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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Solar, Wind, and Load Forecasting Dataset for MISO, NYISO, and SPP Balancing Areas

The Performance-based Energy Resource Feedback, Optimization, and Risk Management (PERFORM) program is an initiative intended to foster "a fundamental shift in grid management rooted in an understanding of asset risk and system risk" (ARPA-E 2020). Launched by the Advanced Research Projects Agency-Energy (ARPA-E), the program supports efforts to incorporate uncertainty in electric power decision making. In support of PERFORM, the National Renewable Energy Laboratory (NREL) has produced a set of time-coincident forecasts of solar, wind, and load profiles. As part of Phase I of the PERFORM effort, NREL created a dataset that consists of one year of time-coincident load, wind, and solar actuals and probabilistic forecasts based on data from the Electric Reliability Council of Texas (ERCOT) (Bryce et al. 2023). In Phase II, NREL developed similar datasets for three other U.S. Independent System Operators (ISO): the Midcontinent Independent System Operator (MISO), the New York Independent System Operator (NYISO), and the Southwest Power Pool (SPP).

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Temporal change in chromatin accessibility predicts regulators of nodulation in Medicago truncatula

Symbiotic associations between bacteria and leguminous plants lead to the formation of root nodules that fix nitrogen needed for sustainable agricultural systems. Symbiosis triggers extensive genome and transcriptome remodeling in the plant, yet an integrated understanding of the extent of chromatin changes and transcriptional networks that functionally regulate gene expression associated with symbiosis remains poorly understood. In particular, analyses of early temporal events driving this symbiosis have only captured correlative relationships between regulators and targets at mRNA level. Here, we characterize changes in transcriptome and chromatin accessibility in the model legume Medicago truncatula, in response to rhizobial signals that trigger the formation of root nodules. We profiled the temporal chromatin accessibility (ATAC-seq) and transcriptome (RNA-seq) dynamics of M. truncatula roots treated with bacterial small molecules called lipo-chitooligosaccharides that trigger host symbiotic pathways of nodule development. Using a novel approach, dynamic regulatory module networks, we integrated ATAC-seq and RNA-seq time courses to predict cis-regulatory elements and transcription factors that most significantly contribute to transcriptomic changes associated with symbiosis. Regulators involved in auxin (IAA4-5, SHY2), ethylene (EIN3, ERF1), and abscisic acid (ABI5) hormone response, as well as histone and DNA methylation (IBM1), emerged among those most predictive of transcriptome dynamics. RNAi-based knockdown of EIN3 and ERF1 reduced nodule number in M. truncatula validating the role of these predicted regulators in symbiosis between legumes and rhizobia. Our transcriptomic and chromatin accessibility datasets provide a valuable resource to understand the gene regulatory programs controlling the early stages of the dynamic process of symbiosis. The regulators identified provide potential targets for future experimental validation, and the engineering of nodulation in species is unable to establish that symbiosis naturally.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Autoregressive Neural Network for Simulating Open Quantum Systems via a Probabilistic Formulation

The theory of open quantum systems lays the foundation for a substantial part of modern research in quantum science and engineering. Rooted in the dimensionality of their extended Hilbert spaces, the high computational complexity of simulating open quantum systems calls for the development of strategies to approximate their dynamics. In this Letter, we present an approach for tackling open quantum system dynamics. Using an exact probabilistic formulation of quantum physics based on positive operator-valued measure, we compactly represent quantum states with autoregressive neural networks; such networks bring significant algorithmic flexibility due to efficient exact sampling and tractable density. We further introduce the concept of string states to partially restore the symmetry of the autoregressive neural network and improve the description of local correlations. Efficient algorithms have been developed to simulate the dynamics of the Liouvillian superoperator using a forward-backward trapezoid method and find the steady state via a variational formulation. Our approach is benchmarked on prototypical one-dimensional and two-dimensional systems, finding results which closely track the exact solution and achieve higher accuracy than alternative approaches based on using Markov chain Monte Carlo method to sample restricted Boltzmann machines. Our Letter provides general methods for understanding quantum dynamics in various contexts, as well as techniques for solving high-dimensional probabilistic differential equations in classical setups.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Wideband Impedance Passivation of MMCs for Suppressing Harmonic Oscillations

