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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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Impact of duration and missing data on the long-term photovoltaic degradation rate estimation
Accurate quantification of photovoltaic (PV) system degradation rate (R D ) is essential for lifetime yield predictions. Although R D is a critical parameter, its estimation lacks a standardized methodology that can be applied on outdoor field data. The purpose of this paper is to investigate the impact of time period duration and missing data on R D by analyzing the performance of different techniques applied to synthetic PV system data at different linear R D patterns and known noise conditions. The analysis includes the application of different techniques to a 10-year synthetic dataset of a crystalline Silicon PV system, with emulated degradation levels and imputed missing data. Here, the analysis demonstrated that the accuracy of ordinary least squares (OLS), year-on-year (YOY), autoregressive integrated moving average (ARIMA) and robust principal component analysis (RPCA) techniques is affected by the evaluation duration with all techniques converging to lower R D deviations over the 10-year evaluation, apart from RPCA at high degradation levels. Moreover, the estimated R D is strongly affected by the amount of missing data. Filtering out the corrupted data yielded more accurate R D results for all techniques. It is proven that the application of a change-point detection stage is necessary and guidelines for accurate R D estimation are provided.
Mapping bull kelp canopy in northern California using Landsat to enable long-term monitoring
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State-of-the-art short-term electricity market operation with solar generation: A review
With the accelerating penetration of solar energy in energy systems, market operations concerning the solar-associated intermittency are widely discussed. How to correctly model the solar generation in the market and solve the uncertainty-based operation problems call for solutions. Unlike other renewable resources, solar power can be more easily applied to the demand side through behind-the-meter installations in a distributed manner with significant future extensibility. The corresponding operational rules in the markets are largely being revised. Over the past two decades, with the development of deregulated power markets, numerous studies related to uncertainty-based market operations tend to transform the market from a deterministic and centralized framework to a stochastic and decentralized one. As the relevant literature is most recent, this paper reviews their contributions to electricity market operations with solar integration and underlines the research directions of this topic and ongoing industry practices. A thorough review of the electricity markets worldwide with solar energy is provided. A variety of proposed mathematical solutions to the problem are also addressed.
Unusual photophysical properties of a new tricyclic derivative of thiopurines in terms of potential applications
The thio analogues of purine bases have been found to possess notable biological and pharmacological capabilities and have an important role to play as anticancer and immunosuppressive drugs. In this work a new tricyclic analogue of guanosine containing sulfur was synthesized, in particular, DTEG (2',3',5'-tri-O-acetyl-6,9-dithioethanoguanosine). Although there is promise for thiopurine derivatives for biomedical applications, there are some liabilities in regard to their exposure to light. As a preliminary survey for such difficulties with DTEG, this work looks into spectral and photophysical processes of DTEG using time-resolved and steady-state optical excitation. In contrast to other thiopurines, which have long-lived triplets, DTEG is shown to have a short-lived triplet making it less dangerous for singlet-oxygen sensitization. Even in anaerobic solutions, its photoreactivity is negligible. These various unusual photochemical properties of DTEG are consistent with DTEG being very promising as an alternative drug to the currently used 6-thiopurines. DTEG also has some interesting photophysical behavior that is distinct from other thioketones. Although thioketones have an unusual fluorescence violating Kasha’s Rule and emitting from the second excited singlet state, DTEG does this also, but, in addition, it shows dual fluorescence by emitting from its first excited singlet as well. The assignments of the nature of these excited states are supported by DFT results. This theory and associated kinetic analysis show quantitatively that the dual fluorescence is, in part, tied to the relatively fast S 2 to S 1 internal conversion compared to other S 2 decays and, in part, tied to the relatively slow nonradiative decay of S 1 itself.
Influence of late-Holocene climate change on the solid-phase speciation and long-term stability of arsenic in sub-Arctic lake sediments
Abstract not provided
The effects of mixed-species root zones on the resistance of soil bacteria and fungi to long-term experimental and natural reductions in soil moisture
Mixed forest stands tend to be more resistant to drought than species-specific stands partially due to complementarity in root ecology and physiology. For this work, we asked whether complementary differences in the drought resistance of soil microbiomes might contribute to this phenomenon. We experimented on the effects of reduced soil moisture on bacterial and fungal community composition in species-specific (single species) and mixed-species root zones of Norway spruce and European beech forests in a 5-year-old throughfall-exclusion experiment and across seasonal (spring-summer-fall) and latitudinal moisture gradients. Bacteria were most responsive to changes in soil moisture, especially members of Rhizobiales, while fungi were largely unaffected, including ectomycorrhizal fungi (EMF). Community resistance was higher in spruce relative to beech root zones, corresponding with the proportions of drought-favored (more in spruce) and drought-sensitive bacterial taxa (more in beech). The spruce soil microbiome also exhibited greater resistance to seasonal changes between spring (wettest) and fall (driest). Mixed-species root zones contained a hybrid of beech- and spruce-associated microbiomes. Several bacterial populations exhibited either enhanced resistance or greater susceptibility to drought in mixed root zones. Overall, patterns in the relative abundances of soil bacteria closely tracked moisture in seasonal and latitudinal precipitation gradients and were more predictive of soil water content than other environmental variables. We conclude that complementary differences in the drought resistance of soil microbiomes can occur and the likeliest form of complementarity in mixed-root zones coincides with the enrichment of drought-tolerant bacteria associated with spruce and the sustenance of EMF by beech.
Long-term study on wastewater SARS-CoV-2 surveillance across United Arab Emirates
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Short-term exposure to air pollution and infant mortality: A systematic review and meta-analysis
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Impacts of long-term irrigation of municipally-treated wastewater to the soil microbial and nutrient properties
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Rare microbial taxa as the major drivers of ecosystem multifunctionality in long-term fertilized soils
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Resampling and data augmentation for short-term PV output prediction based on an imbalanced sky images dataset using convolutional neural networks
Integrating photovoltaics (PV) into electricity grids is challenged by potentially large fluctuations in power generation. In recent years, sky image-based PV output prediction using convolutional neural networks (CNNs) has emerged as a promising approach to forecasting fluctuations. A key challenge is imbalanced sky image datasets: because of the geography of solar PV system installations, sky image datasets are often rich in sunny condition data but deficient in cloudy condition data. This imbalance contrasts with the fact that model errors are dominated by cloudy condition performance. In this study, we attempt to remedy this by exploring the enrichment and augmentation of an imbalanced sky images dataset for two PV output prediction tasks: nowcasting (predicting concurrent PV output) and forecasting (predicting 15-minute-ahead future PV output). We empirically examine the efficacy of using different resampling and data augmentation approaches to create a rebalanced dataset for model development. A three-stage greedy search is used to determine the optimal resampling approach, data augmentation techniques and over-sampling rate. The results show that for the nowcast problem, resampling and data augmentation can effectively enhance the model performance, reducing overall root mean squared error (RMSE) by an average of 4%, or a 15 std. (standard deviation) of improvement compared to the variability of the baseline model. In contrast, the treatment RMSE for the forecast problem nearly always overlaps the baseline performance at the ± 2 std. level. The optimal resampling approach expands on the original dataset by over-sampling the minority cloudy data, with the best results from large over-sampling rate (e.g., 4 ~ 6 times over-sampling of cloudy images).
Quantifying the rear and front long-term spectral impact on bifacial photovoltaic modules
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Meta-analysis of animal studies applied to short-term inhalation exposure levels of hazardous chemicals
Not Available