AI HVAC optimization reads your BMS, meters, weather and occupancy, forecasts tomorrow's cooling load, and writes safe setpoints for chillers, AHUs and pumps back to the controls — with engineers holding override and every saving measured against a baseline.
Sample data. Twelve months of baseline chiller-plant energy, then a six-month forecast with optimised sequencing and setpoints; the band shows forecast uncertainty.
AI HVAC optimization is software that uses machine learning on building management system data, energy meters, weather forecasts and occupancy to run heating, ventilation and cooling plant more efficiently. It outputs a load forecast, recommended or automatically applied setpoints and equipment sequences, fault alerts, and a measured energy saving against a weather-normalised baseline, while keeping comfort and equipment limits your engineers set.
Chillers run in fixed order, setpoints stay flat through empty afternoons, and simultaneous heating and cooling goes unnoticed. In hot climates the waste lands on the biggest line of the utility bill.
of peak electricity demand is cooling across the Middle East and North Africa, and a quarter of annual demand, per the IEA.[1]
or more of building energy is often wasted through inefficiencies, according to ENERGY STAR.[3]
of MENA's electricity demand growth to 2035 comes from cooling and desalination, the IEA projects.[2]
site energy savings are estimated nationally for US commercial buildings from better controls, in a PNNL study for the US DOE.[4]
We connect to the controls you have and clean the point list, because half of optimisation is knowing which sensors lie.
Models predict cooling load hours ahead, then choose chiller staging, chilled-water and supply-air setpoints that meet it at the lowest energy and tariff cost.
Every change is logged and measured, so finance sees a number they can defend and engineers see faults before tenants call.
We map BMS points and meters, flag broken sensors, and agree comfort bands, equipment limits and the metric — typically HVAC kWh per cooling degree day.
Load forecasts and recommended setpoints run alongside existing schedules. Your facilities team sees each recommendation and the expected effect.
With engineer approval, the optimiser writes setpoints within hard limits on selected plant. Any manual override takes priority immediately.
We report energy and demand savings against the baseline, comfort exceptions and faults found, plus the rollout case for other buildings.
| Criterion | Fixed BMS schedules | Packaged analytics platform | Stratgik build + run |
|---|---|---|---|
| Adjusts to weather and occupancy | No, static schedules | Partly, often dashboards only | Forecast-driven setpoints every 15 minutes |
| Writes back to controls | Manual changes | Varies by vendor | Advisory first, then supervised closed loop |
| Mixed BMS vendors and older plant | Depends on the integrator | Best on supported systems | Connectors built for your point list |
| Savings evidence | Utility bill guesswork | Vendor-calculated | Weather-normalised baseline you agree up front |
| Setup effort | None | Low for standard sites | Six-week pilot per building type |
| Best fit | Small, simple buildings | Uniform portfolios on one BMS | Malls, hotels, data halls and mixed portfolios |
Hotter summers and tighter efficiency targets make the cooling plant the obvious place to find savings — without new chillers or capital projects.
HVAC share and achievable saving vary by building, climate and plant age. Both are assumptions here; the pilot measures them against your own weather-normalised baseline.
Illustrative estimate using your inputs and stated assumptions, not a quote or guarantee. The pilot measures the real figure against your baseline.
One building or central plant, 6 weeks
Per building, per month
Portfolios and campuses
BMS integrator time, gateways, additional meters and cloud compute billed at cost with no markup; taxes excluded. GBP and AED prices are indicative conversions from USD.
Savings depend on how far the building's current operation is from optimal, so we measure rather than quote a fixed number. PNNL research for the US Department of Energy estimates better controls could save about 29% of site energy across US commercial buildings. Sites with fixed schedules and older sequencing usually have the most room; well-commissioned buildings have less.
It should not, because comfort is a hard constraint rather than a trade-off. Temperature, humidity and CO₂ bands are set with your facilities team, the optimiser cannot write setpoints outside them, and any manual override on the BMS wins immediately. We track comfort exceptions as a pilot metric alongside energy, and report both.
Yes, in most cases. We connect over BACnet/IP, Modbus or vendor APIs to Johnson Controls Metasys, Siemens Desigo, Schneider EcoStruxure, Honeywell and Tridium Niagara systems. Very old or proprietary controllers may need a gateway or integrator support, which we identify in week one and bill at cost.
Yes. On district cooling, savings come from lower chilled-water consumption and, where contracts include them, lower capacity charges. The optimiser works on the building side — AHUs, fan coils, secondary pumps and setpoints — and uses BTU meter data to measure results. Gulf buildings are strong candidates because cooling dominates their energy use.
A Stratgik pilot on one building or central plant is $16,000 over six weeks. Ongoing run is $3,500 per building per month, including continuous optimisation, fault alerts and monthly savings reports. Integrator time, gateways and extra meters are billed at cost. For large malls, hotels and data halls, monthly fees are typically small relative to the cooling bill.
A first building runs in about six weeks. Week one audits BMS points and sets a baseline, week two runs advisory mode, and supervised control runs for three weeks before a measured readout. Portfolios roll out faster after the first site because connectors and models carry over between similar buildings.
Share a month of point data and utility bills. We'll show where the plant wastes energy, which setpoints we'd change first, and what a pilot would measure.