the U.S. solar workforce, and (4) ongoing efforts to expand solar workforce participation, including local staffing dynamics, challenges, and relevant strategies.

14 SOLAR ENERGY↗
NASA NTRS2017

Training Early Career Scientists in Flight Instrument Design Through Experiential Learning: NASA Goddard's Planetary Science Winter School.

The NASA Goddard Planetary Science Winter School (PSWS) is a Goddard Space Flight Center-sponsored training program, managed by Goddard's Solar System Exploration Division (SSED), for Goddard-based postdoctoral fellows and early career planetary scientists. Currently in its third year, the PSWS is an experiential training program for scientists interested in participating on future planetary science instrument teams. Inspired by the NASA Planetary Science Summer School, Goddard's PSWS is unique in that participants learn the flight instrument lifecycle by designing a planetary flight instrument under actual consideration by Goddard for proposal and development. They work alongside the instrument Principal Investigator (PI) and engineers in Goddard's Instrument Design Laboratory (IDL; idc.nasa.gov), to develop a science traceability matrix and design the instrument, culminating in a conceptual design and presentation to the PI, the IDL team and Goddard management. By shadowing and working alongside IDL discipline engineers, participants experience firsthand the science and cost constraints, trade-offs, and teamwork that are required for optimal instrument design. Each PSWS is collaboratively designed with representatives from SSED, IDL, and the instrument PI, to ensure value added for all stakeholders. The pilot PSWS was held in early 2015, with a second implementation in early 2016. Feedback from past participants was used to design the 2017 PSWS, which is underway as of the writing of this abstract.

Flight↗
DOE OSTI2024

Atmospheric parameters and chemical abundances within 100 pc: a sample of G, K, and M main-sequence stars

ABSTRACT To date, we have access to enormous inventories of stellar spectra that allow the extraction of atmospheric parameters and chemical abundances essential in stellar studies. However, characterizing such a large amount of data is complex and requires a good understanding of the studied object to ensure reliable and homogeneous results. In this study, we present a methodology to measure homogenously the basic atmospheric parameters and detailed chemical abundances of over 1600 thin disc main-sequence stars in the 100 pc solar neighbourhood, using APOGEE-2 infrared spectra. We employed the code tonalli to determine the atmospheric parameters using a prior on $\log {g}$. The $\log {g}$ prior in tonalli implies an understanding of the treated population and helps to find physically coherent answers. Our atmospheric parameters agree within the typical uncertainties (100 K in $\mathrm{T_{eff}}$, 0.15 dex in $\log {g}$ and [M/H]) with previous estimations of ASPCAP and Gaia DR3. We use our temperatures to determine a new infrared colour–temperature sequence, in good agreement with previous works, that can be used for any main-sequence star. Additionally, we used the bacchus code to determine the abundances of Mg, Al, Si, Ca, and Fe in our sample. The five elements (Mg, Al, Si, Ca, Fe) studied have an abundance distribution centred around slightly subsolar values in agreement with previous results for the solar neighbourhood. The over 1600 main-sequence stars’ atmospheric parameters and chemical abundances presented here are useful in follow-up studies of the solar neighbourhood or as a training set for data-driven methods.

López-Valdivia, Ricardo (ORCID:0000000277950018)↗
NASA NTRS1988

Crew activities, science, and hazards of manned missions to Mars

The crew scientific and nonscientific activities that will occur at each stage of a mission to Mars are examined. Crew activities during the interplanetary flight phase will include simulations, maintenance and monitoring, communications, upgrading procedures and operations, solar activity monitoring, cross-training and sharpening of skills, physical conditioning, and free-time activities. Scientific activities will address human physiology, human psychology, sociology, astronomy, space environment effects, manufacturing, and space agriculture. Crew activities on the Martian surface will include exploration, construction, manufacturing, food production, maintenance and training, and free time. Studies of Martian geology and atmosphere, of the life forms that may exist there, and of the Martian moons will occur on the planet's surface. Crew activities and scientific studies that will occur in Mars orbit, and the hazards relevant to each stage of the mission, are also addressed.

