Solutions

Four energy systems. One method: model, simulate, decide, execute, verify.

SCENARIOS

Four energy systems, four kinds of operating conditions

Each system has its own physical constraints and its own measurement basis. What carries across is the method, not a template.

01 · DISTRICT HEATING
District heating
Full-season AI-managed substation operations, long-distance pumping, multi-source dispatch, and secondary-network balancing.

02 · LOW-CARBON PARKS
Low-carbon energy parks
Demand forecasts, equipment constraints and multi-energy scheduling for cooling, heating, electricity and storage.

03 · DATA CENTERS
Data centers
Physics-based cooling optimization with rack-hotspot forecasts, cooling-air control and equipment constraints.

04 · MANUFACTURING
Advanced manufacturing
Model-driven dehumidification, plant cooling and utilities optimization within process quality limits.

WHAT THE WORK INVOLVES

What each system actually asks for

The engineering question changes from system to system. So does the evidence we can hand over.

District heating

Heating problems are rarely single-point faults. Heat sources, the primary network, substations and end users affect each other, so a setpoint change in one place shows up as a pressure or temperature problem somewhere else.

We build source, network, substation and demand into one solvable model, then use it to schedule, balance and optimise under each specific operating condition rather than one fixed season-long assumption.

Multi-source dispatch by efficiency and cost when CHP, industrial waste heat and boilers operate together
Long-distance and high-elevation transmission: static and dynamic pressure, temperature and pumping re-evaluated after every condition change
Substation AI group control that separates spatial water distribution from weather-driven timing, so transient return-temperature swings are not misread as hydraulic imbalance
Fault location narrowed with online hydraulic simulation, network sensors and operating data

Low-carbon energy parks

A park's energy problem is typical: each subsystem reaches its own optimum and the whole does not. Cooling, heating, electricity, gas and storage each have a local control objective that conflicts with the park-level one.

We place them in a single forecasting and scheduling framework and optimise against park-level efficiency and carbon targets, instead of asking each subsystem to meet its own standard.

Cooling and heating sources, storage and distribution decided together against time-of-use tariffs and load forecasts
PV output forecast combined with storage state to raise self-consumption and lower energy cost
Carbon ledger collected by activity level and emission factor, auditable rather than estimated
Distributed energy stations connected, dispatched and assessed on one basis

Data centers

As rack density rises, cooling becomes the main variable in efficiency. The cooling plant, the distribution loop and the terminal units are usually controlled separately, which leaves the system optimum unreachable.

We model cooling plant, distribution and terminal units as one system, match cooling supply to IT load continuously, and search for the system-level optimum while holding supply-air temperature and equipment safety as constraints.

Chillers, pumps and cooling towers optimised jointly toward the lowest system energy point
Supply-air temperature and airflow optimised for air-cooled rooms with rack temperature kept safe
Cooling and storage strategies set against compute-load forecasts and time-of-use tariffs
Parallel deployment with the existing control system, double interlock and one-key return to conventional mode

Advanced manufacturing

In lithium battery and semiconductor plants, facility systems carry a high share of energy use and operate under strict process constraints. An energy saving that disturbs the process environment is not a saving.

We build physics models of HVAC, compressed air, chiller plant, dehumidification and process cooling, then optimise them as one loop with the process environment and production safety as hard constraints.

Desiccant dehumidification modelled with rotor parameters calibrated against operating data, replacing long-fixed settings
MAU and DCC coordinated with the chiller plant to follow cleanroom demand
Process temperature treated as a hard constraint, so cooling redundancy is compressed only where production allows
System-level modelling rather than separate optimisation of each subsystem

Start with a real operating challenge.

Share the network, operating constraints and control objectives. We will discuss technical fit and the required evidence.

Contact our team ↗