HydroSense AI

Atsafack Lab

AI-driven insights for smarter hydropower maintenance

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Hydropower AI

A large dam structure spans across a body of calm water, with the dam itself featuring industrial elements like metal railings and a control tower. The surrounding area consists of rugged terrain with dense greenery and pine trees covering the hills in the background. Overhead, the sky is clear and blue, with visible electrical pylons atop the hills.

LSTM Models

Developed deep learning LSTM models to predict equipment failures before they happen, reducing downtime.
A grey processor chip with the letters 'AI' prominently displayed in blue, set against a subtle background with faint outlines of a map.

Smart Sensors

Implemented IoT smart sensors for continuous monitoring of hydropower plant components, enabling proactive maintenance.
A vintage typewriter is displaying a sheet of paper with the words 'EDGE COMPUTING' typed in bold letters. The typewriter features a dark green body and metal components, set against a soft white background.

Anomaly Detection

Created algorithms to detect unusual patterns in operational data, alerting operators to potential issues early.
A large rusted industrial machine with intricate gears and mechanisms is set against a clear blue sky. The metal parts are weathered, showing signs of aging and use, with a mix of brown and red oxidation visible. The machine's design suggests it is used for heavy-duty mechanical work or construction.

Smart Sensors

Analyzed energy output trends to optimize performance and support sustainable hydropower management.
A detailed view of a yellow mechanical component with various hydraulic hoses and connectors. The central circular part has the label 'Plasser & Theurer'. The setup includes metallic bolts, pipes, and intricate wiring, indicating it is part of heavy machinery.

About Me

I’m a PhD candidate specializing in IoT and machine learning, passionate about advancing hydropower through AI-driven predictive maintenance.

My Work

Focusing on LSTM models and real-time anomaly detection in smart energy systems.

Research Focus