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Earlier sources: Energy
Energy research and engineering concern how power is produced, stored, delivered, and consumed. Follow solar and nuclear power, fusion, geothermal systems, batteries, electricity grids, and changing power demand.
SYMTRI organizes observations from public sources into a living map. Connections reflect shared subject matter; they do not establish causation or endorse a source.
Open in the map ↗663 classified signals in the fourteen-day window. Recent momentum: steady. These counts include signals awaiting readable evidence.
Earlier sources
Up to ten sources with readable evidence, ordered by their feed timestamp. These archive sources can be older than the live fourteen-day window. This list is a sample, not a measure of total activity. Dates distinguish paper publication, repository creation, model/dataset activity, Hacker News posts, and KEV catalog additions. “First archived” records when SYMTRI first stored the signal.
- OpenAlex · Published · First archived
Research on the Impact and Optimization of the Active Current Recovery Rate of Distributed Photovoltaic Pulse Blocking Strategy on Grid Frequency and Voltage Stability ↗
With the large-scale integration of Class B distributed photovoltaic power sources without fault-tolerant capabilities, the “pulse blocking-recovery” transient behavior during power grid faults has become a key factor affecting the frequency and voltage stability of the system. The recovery rate of active current after pulse blocking directly determines the speed of filling the active power deficit, but the mechanism of its impact on frequency and voltage stability has not been fully revealed. This paper first establishes a frequency response model for photovoltaic power grids with high penetr…
- OpenAlex · Published · First archived
Peak-aware short-term household electricity consumption forecasting using deep learning and transformer-based architectures ↗
Abstract Accurate short-term prediction of domestic electricity consumption is a prerequisite for smart grid management, demand response, and peak-load reduction. This work presents a detailed deep learning analysis of the UCI household power consumption dataset, which includes minute-level measurements from French households over four years. The data is resampled to hourly and undergoes an intensive feature engineering pipeline and then used with different neural architectures: a standard long short-term memory (LSTM), bidirectional attention LSTM, hybrid CNN-LSTM, temporal fusion transformer…
- OpenAlex · Published · First archived
“Tripartite-interlocking Strategy” advances the scale-up implementation of single-atom catalysts for energy and environmental engineering ↗
Single-atom catalysts (SACs) hold transformative prospect for energy conversion and environmental remediation. However, their translation from laboratory research to engineering application is bottlenecked by severe challenges. In response, this review proposes a “tripartite-interlocking” strategy for advancing the scale-up implementation of SACs. Along the outer loop, tailored design for SACs is performed to solve the problems of synthesis processes and integrated modular reactors application. Subsequently, the tailor-designed SACs dictate the synthetic methods, and the morphology and perform…
- OpenAlex · Published · First archived
Toward wind-resilient cable-supported photovoltaic system: nonlinear stiffness, dynamic identification, and parametric instability ↗
Cable-supported flexible photovoltaic (FPV) system is emerging as lightweight low-carbon power-generation structures, but their large-span, low-stiffness configuration makes them susceptible to large-amplitude wind-induced oscillations, for which the conventional small-amplitude assumption no longer provides a valid basis for dynamic analysis. This study establishes a finite-amplitude nonlinear dynamic framework for two-cable FPV systems, in which vibration-induced cable-force evolution is explicitly incorporated into the governing equations as time-varying stiffness. Based on this framework,…
- OpenAlex · Published · First archived
BayesSeg: A Bayesian Optimization Framework for State Segmentation of Electricity Consumption Time Series ↗
In Non-Intrusive Load Monitoring (NILM), adaptive segmentation of electricity consumption time series is critical for appliance recognition. However, prevailing methods face challenges including heuristic parameter tuning, boundary sensitivity, and metric saturation. This paper proposes BayesSeg, a unified framework integrating time-series segmentation, multi-dimensional evaluation, and automatic parameter optimization. The segmentation layer employs a dual steady-state criterion based on the tail value and mean of preceding subsequences, combined with a sequential extraction and complement-se…
- OpenAlex · Published · First archived
Scalable, energy-efficient optical-neural architecture for multiplexed deepfake video detection ↗
Abstract The rapid proliferation of AI-generated visual media has created an urgent need for efficient, trustworthy deepfake detection systems. However, existing deep learning–based detection methods rely on computationally intensive and energy-demanding inference algorithms, limiting their scalability. Here, we present a hybrid digital–analog deepfake video detection framework that combines a lightweight digital front-end with a spatially multiplexed optical decoding back-end for massively parallel analog inference through a programmable spatial light modulator. By simultaneously processing ≥…
- arXiv · Published · First archived
Horizon-Aware Early Event Prediction for Tokamak Disruption Alarms ↗
Reliable disruption prediction is essential for the safe operation of future tokamaks. Existing full-distribution survival methods model the complete residual time-to-disruption distribution, whereas operational decisions primarily depend on disruption risk within a finite prediction horizon. This mismatch motivates introducing Early Event Prediction (EEP) objectives into survival-based disruption prediction. We take Deep Survival Machines (DSM) as the full-distribution baseline and propose applying two established EEP methods to tokamak disruption prediction: Temporal Label Smoothing (TLS), w…
- arXiv · Published · First archived
Geodesic Acoustic Modes in pair plasmas confined in tokamak magnetic fields ↗
This paper is devoted to the derivation of the dispersion relation of the Geodesic Acoustic Mode in pair plasmas, i.e. assuming that ions and electrons have the same mass. Geodesic Acoustic Modes are plasma perturbations playing a crucial role in turbulence regulation, and therefore in the determination of the plasma confinement in tokamaks. Experiments with pair plasmas, like electron-positron plasmas, have been proposed with different kinds of confinements, and aim to study fundamental processes in plasma physics and understanding the formation of the early universe.
- arXiv · Published · First archived
Physics-Informed Kolmogorov-Arnold Networks for Grad-Shafranov Tokamak Equilibria ↗
We employ equation-driven, physics-constrained deep learning to solve the fixed-boundary Grad-Shafranov (GS) equilibrium problem, constructing axisymmetric magnetohydrodynamic equilibria with tokamak-relevant characteristics. Equilibria across linear (Solov'ev) and nonlinear profile functions are constructed using Physics-Informed Kolmogorov-Arnold Networks (KANs) that approximate GS solutions while satisfying appropriate boundary conditions. A highly nonlinear pressure profile recreating high-confinement mode phenomenology, such as pressure pedestals and significant bootstrap current componen…
- arXiv · Published · First archived
Effects of particle-induced electron emission on transport properties in electrically-biased plasma sheaths under fusion-relevant conditions ↗
A rigorous implementation of energy-dependent ion- and electron-induced electron emission in a continuum-kinetic framework is used to reveal their effects on the scaling of plasma properties in the sheath with an applied bias potential to the walls. The approach comes with a novel methodology for modeling particle-induced electron emission (PIEE) that includes 1) improved fitting functions for the yield and spectra, 2) the use of SRIM and a summation of the Lindhard formula and a modified Bethe formula for obtaining accurate stopping powers, and 3) a binding energy correction to the PIEE spect…
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How to assess evidence about Energy
Check each original source for its methods, release status, limitations, and supporting data. A repository announcement, a preprint, and a published study provide different kinds of evidence. SYMTRI’s subject classification helps discovery; it does not independently validate the source’s claims.
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