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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
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↗
DOE OSTI2022

Physiochemical Machine Learning Models Predict Operational Lifetimes of CH3NH3PbI3 Perovskite Solar Cells

Halide perovskites are promising photovoltaic (PV) materials with the potential to lower the cost of electricity and greatly expand the penetration of PV if they can demonstrate long-term stability under illumination in the presence of moisture and oxygen. The solar cell service lifetime as quantified by the T80 (the time required for the power conversion efficiency to drop to 80% of its starting value) is a useful metric to assess stability. The T80 for utility, commercial, or residential PV systems needs to be several decades in order to yield low-cost electricity, and thus it is not practical to directly measure the T80. It would be useful if T80 could be predicted from the initial dynamics of a solar cell’s performance, but until now no models have been developed to forecast T80. In this work, we report the development of machine learning models to predict T80 of ITO/NiOx/CH3NH3PbI3/C60/BCP/Ag solar cells operating at maximum power point under 1-sun equivalent photon flux in air at varying temperatures and relative humidities. Efficiency losses are driven by short-circuit current and fill factor, indicating that chemical decomposition of the perovskite is a major contributor to degradation. Spatial patterns evident from in situ dark field optical microscopy suggest that the electric field gradient at device edges plays a significant role in perovskite decomposition, along with photochemical reactions with O2 and H2O. Models are trained using a menu of features from three distinct categories: (i) features based on measurements of the initial rates of change of device parameters, (ii) features based on the ambient conditions during operation (temperature, & partial pressure of H2O), and (iii) features based on underlying physics and chemistry. We show that a theory-based physiochemical feature derived from a model of the chemical reaction kinetics of the rate of degradation of the CH3NH3PbI3 is particularly valuable for prediction. This physiochemical feature was selected as the first or second most dominant feature in the best performing models. With a dataset consisting of 45 accelerated degradation experiments with T80 that range over a factor of 30, the model predicts T80 with an accuracy of about 40% (|predicted T80 - observed T80| / observed T80) on samples not used in training. This hybrid ML approach should be effective when applied to other compositions, device architectures, and advanced packaging schemes.

14 SOLAR ENERGY↗
DOE OSTI2022

Physiochemical Machine Learning Models Predict Operational Lifetimes of CH3NH3PbI3 Perovskite Solar Cells

Halide perovskites are promising photovoltaic (PV) materials with the potential to lower the cost of electricity and greatly expand the penetration of PV if they can demonstrate long-term stability under illumination in the presence of moisture and oxygen. The solar cell service lifetime as quantified by the T80 (the time required for the power conversion efficiency to drop to 80% of its starting value) is a useful metric to assess stability. The T80 for utility, commercial, or residential PV systems needs to be several decades in order to yield low-cost electricity, and thus it is not practical to directly measure the T80. It would be useful if T80 could be predicted from the initial dynamics of a solar cell’s performance, but until now no models have been developed to forecast T80. In this work, we report the development of machine learning models to predict T80 of ITO/NiOx/CH3NH3PbI3/C60/BCP/Ag solar cells operating at maximum power point under 1-sun equivalent photon flux in air at varying temperatures and relative humidities. Efficiency losses are driven by short-circuit current and fill factor, indicating that chemical decomposition of the perovskite is a major contributor to degradation. Spatial patterns evident from in situ dark field optical microscopy suggest that the electric field gradient at device edges plays a significant role in perovskite decomposition, along with photochemical reactions with O2 and H2O. Models are trained using a menu of features from three distinct categories: (i) features based on measurements of the initial rates of change of device parameters, (ii) features based on the ambient conditions during operation (temperature, & partial pressure of H2O), and (iii) features based on underlying physics and chemistry. We show that a theory-based physiochemical feature derived from a model of the chemical reaction kinetics of the rate of degradation of the CH3NH3PbI3 is particularly valuable for prediction. This physiochemical feature was selected as the first or second most dominant feature in the best performing models. With a dataset consisting of 45 accelerated degradation experiments with T80 that range over a factor of 30, the model predicts T80 with an accuracy of about 40% (|predicted T80 - observed T80| / observed T80) on samples not used in training. This hybrid ML approach should be effective when applied to other compositions, device architectures, and advanced packaging schemes.

14 SOLAR ENERGY↗
DOE OSTI2022

Posterior Regularized Bayesian Neural Network

Traditional NNs often lack the ability for uncertainty quantification. Bayesian NNs(BNNs) could help measure the confidence level by using distributions in NNs modeling. Besides, knowledge is commonly available and could improve the performance of BNNs if it can be properly incorporated. In this work, we propose a novel Posterior-Regularized BNN(PR-BNN) model by incorporating soft and hard constraints as a posterior regularization term. We also propose an augmented Lagrangian method and stochastic optimization algorithm for efficient updating via Monte Carlo sampling. The simulations and case studies for solar PV plants have shown the performance improvement of the proposed model over traditional BNNs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Presenting a Model to Predict Changing Snow Albedo for Improving Photovoltaic Performance Simulation

As photovoltaic (PV) deployment increases worldwide, PV systems are being installed more frequently in locations that experience snow cover. The higher albedo of snow, relative to the ground, increases the performance of PV systems in northern and high-altitude locations by reflecting more light onto the PV modules. Accurate modeling of the snow’s albedo can improve estimates of PV system production. Typical modeling of snow albedo uses a simple two-value model that sets the albedo high when snow is present, and low when snow is not present. However, snow albedo changes over time as snow settles and melts and a binary model does not account for transitional changes, which can be significant. Here, we present and validate a model for estimating snow albedo as it changes over time. The model is simple enough to only require daily snow depth and hourly average temperature data, but can be improved through the addition of site-specific factors, when available. We validate this model to quantify its ability to more accurately predict snow albedo and compare the model’s performance against satellite imagery-based methods for obtaining historical albedo data. In addition, we perform modeling using the System Advisor Model (SAM) to show the impact of changes in albedo on energy modeling for PV systems. Overall, our albedo model has a significantly improved ability to predict the solar insolation on PV modules in real time, especially on bifacial PV modules where reflected irradiance plays a larger role in energy production.

