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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.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Automated analysis of unlabeled PV data with Solar Data Tools software: Overview and feature updates

Distributed rooftop PV systems: ubiquitous, yet commonly have unlabeled data Difficult or impossible to form a performance index We developed Solar Data Tools (SDT), an open-source Python library for analyzing PV power (and irradiance) time-series data SDT enables analysis of unlabeled PV data—no model, no meteorological data, no performance index required Takes a statistical signal processing approach Data processing steps are largely pre-defined and automatic regardless of system type—from utility tracking systems to multi-pitch rooftop systems

Meyers-Im, Bennet E↗
DOE OSTI2025

sdt (Solar Data Tools) [SWR-25-130]

Solar Data Tools (sdt) is an open-source Python library for analyzing PV power (and irradiance) time-series data. It was developed to enable analysis of unlabeled PV data, i.e. with no model, no meteorological data, and no performance index required, by taking a statistical signal processing approach in the algorithms used in the package’s main data processing pipeline. Solar Data Tools empowers PV system fleet owners or operators to analyze system performance a hundred times faster even when they only have access to the most basic data stream—power output of the system.

Meyers-Im, Bennet [National Laboratory of the Rock↗
DOE OSTI2022

Solar data uncertainty impacts on MCMC methods for r-process nucleosynthesis

In recent work, we developed a Markov Chain Monte Carlo (MCMC) procedure to predict the ground state masses capable of forming the observed Solar r-process rare-earth abundance peak. By applying this method to nucleosynthesis calculations which make use of distinct astrophysical conditions and comparing our results to the latest precision mass measurements, we are able to shed light on the conditions/masses capable of producing a rare-earth peak which matches Solar data. Here we examine how our mass predictions change when using a few different sets of r-process Solar abundance residuals that have been reported in the literature. We explore how the differing error estimates of these Solar evaluations propagate through the Markov Chain Monte Carlo to our mass predictions. We find that Solar data which reports the rare-earth peak to have its highest abundance at mass number A = 162 can require distinctly different mass predictions from data with the peak centered at A = 164. Nevertheless, we find that two important general conclusions from past work, regarding the inconsistency of ‘cold’ astrophysical outflows with current mass measurements and the need for local stability at N = 104 in ‘hot’ scenarios, remain robust in the face of differing Solar data evaluations. Additionally, we show that the masses our procedure finds capable of producing a peak at A < 164 are not in line with the latest precision mass measurements.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

NREL Solar Data, Analysis, & Tools: FY19 Research Dissemination Statistics

This report summarizes the key Fiscal Year 2019 (FY19) outreach metrics for the NREL Solar Data, Analysis, and Tools (DAT) portfolio. All metrics herein reflect the reporting period of Oct. 1, 2018, through Sept. 30, 2019. These projects are all supported by the U.S. Department of Energy Solar Energy Technologies Office Soft Costs program.

14 SOLAR ENERGY↗
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 OSTI2024

A Refined Method to Translate Solar Data Quality Assessment Flags to Estimated Measurement Uncertainty

Integrating solar resource uncertainties due to radiometer measurement performance and operational data quality assessment can provide improved estimates of economic bankability, system design performance, and compliance of solar energy conversion systems. Estimating radiometer measurement uncertainty is an established procedure consistent with recognized best practices and international guidelines. SERI QC is a robust solar data quality assessment software tool that has been in continuous use for more than three decades. This report, the fourth of six for the Data Quality and Uncertainty Integration Project, presents a refined algorithm description for software to translate solar resource data quality assessment results into estimated uncertainty values in a Solar Resource Operational Uncertainty Integrator (SROUI) application. This algorithm requires three-component solar irradiance measurements - global horizontal irradiance, direct normal irradiance, and diffuse horizontal irradiance - collected at 1- to 60-minute intervals, as described in the previous deliverables. The development of this report as Deliverable 6.4 was an iterative process that included reviews and feedback from the project team on initial drafts designed to refine how the new software could best support determining solar resource data uncertainty. The results of this effort will contribute to the final software system development by National Renewable Energy Laboratory staff.

14 SOLAR ENERGY↗
NASA NTRS1984

Solar-geophysical data number 479, July 1984. Part 1: (Prompt reports). Data for June 1984, May 1984 and later data

Solar and geophysical data for May and June 1984 are reported. Topics include: detailed index for 1983/1984; data for June 1984 (solar activity indices, solar flares, solar radio emission, mean solar magnetic field, boulder geomagnetic substorm log); data for May 1984 (solar active regions, sudden ionospheric disturbances, solar radio spectral observations, cosmic ray measurements by neutron monitor, geomagnetic indices, radio propagation indices); and late data (geomagnetic indices March and April 1984 sudden commencements/solar flare effects, cosmic ray measurements by neutron monitor, and solar active regions).

