AI shows promise for flood forecasting and water security in data scarce regions

New research reveals that "foundation models" trained on vast, general time-series data may be able to forecast river flows accurately, even in regions with little or no local hydrological records.
Devdiscourse

From transformers to microgrids: AI expands across critical energy infrastructure

Utilities worldwide are turning to artificial intelligence (AI) and machine learning to stabilize networks, forecast ...
Digital Trends

Michigan Dad Built a Solar Power System Using Temu and Cut His Energy Bill by 90%

With $1,300 in Temu-sourced batteries and a lot of determination, this dad of six—soon to be seven—built a custom solar system that powers his household of eight and protects his growing family from ...
techxplore

Machine learning-based design enables more efficient wireless power transfer

Wireless power transfer (WPT) systems transmit electrical energy from a power source to a load without physical connectors or wires, using electromagnetic fields. This idea goes as far back as the ...
IEEE

A Hybrid Machine Learning Approach for Predicting Power Transformer Failures Using Internet of Things-Based Monitoring and Explainable Artificial Intelligence

Abstract: Power transformers are critical components in ensuring the continuous and stable operation of power systems. Failures in these units can lead to significant power outages and costly downtime ...
AZoOptics

High-Power Laser Stabilization Using Machine Learning

From left to right, Berkeley Lab researchers Anthony Gonsalves, Alessio Amodio, and Dan Wang align precision optics to prepare the Berkeley Lab Laser Accelerator (BELLA) Petawatt laser for ...
IEEE

Power System Faults Detection Using Machine Learning with PMU Based Wide Area Monitoring

Abstract: An effective control system is required to increase the power system’s safety, effectiveness, and reliability. Phasor measurement unit (PMU) and the wide area measurement systems (WAMS) both ...