Harmonic instability and associated oscillation events have become one of the main concerns in MMC-based HVdc systems. These oscillations can appear in the range from a few hundred hertz to several kilohertz, and the root cause is identified as delay-induced negative damping of MMC impedance appearing at the system resonance frequency. Here, this paper introduces a wideband impedance reshaping method through MMC control to eliminate such negative damping for both grid-following (GFL) and grid-forming (GFM) MMCs, which makes the MMC impedance completely passive from the second harmonic frequency upward, thus preventing all harmonic oscillations. First, simplified impedance models of MMCs are derived for the harmonic stability analysis and the impedance reshaping control design. Next, a passivity-based impedance reshaping method is presented, as well as practical considerations for its implementation. In addition, to maintain the MMC's disturbance ride-through capability, an adaptive activation scheme is developed, which enables the wideband impedance reshaping control only in the presence of harmonic oscillation events. The effectiveness of the proposed method is validated by frequency domain analysis and by electromagnetic transient (EMT) simulations of two typical MMC-based power systems.

17 WIND ENERGY↗
DOE OSTI2025

Non-Destructive, Three-Dimensional Imaging of Processes in the Rhizosphere Utilizing High Energy Photons

Soil structure, which can be described as the aggregation and distribution of pore spaces, regulates carbon, nutrient, and water cycling across the Earth system. Yet the inability to make quantitative, dynamic, in situ measurements of soil structure and rhizosphere carbon flow has prevented meaningful incorporation of soil structural processes into Earth System Models (ESMs). The key limitations are: (i) Lack of scale integration between micron-scale soil structure and ecosystem-scale models, (ii) Poor functional linkage between soil structural properties and biogeochemical processes, and (iii) Absence of dynamic 3D measurements of rhizosphere structural changes and carbon transformations. To address these challenges, we developed a new integrated positron emission tomography (PET) - microcomputed tomography (CT) imaging platform that enables the first 4D (spatial 3D and time), non-invasive, quantitative imaging of carbon allocation and rhizosphere structural dynamics in living plants and intact soils. The system combines: • Rhizo-PET (R-PET): a high-resolution positron emission tomography scanner optimized for 11 CO 2 tracing in plant roots. • a-Se micro-CT: a high-contrast, high-spatial resolution CT system based on amorphous selenium (a-Se) direct conversion technology, enabling micron-scale visualization of soil structure and root–soil interfaces. Together, these advances allow us to quantify how carbon exudates move, transform, and stabilize within the rhizosphere, directly informing missing processes in BER-relevant carbon cycle models.

42 ENGINEERING↗
DOE OSTI2025

Direct root contact among neighboring plants influences activity of soil extracellular enzymes

Composition and diversity of vegetation systems can influence soil microbial activity and extracellular enzyme (EE) dynamics, which are crucial for soil carbon (C) accrual and nutrient cycling. Yet, the impact of plant interactions and competition on EE activities remains a notable knowledge gap. This study examines how direct root contact and neighboring plant identity affect the activity and spatial distribution of four key soil EEs: β-glucosidase (BGlu), chitinase, acid phosphatase (AcidP), and alkaline phosphatase (AlkP). Using three-compartment rhizoboxes with switchgrass (Panicum virgatum L.) grown alongside bush clover (Lespedeza capitata Michx.), and black-eyed Susan (Rudbeckia hirta L.), we assessed enzyme activities using zymography under conditions that either allowed or restricted direct root contact by root barriers. Results show that root proliferation and species interactions significantly influenced EE activity. While BGlu and AcidP activities were strongly correlated with root biomass, AlkP activity was consistently higher in the absence of root barriers, indicating a pronounced microbial response to plant interactions via direct/close root contacts. Additionally, soil phosphorus availability modulated enzyme activity, with higher phosphatase activities in low-P soils. Furthermore, these findings highlight the importance of root-root interactions and plant species composition in shaping soil biochemical processes.

enyzme activity↗
DOE OSTI2025

Automated Programmable Logic Controller Memory Forensics Using RGB Image Analysis and Deep Learning