Clark, Benton C.↗
DOE OSTI2021

A novel machine learning based identification of potential adopter of rooftop solar photovoltaics

With the proliferation of rooftop solar photovoltaic installations, there is a need to proactively predict consumer potential for solar photovoltaic adoption, for improved electric utility planning and operation. Traditional analytical modeling approaches are limited to a few survey features and a larger part of the survey would remain untouched by the decision model. This article presents a novel, data-driven modeling approach that strategically prunes a large set of consumer profile features using a machine learning framework to train a model for predicting potential solar adoption. The approach utilizes the Gradient Boosting Decision Tree model through a Light Gradient Boosting framework that improves significantly over the poor prediction accuracy of the existing approaches. Model training using focal-loss based supervision is used to overcome the difficulty in identifying the potential adopters that is inherent in conventional data-driven models. In addition, to overcome possible data sparsity in a limited survey sample, a Generative Adversarial Network is presented to create synthetic user samples and its effectiveness on model performance is assessed. A Bayesian optimization approach is used to systematically arrive at the hyperparameters of the proposed model. Validation of the presented approach on a survey data collected by the National Rural Electric Cooperative Association in Virginia in 2018 demonstrates the excellent predictive capability of the machine learning based approach to modeling solar adoption reliably.

14 SOLAR ENERGY↗
NASA NTRS1994

Ulysses field and plasma observations of magnetic holes in the solar wind and their relation to mirror-mode structures

The term 'magnetic hole' has been used to denote isolated intervals when the magnitude of the interplanetary magnetic field drops to a few tenths, or less, of its ambient value for a time that corresponds to a linear dimension of tens to a few hundreds of proton gyro-radii. Data obtained by the Ulysses magnetometer and solar wind anlayzer have been combined to study the properties of such magnetic holes in the solar wind between 1 AU and 5.4 AU and to 23 deg south latitude. In order to avoid confusion with decreases in field strength at interplanetary discontinuities, the study has focused on linear holes across which the field direction changed by less than 5 deg. The holes occurred preferentially, but not without exception, in the interaction regions on the leading edges of high-speed solar wind streams. Although the plasma surrounding the holes was generally stable against the mirror instability, there are indications that the holes may have been remnants of mirror-mode structures created upstream of the points of observation. Those indications include the following: (1) For the few holes for which proton of alpha-particle pressure could be measured inside the hole, the ion thermal pressure was always greater than in the plasma adjacent to the holes. (2) The plasma surrounding many of the holes was marginally stable for the mirror mode, while the plasma environment of all holes was significantly closer to mirror instability than was the average solar wind. (3) The plasma containing trains of closely spaced holes was closer to mirror instability than was the plasma containing isolated holes. (4) The near-hole plasma had much higher ion beta (ratio of thermal to magnetic pressure) than did the average solar wind. (5) Near the holes, T(sub perp)/T(sub parallel) tended to be either greater than 1 or larger than in the average wind. (6) The proton and alpha-particle distribution functions measured inside the holes occasionally exhibited the flattened phase-space-density contoures in nu(sub perp)/nu(sub parallel) space found in some numerical simulations of the mirror instability.

Winterhalter, Daniel↗
DOE OSTI2021

SoDa: An Irradiance-Based Synthetic Solar Data Generation Tool (SoDa) v0.1

SoDa is an irradiance-based synthetic Solar Data generation tool to generate realistic sub-minute solar photovoltaic (PV) power time series, that emulate the weather pattern for a certain geographical location. Our tool relies on the National Solar Radiation Database (NSRDB) to obtain irradiance and weather data patterns for the site. Irradiance is mapped onto a PV model estimate of a solar plant's 30-min power output, based on the configuration of the panel. We use a stochastic model with a switching behavior due to different weather regimes as provided by the cloud type label in the NSRDB, with parameters for the cloudy states trained on the high-resolution solar power measurements from a Phasor Measurement Unit (PMU).

Carreno, IgnacioLosada↗
DOE OSTI2022

National Community Solar Partnership Credit Ready Solar Initiative

This fact sheet discusses the Credit Ready Solar Initiative, which will leverage working groups, technical assistance, training, and strategic partnerships to build a pipeline of credit-ready community solar projects and broad access to a community solar marketplace that connects owners, developers, lenders, and philanthropy to support more equitable deployment of community solar.

solar, solar energy, community solar, national com↗
DOE OSTI2021

Solar Energy Technologies Office Workforce Request for Information and Convenings (Summary)

On May 4, 2021, the U.S. Department of Energy (DOE) Solar Energy Technologies Office (SETO) published a Request for Information (RFI) on programs that support the development of a diverse and skilled clean energy workforce. The purpose of the RFI was to solicit feedback from industry, academia, government agencies, worker organizations (including unions), and other stakeholders on issues related to the employment needs of the solar industry, and the perceived value of different workforce development programs, training strategies, and tools. To supplement the RFI, SETO hosted four virtual convenings that brought together the utility-scale solar industry, the distributed generation solar industry, and labor and other workforce training organizations to hear direct feedback on the questions in the RFI. In addition, SETO held listening sessions with about a dozen other organizations and staff who could not participate in the virtual convenings. Altogether, SETO received 45 responses from the RFI and heard directly from 80-100 other stakeholders via the convenings and listening sessions. This document summarizes the stakeholder feedback that SETO received as a result of this process. While both the RFI and convening series were focused on solar deployment and solar industry members, much of this information is relevant across clean energy technologies and programs. It is important to recognize that DOE is intentionally reviewing our workforce development programming and support to focus on clean energy careers more holistically.