Pike, Christopher (ORCID:0000000155888033)↗
DOE OSTI2025

Impact of the Latest 22 Ne+ α Reaction Rates on Nucleosynthesis in Massive Stars and Galactic Chemical Evolution

In massive stars (initial mass of ≳9 M ⊙ ), the weak s- (slow neutron capture) process produces elements between Fe and Zr, enriching the Galaxy with these elements through core-collapse supernova explosions. The weak s-process nucleosynthesis is driven by neutrons produced in the 22 Ne(α, n) 25 Mg reaction during convective He core and C shell burning. The yields of heavy elements thus depend on the 22 Ne(α, n) 25 Mg and the competitive 22 Ne(α, γ) 26 Mg reaction rates, which are dominated by several narrow-resonance reactions. While the accuracy of these rates has been under debate for decades, recent experimental efforts, including ours, drastically reduced these uncertainties. In this work, we use a set of 280 massive star nucleosynthesis models calculated using different 22 Ne(α, n) 25 Mg and 22 Ne(α, γ) 26 Mg rates and a galactic chemical evolution (GCE) study to probe their impact on the weak s-process elemental abundances in the Galaxy. The GCE was computed with the OMEGA+ code, using the new sets of stellar yields with different 22 Ne+α rates. From GCE, we find that these rates are causing up to 0.45 dex of variations in the [Cu/Fe], [Ga/Fe], and [Ge/Fe] ratios predicted at solar metallicity. The greatest impact on the stellar nucleosynthesis and GCE results derives from uncertainties in the (α, n) strength (ωγ (α,n) ) of the E x = 11.32 MeV resonance. We show that variations observed in the GCE calculations for weak s-process elements become negligibly smaller than dispersions found in observations once the ωγ (α,n) is accurately determined within the uncertainty of 10%–20% (typically reported experimental errors for the resonance) in future nuclear physics experiments.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1973

A possible solar electrograph

Some theories of solar flares predict strong electric fields during the pre-flare energy buildup. These might well be observable with existing techniques by measuring the linear polarization in spectral lines that are split due to the Stark effect.

Dravins, D.↗
NASA NTRS1974

The elemental abundance ratios of interstellar secondary and primary cosmic rays

We report new observations of abundances in the charge range (Z) between 2 and 10, which were obtained with a dE/dx-Cerenkov detector launched into a polar orbit on OGO-6 as part of the Caltech Solar and Galactic Cosmic Ray Experiment. Integral rigidity spectra of all the elements observed have shapes similar to that of the helium spectrum in the rigidity range of 2 to 14 GV, approaching a power law with exponent -1.6 above 8 GV. Calculations of interstellar propagation assuming a steady-state model and including the presence of interstellar helium and the effects of solar modulation predict a variation with rigidity of ratios such as Be-O and B/O, which is not observed. The data can be explained by assuming a rigidity-dependent confinement of cosmic rays within the Galaxy.

Brown, J. W.↗
NASA NTRS1980

The dependence of coronal hole size on large scale magnetic field strength

The importance of mathematical models of the coronal structure for studies of coronal energetics, to simulate global flows of the solar wind, and to obtain reliable solar terrestrial predictions is discussed. Previous coronal models, including an example of a coronal MHD flow model, are reviewed. The development of a coronal model which is a logical extension of earlier models and which allows a closer relationship to the photospheric magnetic field as it is observed daily is described. The calculations are outlined. The assumptions of the model are: axisymmetric flow with no rotation, resulting in two dimensional flow in a meridional plane; zero viscosity and infinite electrical conductivity; polytropic, single fluid flow; and no momentum addition.

Suess, S. T.↗
NASA NTRS1979

Short term magnetospheric particle variations (1 minute T 1 day), appendix 1

A schematic representation of the generation and propagation processes for energetic particles of concern in solar terrestrial predictions is given. Particle precipitation at low, mid, and high altitudes is discussed with emphasis on prediction techniques. Methods given for testing of such techniques include traditional collaborations, enhanced collaborations, simulated prediction schemes, and field tests.

Higbie, P. R.↗
NASA NTRS1987

Space station control moment gyro control

The potential large center-of-pressure to center-of-gravity offset of the space station makes the short term, within an orbit, variations in density of primary importance. The large range of uncertainty in the prediction of solar activity will penalize the design, developments, and operation of the space station.

Bordano, Aldo↗
NASA NTRS1988

Using geomagnetic indices to forecast the next sunspot maximum

The Babcock (1961) 'solar dynamo' model and known interactions of the interplanetary magnetic field with the earth's magnetosophere are used to explain the relations found between geomagnetic indices at solar minimum and the sunspot number at the following solar maximum. The work of Kane (1987) is augmented by including recent smoothed aa and Ap indices. A smoothed maximum sunspot number of 163 + or - 40 to peak in October 1990 + or - 9 months for solar cycle 22 is predicted. This value is close to the Schatten and Sofia (1987) value of 170 + or - 25, predicted using more direct solar indicators.

Gonzalez, Guillermo↗