AI Thermal Network Optimization

Machine learning for thermal energy distribution network efficiency

The Problem

“Optimize thermal networks amid volatile demand and prices”

Organizations face these key challenges:

Inaccurate short-term heat demand and return temperature forecasts drive conservative overproduction and higher heat losses

Local, rule-based control of pumps/valves and generators causes suboptimal system-wide ΔT, elevated return temperatures, and higher pumping power

Limited visibility into network anomalies (leaks, bypassing substations, sensor drift, fouling) leads to slow diagnosis, customer complaints, and avoidable downtime

Impact When Solved

5-12% lower thermal energy input by optimizing dispatch across boilers/CHP/heat pumps and thermal storage3-8% lower pumping electricity through coordinated pressure/flow optimization while maintaining service constraints10-25% peak load reduction (or deferred capacity investment) via predictive control and storage optimization, improving reliability during cold snaps

Real-World Use Cases

AI in Energy Industry: Smart Grid Optimization and Energy Management

This is like giving the entire power system—power plants, grids, and large customers—a real‑time ‘autopilot’ that constantly predicts demand, reroutes electricity, and tunes equipment so you use less fuel, waste less energy, and keep the lights on more reliably.

Time-SeriesEmerging Standard

8.5

AI Grid Congestion Management

This AI helps optimize the layout of power grids to reduce congestion without increasing costs or carbon emissions.

optimizationgrowing

8.0