Coffey, H. E.↗
NASA NTRS1997

The solar minimum X2.6/1B flare and CME of 9 July 1996: Solar data - Pt. 1

The solar observations from GOES-8, the Solar and Heliospheric Observatory (SOHO), and the Yohkoh satellite concerning the events of the X-class flare are discussed. The Michelson Doppler imager (MDI) magnetometer shows a new region of magnetic activity in AR 7978. The rapid development and evolution of this region is shown by the MDI and the extreme-ultraviolet Doppler telescope (EDT) data. The coronal mass ejections (CMEs) observed using coronagraphs are presented. The possible association between the CME and the X-flare is considered.

Andrews, M. D.↗
DOE OSTI2020

Utility-Scale Solar Data Update (2020 Edition) [Slides]

Berkeley Lab’s 2020 update of utility-scale solar data and trends provides an overview of key trends in the U.S. market, with a focus on 2019. Highlights of this year’s update include: More than 4.5 GWAC of utility-scale solar (i.e., ground-mounted solar projects larger than 5 MWAC) achieved commercial operations in 2019, bringing cumulative capacity to 29 GWAC. Projects are spread across all 10 regions that we track, though more heavily concentrated in the sunniest regions. The median installed cost of projects that came online in 2019 fell to $\$$1.4/WAC ($\$$1.2/WDC), down 20% from 2018 and down by more than 70% from 2010. 77% of projects and 88% of capacity added in 2019 used single-axis tracking. Average capacity factors range from 17% in the least-sunny regions to 30% where it is sunniest. Single-axis tracking adds roughly five percentage points to capacity factor in the regions with the strongest solar resource. Nationwide average levelized power purchase agreement (PPA) prices fell to $\$$24/MWh in 2019, down 17% from 2018 and more than 80% since 2010. Thirty-nine projects (totaling 4.2 GWAC) in our PPA price sample include battery storage (totaling 2.3 GWAC and 9.5 GWh). In the “lower 48” states, a number of these recent PV+battery PPAs have been inked in the mid-$\$$20/MWh range (levelized in 2019 dollars).Not including the 30% investment tax credit (ITC), the median levelized cost of energy (LCOE) from utility-scale PV has declined by 85% since 2010, to $\$$40/MWh in 2019. When the 30% ITC is included within the LCOE calculation, the median LCOE closely tracks the median PPA price over time. In higher-penetration markets like CAISO, the value of solar has been declining, but falling PPA prices have largely kept pace, preserving solar's net value. At the end of 2019, there were nearly 370 GW of solar in interconnection queues from all 7 ISOs and 30 additional utilities across the country. Nearly a third of this proposed solar capacity is paired with battery storage.

14 SOLAR ENERGY↗
DOE OSTI2021

solar-data-tools

Tools for performing common tasks on solar PV data signals. These tasks include finding clear days in a data set, common data transforms, and fixing time stamp issues. These tools are designed to be automatic and require little if any input from the user. Libraries are included to help with data IO and plotting as well.

Meyers, Bennet↗
DOE OSTI2020

Adversarial super-resolution of climatological wind and solar data

Accurate and high-resolution data reflecting different climate scenarios are vital for policy makers when deciding on the development of future energy resources, electrical infrastructure, transportation networks, agriculture, and many other societally important systems. However, state-of-the-art long-term global climate simulations are unable to resolve the spatiotemporal characteristics necessary for resource assessment or operational planning. We introduce an adversarial deep learning approach to super resolve wind velocity and solar irradiance outputs from global climate models to scales sufficient for renewable energy resource assessment. Using adversarial training to improve the physical and perceptual performance of our networks, we demonstrate up to a 50 × resolution enhancement of wind and solar data. In validation studies, the inferred fields are robust to input noise, possess the correct small-scale properties of atmospheric turbulent flow and solar irradiance, and retain consistency at large scales with coarse data. An additional advantage of our fully convolutional architecture is that it allows for training on small domains and evaluation on arbitrarily-sized inputs, including global scale. We conclude with a super-resolution study of renewable energy resources based on climate scenario data from the Intergovernmental Panel on Climate Change’s Fifth Assessment Report.

14 SOLAR ENERGY↗
DOE OSTI2020

India Solar Resource Data: National Solar Radiation Database (NSRDB)

The National Solar Radiation Database (NSRDB) provides high-resolution solar radiation data for long periods of time, which allows user to have a clear understanding of the spatial and temporal correlation of the data to determine the true impact of solar variability on the grid. This fact sheet specifically outlines solar resource data for India included in NSRDB.

energy systems↗