AI intelligence for energy
and transportation
Advanced ML models for smart grids, renewable forecasting, and autonomous systems
The platform
State-of-the-art neural networks for energy systems and mobility
Optimize infrastructure with deep learning
The Tenzro Energy & Transportation platform combines Graph Neural Networks for power grid topology, Temporal Fusion Transformers for renewable forecasting, and Vision Transformers for autonomous perception.
From grid state prediction to multi-horizon solar forecasting. Automatically validated, optimized, and deployed across distributed energy resources.
Smart grid optimization
Graph Neural Networks model power grid topology for load forecasting, voltage stability, and fault detection.
Renewable forecasting
Temporal Fusion Transformers predict wind and solar generation with uncertainty quantification for 24-hour horizons.
Autonomous perception
Swin Transformers detect objects, lanes, and traffic signs for real-time autonomous vehicle decision-making.
AI model architectures
Production-ready models for energy systems and autonomous mobility
Power Grid
Graph Neural Network for power grid state prediction and optimization. GraphSAGE convolutions with GRU for temporal dynamics.
Probabilistic Forecaster
Multi-horizon probabilistic forecasting for renewable energy. Variable selection, LSTM encoder-decoder, and quantile regression.
Vision Transformer
Hierarchical vision transformer for autonomous vehicle perception. Object detection, lane detection, and semantic segmentation.
Policy Gradient
Deep Deterministic Policy Gradient for energy management. Optimizes battery charging, load scheduling, and grid interaction.
Core capabilities
Advanced neural networks for energy infrastructure and mobility
Grid state prediction
Graph Neural Networks analyze power grid topology to forecast load patterns, voltage stability, and fault locations across distributed networks.
Renewable forecasting
Multi-horizon probabilistic predictions for wind and solar generation with uncertainty quantification and 24-hour ahead visibility.
Autonomous perception
Real-time object detection, lane boundary identification, and traffic sign recognition for autonomous vehicle decision-making.
Energy optimization
Deep reinforcement learning optimizes battery energy storage, load scheduling, and grid interaction for cost reduction and stability.
The workflow
From data ingestion to autonomous operation
Define system parameters
Configure grid topology, renewable assets, vehicle sensors, and operational constraints through API endpoints or web interface.
Model training & validation
Graph Neural Networks learn grid structure, Transformers capture temporal patterns, and vision models train on perception datasets.
Real-time inference
Deploy models for live predictions: grid state every 15 minutes, renewable forecasts every hour, vehicle perception at 30Hz.
Optimization & control
Reinforcement learning agents execute optimal actions for energy management, load balancing, and autonomous navigation.
Monitor & adapt
Continuous learning from operational data improves accuracy, detects anomalies, and adapts to changing conditions.
Deploy AI intelligence for
energy and mobility
Graph Neural Networks, Temporal Transformers, Vision AI.