Smart Grids & Operations
Machine learning for state estimation, fault detection, grid stability and autonomous control of transmission and distribution networks.
AIREM stands for Artificial Intelligence Research in Energy & Mechanics. We bring together researchers, engineers and industry leaders to apply AI and machine learning across energy and mechanical systems — from smart grids and storage to machines, structures and manufacturing.
Our working groups tackle the technical challenges of energy and mechanical engineering — from power grids and storage to machines, structures and production lines.
Machine learning for state estimation, fault detection, grid stability and autonomous control of transmission and distribution networks.
Probabilistic forecasting of load, solar and wind generation, and prices — built on deep learning and physics-informed models.
Optimal dispatch, battery health prediction and lifecycle management for grid-scale and distributed storage assets.
Reinforcement learning and multi-agent systems for flexible demand, virtual power plants and efficient energy trading.
Condition monitoring and anomaly detection for turbines, transformers, PV plants and power electronics using sensor data.
Data-driven planning, carbon accounting and optimization tools that accelerate the path to net-zero energy systems.
Machine learning surrogates for finite element and CFD models that cut simulation time for structures, thermal systems and fluid machinery.
Learning-based control for robots, drives and mechatronic systems — from motion planning to real-time control of machines.
Process optimization, quality inspection and digital twins for production lines, industrial equipment and rotating machinery.
Simulincai is the workspace built with AIREM for engineers and researchers: a document-aware assistant, a real-physics simulator with 125 equipment blocks, machine-learning surrogates and an AI paper writer — nine tools that share one account, one project store and one paper.
Ask your own standards, papers and reports — every answer is checked against the source it cites.
Rankine, Brayton, refrigeration and psychrometrics on a drag-and-drop canvas, with real physics behind each block.
Train ML models on the real solver, sweep parameters, and let an optimiser or RL agent find the best design.
Turn results into a manuscript with citations, equations and charts, exported to Word.
Peer-reviewed journal, technical reports and open benchmark datasets.
An annual summit, regional chapters and hands-on technical workshops.
Courses, webinars and mentorship for students and professionals.
Connecting utilities, startups and universities on real-world pilots.
Members receive priority registration and discounted rates for all AIREM events.
A technical deep dive with live Q&A and open-source code walkthrough.
Hands-on session on training fast surrogate models for mechanical and thermal simulation.
Two days of keynotes, paper sessions, industry panels and networking.
Access research, events, mentorship and a community of experts working at the intersection of artificial intelligence and energy. Students who join before December 31, 2026 get their first 6 months free.