The introduction of Industry 4.0 and Internet-based technologies has enhanced industrial control system operations but have inadvertently increased their vulnerabilities to cyber attacks. When an industrial control system is compromised, security analysts need to identify the root cause quickly to start the recovery process and develop mitigation strategies. Memory forensics is critical in the incident analysis process to ascertain what occurred. Approaches for analyzing the persistent memory in industrial control devices are limited and almost nonexistent for volatile memory. This chapter proposes an automated methodology for programmable logic controller memory dump analysis using computer vision and deep learning techniques. The methodology converts the sequences of bytes in a programmable logic controller memory dump to red-green-blue pixels and employs a deep learning model that learns the underlying patterns and features of pre-labeled forensic artifacts in images and segments them into distinct regions. The trained model is employed to automatically segment new memory images and identify forensic artifacts. Evaluation of the methodology on a Schneider Electric Modicon M221 programmable logic controller under code injection and code modification attacks demonstrates its ability to detect attack artifacts in memory dumps.

Asmar Awad, Rima [ORNL] (ORCID:0000000233407742)↗
DOE OSTI2020

Discerning the thermodynamic feasibility of the spontaneous coexistence of multiple functional vegetation groups

Can the Second Law of Thermodynamics explain why ecosystems naturally organize into a complex structure composed of multiple vegetation species and functional groups? Ecosystem structure, which refers to the number and type of plant functional groups, is the result of self-organization, or the spontaneous emergence of order from random fluctuations. By considering ecosystems as open thermodynamic systems, we model and study these fluctuations of throughput signatures on short timescales to determine the drivers and characteristics of ecosystem structure. This diagnostic approach allows us to use fluxes of energy and entropy to calculate an ecosystem’s estimated work and understand the thermodynamic behavior of the system. We use a multi-layer canopy-root-soil model to calculate the energy and entropy fluxes of different scenarios for field sites across various climates. At each site, scenarios comprised of native individual plant functional groups and a coexisting multi-group composition scenario including all functional groups observed at the site are compared. Ecosystem-scale calculations demonstrate that entropy fluxes and work efficiency—the work performed for the amount of radiation entering the ecosystem—are greatest in the multi-group scenario when its leaf area is significantly larger than each of its individual functional groups. Thus, we conclude that ecosystems self-organize towards the vegetation structure with the greatest outgoing entropy flux and work efficiency, resulting in the coexistence of multiple functional groups and performing the maximum amount of work within the constraints of locally available energy, water, and nutrients.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

A combined SECM and electrochemical AFM approach to probe interfacial processes affecting molecular reactivity at redox flow battery electrodes

Redox flow batteries are attractive technologies for grid energy storage since they use solutions of redox-active molecules that enable a superior scalability and the decoupling of power and energy density. However, the reaction mechanisms of the redox active components at RFB electrodes are complex, and there is currently a pressing need to understand how interfacial processes impact the kinetics and operational reversibility of RFB systems. Here, we developed a combined electrochemical imaging methodology rooted in scanning electrochemical microscopy (SECM) and atomic force microscopy (AFM) for exploring the impact of electrode structure and conditioning on the electron transfer properties of model redox-active dialkoxybenzene derivatives, 2,5-di-tert-butyl-1,4-bis(2-methoxyethoxy)benzene (C1) and 2,3-dimethyl-1,4-dialkoxybenzene (C7). Using AFM and secondary-ion mass spectrometry (SIMS), we observed the formation of interfacial films with distinct mechanical properties compared to those of cleaved graphitic surfaces, and exclusively during reduction of electrogenerated radical cations. These films had an impact on the median rate and distribution of the electron transfer rate constant at the basal plane of multilayer and single layer graphene electrodes, displaying kinetically-limited values that did not yield the activation expected per the Butler–Volmer model with a transfer coefficient ~0.5. These changes were dependent on redoxmer structure: furthermore, SECM showed strong attenuation of C7 kinetics by a surface layer on MLG and SLG, while C1 kinetics were only affected by SLG. SECM and AFM results together show that these limiting films operate exclusively on the basal plane of graphite, with the edge plane showing a relative insensitivity to cycling and operation potential. Ultimately, this integrated electrochemical imaging methodology creates new opportunities to understand the unique role of interfacial processes on the heterogeneous reactivity of redoxmers at electrodes for RFBs, with a future role in elucidating phenomena at high active concentrations and spatiotemporal variations in electrode dynamics.