14 SOLAR ENERGY↗
DOE OSTI2023

CyberStrike STORMCLOUD

This presentation will be shared at the "Securing Solar for the Grid (S2G)" workshop at RE+ 2023. It provides an overview of the work INL and Sandia have done to develop CyberStrike STORMCLOUD, a training program was designed to enhance the ability of renewable energy and operators to prepare for a cyber incident impacting industrial control systems with specific considerations of the architectures and limitations of renewable energy.

14 SOLAR ENERGY↗
NASA NTRS2018

An Artificial Neural Network for Inferring Solar Wind Proxies at Mars

We present a novel method to determine solar wind proxies from sheath measurements at Mars. Specifically, we develop an artificial neural network (ANN) to simultaneously infer seven solar wind proxies: ion density, ion speed, ion temperature, and interplanetary magnetic field magnitude and its vector components, using spacecraft measurements of ion moments, magnetic field magnitude, magnetic field components in the sheath, and the solar extreme ultraviolet flux. The ANN was trained and tested using3 years of data from the Mars Atmosphere and Volatile EvolutioN (MAVEN) spacecraft. When compared with MAVEN spacecraft's in situ measured values of the solar wind parameters, we find that the ANN proxies for the solar wind ion density, ion speed, ion temperature, and interplanetary magnetic field magnitude havepercentage differences of 50% or less for 84.4%, 99.9%, 86.8%, and 79.8% of the instances, respectively. Forthe cone angle and clock angle proxies, 69.1% and 53.3% of instances, respectively, have angle differences of 30* or less.

Neural Network↗
DOE OSTI2024

Short-Term Probabilistic Solar Forecasting via Reinforcement Learning over ECMWF

In this paper, we present an innovative reinforcement learning approach for short-term solar forecasting, leveraging data from the European Centre for Medium-Range Weather Forecasts (ECMWF). The methodology begins with the application of the System Advisor Model (SAM) to transform various ECMWF numerical weather prediction members into predictive photovoltaic power generation. To enhance the precision of deterministic forecasting, we introduce a dynamic model selection algorithm based on Q-learning. This algorithm dynamically identifies and utilizes the most accurate ensemble member for forecasting purposes. Furthermore, we employ a support vector regression surrogate model with a Gaussian distribution to generate probabilistic forecasts, providing a holistic view of solar energy generation uncertainty. To expedite the training process and make it more practical for real-world applications, we integrate a rolling update workflow. This innovative workflow reduces the training period from months to a mere 19 days, making our method highly efficient. Numerical results of the case study show that in comparison to benchmark models, the proposed method improves the deterministic and probabilistic solar forecasting accuracy by up to 40.84% and 48.42%, respectively.

ensemble forecasting↗
DOE OSTI2023

High-Resolution Synthetic Solar Irradiance Sequence Generation: An LSTM-Based Generative Adversarial Network

The rapid growth of renewable energy resources penetration is bringing more challenges to power system planning and operation. Relevant renewable energy integration studies, such as the capability and dynamic performance of inverter-based resources' primary frequency response and fast frequency response, require high-resolution renewable generation output data that are representative of renewable energy resources. This paper focuses on creating synthetic but realistic solar irradiance data and proposes a long short-term memory-based generative adversarial network to generate high-resolution (second-level) solar irradiance sequences from low-resolution (minute-level) measurements. Combined with a classifier to recognize the solar irradiance patterns, the proposed model is trained using multi-loss functions to accurately capture the temporal correlations among both high-resolution and low-resolution sequences. Verification of the proposed approach is performed on the data set of the Oahu Solar Measurement Grid collected through the National Renewable Energy Laboratory. The results of the case studies demonstrate the proposed approach's capability to capture the statistical characteristics of different solar irradiance patterns and to generate high-quality synthetic solar irradiance sequences in high resolution.

dynamic scheduling↗