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Earlier sources: Grid
This energy thread follows grid through original research, public projects, and source reports. Read the evidence below to see which developments are represented in SYMTRI’s archive.
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 ↗19 classified signals in the fourteen-day window. 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
Energy infrastructure for AI data centers: A critical review of grid integration, techno-economic challenges, and future pathways ↗
Artificial intelligence (AI) workloads are rapidly changing the energy landscape of today's data centers, both in terms of total electricity usage and power density at the facility level. The ongoing deployment of large-scale AI training and inference infrastructure is further driving and increasing the demand for electrical reliability, cooling capacity, power quality, grid interconnection, and low-carbon electricity supply. These interdependent issues require coordinated consideration of energy-infrastructure planning that takes into consideration renewable energy generation and storage, ele…
- OpenAlex · Published · First archived
Cybersecurity as a precondition for the carbon–neutral grid: an investment framework for U.S. utility cybersecurity capital allocation ↗
Abstract The transition to a carbon–neutral electricity system depends on the widespread integration of distributed and inverter-based renewable resources. The U.S. Department of Energy and the North American Electric Reliability Corporation have identified these resources as increasing the grid's cybersecurity attack surface faster than current oversight can manage. Federal reviews have also found that guidance for assessing grid cybersecurity risk remains incomplete. In response, the Federal Energy Regulatory Commission has introduced a mandatory internal network monitoring standard and, for…
- OpenAlex · Published · First archived
Integrated design and reliability enhancement technology of quantum current sensor for non-invasive measurement in power grid ↗
Non-invasive, high-precision current measurement is essential for smart grid perception, yet conventional clamp-on current transformers suffer from reduced accuracy under low-load conditions, battery dependence, and maintenance constraints. To address these challenges, this study proposes an NV-compatible solid-state quantum current sensing framework based on spin-resonance magnetic-field transduction, designed for integrated deployment and stable operation in practical grid environments. A non-invasive clamp structure converts line current into measurable magnetic signals, while a self-powere…
- OpenAlex · Published · First archived
Time-Series Anomaly Detection in Off-Grid Solar-Powered Security Systems Using Deep Learning Approaches ↗
Reliable operation of off-grid solar-powered security systems is essential for maintaining uninterrupted surveillance and monitoring in remote locations. This study proposes a fully unsupervised deep learning framework for detecting energy-related anomalies in multivariate solar-system time-series data before they affect system operation. To this end, seven deep learning-based anomaly detection models, namely, LSTM, GRU, Transformer Autoencoder, CNN-LSTM, CNN-GRU, LSTM Autoencoder, and GRU Autoencoder, were developed and evaluated using both a real-world solar energy dataset and an open-source…
- OpenAlex · Published · First archived
AI-Driven Adaptive Smart Grid for Real-Time Energy Optimization and Fault Detection ↗
Abstract: Conventional smart-grid systems often rely on fixed control strategies that have limited ability to respond dynamically to changing electricity demand, renewable-energy variability, equipment degradation, and unexpected faults. This study proposes an AI-Driven Adaptive Smart Grid (AI-ASG) for real-time energy optimization and automated fault detection. The framework integrates machine learning, real-time sensing, adaptive demand-response control, and intelligent fault classification to improve grid efficiency, reliability, and operational responsiveness. A mixed-methods quantitative…
- OpenAlex · Published · First archived
Event-Driven CNN–LSTM for Grid Exchange Forecasting in Industrial Microgrids: Feature Ablation and Multi-Horizon Evaluation ↗
Industrial microgrids integrating distributed photovoltaic (PV) systems require accurate grid exchange forecasting for effective energy management. However, grid exchange exhibits substantial fluctuations due to physical events such as cloud shading, PV curtailment, and surplus export. Most existing deep learning methods rely primarily on raw historical measurements and overlook the physical causes of these variations. This study proposes an event-driven CNN-LSTM framework that augments raw PV and grid signals with a PV ramp rate feature to capture abrupt transitions in solar output. Seven ope…
- OpenAlex · Published · First archived
Real-time digital-twin analysis of multi-terminal DC grid for hydrogen-powered zero-emission mining haul trucks ↗
As the world pursues sustainable solutions to reduce carbon emissions and minimize environmental impact, the mining industry faces significant challenges. Hydrogen fuel cells have become a promising candidate for mining haul trucks, playing a crucial role in advancing a low-carbon future. However, implementing large-scale hydrogen fuel cells in heavy-duty trucks demands careful real-time monitoring and protection. This paper presents the integration of real-time digital-twin (RTDT) analysis within a multi-terminal direct current (MTDC) grid to support the deployment of hydrogen-battery hybrid…
- OpenAlex · Published · First archived
Operational Carbon Performance of Electric Robotaxis: Deadheading, Occupancy, Grid Carbon, and Service Scale ↗
Electric robotaxis have no tailpipe emissions, but the carbon associated with operating the service depends on empty travel, passenger load, vehicle electricity use, and the electricity supply. This study uses 29 months of California commercial robotaxi reporting from August 2023 to December 2025 to examine those factors on a passenger-mile basis. Vehicle-Miles Traveled per Passenger-Mile Traveled (VMT/PMT) is decomposed exactly into deadheading and passenger-stage occupancy and then linked to electricity use and location-based operational carbon dioxide equivalent (CO2e) emissions. Between 20…
- OpenAlex · Published · First archived
Short-term power forecasting model for distributed wind-PV grid connection based on MIC feature screening and improved FTO optimized XGBoost ↗
Abstract Aiming at the strong randomness, fluctuation, redundant feature interference, low fitting accuracy, and easy local optimum of hyperparameter optimization in traditional distributed wind–photovoltaic grid-connected power forecasting models, a novel hybrid short-term power forecasting model is proposed in this paper. Firstly, the Maximum Information Coefficient (MIC) is adopted to quantitatively evaluate the correlation between multi-dimensional meteorological variables and power output, eliminate redundant features, and construct an optimal input feature set. Secondly, Complete Ensembl…
- Hacker News · Posted on Hacker News · First archived
How Delhi Achieved Its Epic Power-Grid Fix ↗
Hacker News discussion.
Live window recorded . This is the ingestion observation time, not the publication or discovery date of each source.
How to assess evidence about Grid
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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