AI & Machine Learning Engineering for Energy Operations
Custom forecasting models, time-series anomaly detection algorithms, and autonomous AI agents designed with strict human-in-the-loop controls for control rooms and trading desks.
What We Build
Scheduling Revision Agents that analyze live irradiance drift and propose 15-minute schedule revisions with explicit rationale.
Time-Series Anomaly Detection models isolating inverter degradation, tracker drift, and string faults 48 hours in advance.
DGR Narrative Drafting Agents that generate daily operational summaries explaining outages, curtailments, and weather impacts.
Computer Vision Defect Classifiers processing drone thermography footage into IEC 62446 punchlists.
Concrete agent workflows for renewable operations
These are not chatbot demos. They are deterministic, structured agent workflows operating on real telemetry and regulatory windows.
Autonomous Scheduling Revision Agent
Monitors real-time plant telemetry against submitted SLDC schedules.
Evaluates irradiance drop trends 45 minutes ahead of each SLDC revision window, computes optimal revision quantities to minimize DSM penalty exposure, and presents the draft schedule to the duty operator with single-click submission approval.
Inverter Electrical Diagnostic Agent
Assists control room operators with real-time fault isolation.
Correlates high-frequency inverter electrical data (string currents, DC voltages, IGBT temperatures) with historical failure signatures to identify blown fuses, tracker stall, or transformer overheating before equipment trips.
Daily Generation Narrative Agent
Converts raw generation telemetry into executive operational narratives.
Ingests 96-block generation data, weather logs, and inverter trip timestamps, synthesizing an audit-ready executive summary explaining exactly why generation deviated from P50 estimates.
Drone Thermography Hotspot Detector
Automates aerial thermographic PV inspection analysis.
Applies convolutional neural networks to infrared imagery to classify bypass diode failures, cell hotspots, and PID degradation with exact GPS string-level coordinate mapping.
We do not put an LLM in the path of a statutory regulatory submission without an approval step. By default, every scheduling recommendation, portal filing, or critical inverter command requires explicit authorized human confirmation.
Automated DSM Revision Workflow for a 340 MW Solar Portfolio
Built a specialized scheduling agent that flagged irradiance drift across 3 states and prepared revision schedules for SLDC submission 20 minutes before closure.
Frequently Asked Questions
Do your AI agents submit schedules to the SLDC without human oversight?
No. By default, our regulatory agents enforce a human-in-the-loop approval step. The agent analyzes telemetry, drafts the revision, and calculates penalty impact; an authorized operator clicks to approve the submission. Autonomous mode can be enabled with explicit threshold guards.
How is proprietary plant telemetry protected when using LLMs?
Telemetry and grid schedules remain inside your private security perimeter. We deploy localized private LLM endpoints or enterprise isolated instances with zero external data retention and encryption in transit and at rest.
Do we own the intellectual property and code for custom AI models?
Yes. Custom software and model development projects are delivered with complete source code ownership, model weights, repository handover, and technical documentation.
Discuss your custom requirements
Schedule a scoping call with our lead engineers. We will analyze your workflows and outline an actionable implementation roadmap.
Schedule a Scoping Call