25 ENERGY STORAGE↗
DOE OSTI2023

GWAS identifies candidate genes controlling adventitious rooting in Populus trichocarpa

Adventitious rooting (AR) is critical to the propagation, breeding, and genetic engineering of trees. The capacity for plants to undergo this process is highly heritable and of a polygenic nature; however, the basis of its genetic variation is largely uncharacterized. To identify genetic regulators of AR, we performed a genome-wide association study (GWAS) using 1148 genotypes of Populus trichocarpa. GWASs are often limited by the abilities of researchers to collect precise phenotype data on a high-throughput scale; to help overcome this limitation, we developed a computer vision system to measure an array of traits related to adventitious root development in poplar, including temporal measures of lateral and basal root length and area. GWAS was performed using multiple methods and significance thresholds to handle non-normal phenotype statistics and to gain statistical power. These analyses yielded a total of 277 unique associations, suggesting that genes that control rooting include regulators of hormone signaling, cell division and structure, reactive oxygen species signaling, and other processes with known roles in root development. Numerous genes with uncharacterized functions and/or cryptic roles were also identified. These candidates provide targets for functional analysis, including physiological and epistatic analyses, to better characterize the complex polygenic regulation of AR.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Daily evapotranspiration changes during heatwaves at 32 NEON sites, 2019-2021

This dataset provides partitioned evapotranspiration (ET, the combined loss of water from soil and plant surfaces) anomalies during heatwave events—soil evaporation (E) and transpiration (T)—for 268 heatwave events across 32 National Ecological Observatory Network (NEON) flux sites in the contiguous United States from 2019–2021. Using an ensemble of four high-frequency turbulence methods (Flux-variance Similarity, Conditional Eddy Covariance [CEC], CEC with Water-Use Efficiency, and Conditional Eddy Accumulation; see Zahn and Bou-Zeid 2024), half-hourly transpiration-to-evapotranspiration (T/ET) ratios were derived from 20 hertz (Hz, cycles per second) eddy covariance measurements of carbon dioxide (CO₂) and water vapor (H₂O) concentrations. The dataset spans six vegetation types including evergreen and deciduous forests, grasslands, cultivated crops, shrublands, and emergent herbaceous wetlands. Data Package Contents: The dataset includes a single CSV (comma-separated values) file containing daily anomalies (deviations from baseline conditions) for transpiration (Delta_T), evaporation (Delta_E), total evapotranspiration (Delta_ET), and T/ET ratio (Delta_T_ET) during each day of identified heatwave events. The file also includes site codes, dates, heatwave event identifiers, and day-of-heatwave indicators. The CSV file can be opened with spreadsheet software (Microsoft Excel, Google Sheets) or programming environments (Python, R, MATLAB). This resource enables researchers to investigate ecosystem-specific responses to thermal extremes, validate land surface model partitioning of ET fluxes, and examine feedbacks between water cycling and surface energy balance during heatwaves. The dataset is particularly valuable for studies linking vegetation hydraulic strategies to climate resilience, as it captures the divergent responses of shallow-rooted versus deep-rooted ecosystems. Potential applications include improving drought early warning systems, informing irrigation management strategies, and advancing our mechanistic understanding of land-atmosphere interactions under extreme heat conditions.

Day of Heatwave↗
DOE OSTI2024

Beyond Binary: Automated PLC Memory Forensics through RGB Image Analysis and Deep Learning

The introduction of Industry 4.0 and the evolution of industrial control systems (ICS) to adopt Internet-based technologies enhanced productivity, but have inadvertently increased their vulnerability to cyber-based malicious attacks. When an ICS system is compromised, security analysts need to identify the root cause quickly to start the recovery process and develop mitigation strategies to safeguard against future instances. Memory forensics is critical in the analysis process to ascertain what occurred. To date, approaches to analyze the persistent memory in ICS devices are limited, and almost nonexistent for volatile memory. This paper proposes an automated methodology, COMA, for PLC memory dump analysis using computer vision and deep learning techniques. Specifically, COMA converts the sequences of bytes in a PLC memory dump to RGB pixels and creates a deep learning model that learns the underlying patterns and features of pre-labeled forensic artifacts in images and segments them into distinct regions. COMA then uses the trained model to automatically segment new memory images and extract forensic artifacts. We evaluate COMA on a Schneider Electric Modicon M221 PLC involving two cyber-based attack scenarios: (i) code injection and (ii) code modification. The empirical results show that COMA can successfully detect attack artifacts in memory dumps in both scenarios.

Asmar Awad, Rima↗
DOE OSTI2020

The rhizosphere and cropping system, but not arbuscular mycorrhizae, affect ammonia oxidizing archaea and bacteria abundances in two agricultural soils

Arbuscular mycorrhizal fungi (AMF) form symbioses with roots that can enhance plant nutrition. While AMF have been shown to have a role in soil nitrogen (N) cycling, it is unclear whether AMF affect N cycling microbes such as ammonia-oxidizing bacteria (AOB) and archaea (AOA), which convert ammonium into nitrite in the first step of nitrification. In this study, we examined the effects of AMF on AOA and AOB abundances within the corn rhizosphere and bulk soil of conventional (corn-soybean rotation with inorganic fertilizer) and diversified (cornsoybean- oats/alfalfa-oats rotation with composted manure) systems. We hypothesized that AMF would decrease AOA and AOB abundances in a cropping-system dependent manner, possibly due to competition for ammonium. We grew corn deficient or proficient in AMF symbiosis in microcosms for 10 weeks. At the end of the experiment, both soils planted with the AMF-proficient corn genotype had higher ammonium and lower nitrate pool sizes compared to the same soils planted with the AMF-deficient corn genotype. Likewise, total plant N was higher in the AMF-proficient genotype compared to the AMF-deficient genotype. Despite changes in soil inorganic N pool sizes, AOA and AOB abundances were unaffected by plant AMF-proficiency. Instead, AOA abundance was greater in the rhizosphere than in the bulk soil regardless of cropping system, and AOB abundance was greater in the conventional than the diversified cropping system soil regardless of proximity to the root. These data indicate that 1) AMF did not affect AOA or AOB abundance in these N-rich soils but other factors such as root proximity and inorganic fertilization did and 2) AOA and AOB have differing ecological niches within rhizosphere and bulk soil that should be considered when managing for nitrogen losses.

Wattenburger, Cassandra↗
DOE OSTI2025

Rational Modulation of Plant Root Development Using Engineered Cytokinin Regulators

Achieving precise control over quantitative developmental phenotypes is a key objective in plant biology. Recent advances in synthetic biology have enabled tools to reprogram entire developmental pathways; however, the complexity of designing synthetic genetic programs and the inherent interactions between various signaling processes remains a critical challenge. Here, we leverage Type-B response regulators to modulate the expression of genes involved in cytokinin-dependent growth and development processes. We rationally engineered these regulators to modulate their transcriptional activity (i.e., repression or activation) and potency while reducing their sensitivity to cytokinin. By localizing the expression of these engineered transcription factors using tissue-specific promoters, we can predictably tune cytokinin-regulated traits. As a proof of principle, we deployed this synthetic system in Arabidopsis thaliana to either decrease or increase the number of lateral roots. The simplicity and modularity of our approach makes it an ideal system for controlling other developmental phenotypes of agronomic interest in plants.

Cell signaling↗
DOE OSTI2021

Disentangling the Effects of Vapor Pressure Deficit and Soil Water Availability on Canopy Conductance in a Seasonal Tropical Forest During the 2015 El Niño Drought

In land models used in Earth system models, vegetation water stress is heuristically related to soil water matric potential, and root water uptake in soil layers is simply proportional to root density to meet the transpiration demand. The efficiency of roots utilizing deeper soil water under water stress cannot be well represented, but it is of critical importance for transpiration regulation and carbon flux. To improve modeling of tropical forest response to drought, a plant hydrodynamic scheme (HYDRO), adapted from a trait-based model in the Functionally Assembled Terrestrial Ecosystem Simulator (FATES), has been implemented in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) to represent the hydraulic dynamics and root water uptake strategy. The adapted model conforms to the big-leaf photosynthesis model in ELM. Vegetation water stress in the model is a function of prognostic leaf water potential and is used to regulate stomatal conductance. Point simulations were performed at two tropical forest sites in the Amazon and Panama. Model sensitivity to the numerical solver, plant hydraulic traits, soil properties, and atmospheric forcing has been evaluated. Our results showed that, even though water stress factors calculated at our study sites using HYDRO are comparable to that simulated by the heuristic water stress model, soil moisture prediction is improved with soil water redistributions through roots. Simulated results using HYDRO is not as sensitive to plant hydraulic traits measured for different tree species as to the soil properties and atmospheric forcing. Simplified numerical solver may cause inaccuracy as to where roots uptake water. This study suggests that plant hydrodynamic representation is critical for improved simulations of soil moisture under water stresses.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Stabilize High-IBR Power Systems with Grid-Forming Inverters

The increasing share of IBRs challenges the stable operation of our power systems. Leveraging an oscillation event in Kaua`i Island as an example, we showcase the root cause of this event and highlight the potential of grid-forming inverters to reduce the stability risks in high-IBR power systems.

grid-forming inverters↗
DOE OSTI2022

Tree hydrodynamic modelling of the soil–plant–atmosphere continuum using FETCH3

Modelling the water transport along the soil–plant–atmosphere continuum is fundamental to estimating and predicting transpiration fluxes. A Finite-difference Ecosystem-scale Tree Crown Hydrodynamics model (FETCH3) for the water fluxes across the soil–plant–atmosphere continuum is presented here. The model combines the water transport pathways into one vertical dimension, and assumes that the water flow through the soil, roots, and above-ground xylem can be approximated as flow in porous media. This results in a system of three partial differential equations, resembling the Richardson–Richards equation, describing the transport of water through the plant system and with additional terms representing sinks and sources for the transfer of water from the soil to the roots and from the leaves to the atmosphere. The numerical scheme, developed in Python 3, was tested against exact analytical solutions for steady state and transient conditions using simplified but realistic model parameterizations. The model was also used to simulate a previously published case study, where observed transpiration rates were available, to evaluate model performance. With the same model setup as the published case study, FETCH3 results were in agreement with observations. Through a rigorous coupling of soil, root xylem, and stem xylem, FETCH3 can account for variable water capacitance, while conserving mass and the continuity of the water potential between these three layers. FETCH3 provides a ready-to-use open access numerical model for the simulation of water fluxes across the soil–plant–atmosphere continuum.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Artificial Intelligence and Machine Learning Applications in Modern Power Systems

Machine learning (ML) and artificial intelligence (AI) algorithms offer valuable tools for the analysis and interpretation of large datasets. These tools have the capability to uncover insights that may not be readily apparent within these datasets. In recent years, the integration of ML and AI has become increasingly prevalent in various applications within the power system domain. One of the earliest instances of machine learning in power systems can be traced back to demand forecasting, where artificial neural networks were employed for short-term load forecasting. In contemporary power systems, an abundance of high-resolution geospatial and temporal data is generated at various time intervals, ranging from sub-seconds (Phasor Measurement Units or PMUs) to seconds (Supervisory Control and Data Acquisition or SCADA), minutes (Process Information or PI), and extending to days, months, and years. These datasets contain valuable information concerning system reliability and performance. This information holds the potential to offer critical insights into system operations, as well as solutions for predicting and mitigating contingencies to prevent cascading outages. Despite the immense power of machine learning tools, system operators, planners, and utilities often exhibit hesitancy in fully embracing AI-enabled system operations and planning. This cautious approach persists, even as numerous diverse applications of machine learning continue to emerge in the realm of power systems. In this chapter, our focus will delve deep into ML and AI applications tailored for power systems. These applications aim to furnish system operators with enhanced situational awareness and augment their decision-making capabilities, especially during challenging operating conditions. Specific areas of interest encompass root cause analyses of electricity market datasets and the strategic selection of representative samples from vast power system databases for training ML/AI models. Finally, the chapter will conclude with a short discussion on the future of ML/AI in power systems and possible directions that the industry is moving towards.

power system applications, machine learning (ML), ↗