Solutions for Every Scenario

The same energy is wasted in different ways in every business. Element AI learns each scenario's behavior — occupancy, SOP, weather, tariffs — and lets equipment run only when needed, at exactly the power needed.

Scenario 01

Hotels & Resorts

Roughly 60% of guestrooms sit empty during operating hours — yet their air conditioning keeps running at full guest comfort settings. Guests open windows, set extreme temperatures and check out without anything switching off.

Element AI senses real room occupancy (mmWave radar, door locks, PMS integration), predicts departure before it happens, and returns vacant rooms to energy-saving mode automatically — while welcoming each new guest with a pre-conditioned room.

Guestroom occupancy AI ("Housekeeping assistant") Chiller plant & pump group control VRV/VRF central control retrofit Window-open detection

Typical pain points we solve

Guests set extreme temperatures; the AC never stops
AC left running in empty or checked-out rooms
Windows or balcony doors open while cooling
No central control across hundreds of rooms
Fouled heat exchangers quietly add 30% to consumption
Low season: chillers cannot turn down efficiently
Forced temperature limits trigger guest complaints
Pumps and towers run fixed-speed regardless of load
15–20%
overall energy reduction
THB 240k+
monthly savings per hotel (measured)
Scenario 02

Restaurants & F&B Chains

A restaurant's kitchen exhaust genuinely needs to run less than 30% of opening hours — but without knowing what is happening at each wok station, it runs at full speed all day, dumping air-conditioned air onto the street.

Element AI reads the cooking SOP from equipment status and vision AI, then modulates exhaust fans, makeup air, induction power, signboards and dining-room air conditioning in real time. Chefs notice nothing; the P&L does.

SOP-driven exhaust control — up to 60% fan savings Signboard & lightbox: 30–50% Induction cooker dynamic power: 10–15% AC condenser misting: ~15%

Typical pain points we solve

Exhaust fans run at full speed from open to close
Kitchen heat spills into dining areas
Signboards and lightboxes blaze all night
Induction cookers idle at full power
AC battles kitchen heat around the clock
Every store wastes differently — no way to standardize
HQ cannot see store-level energy data
Utility bills climb with every new store opened
25–53%
store-wide savings (measured)
THB 2.7M+
annual savings per store (top case)
Scenario 03

Shopping Malls & Complexes

A mall's chiller plant is a combinatorial nightmare: demands, chillers, pumps and cooling towers each have thousands of possible combinations — no human operator can keep the plant at its best point as weather and crowds shift all day.

Element AI searches that combination space continuously — staging equipment, balancing run-hours, pre-cooling ahead of crowds and exploiting off-peak tariffs — while zoning supply by floor, orientation and actual footfall.

Chiller plant AI optimization Zone-by-zone demand matching Peak/valley tariff arbitrage Tenant sub-metering transparency

Typical pain points we solve

70% of cooling lost through glass curtain walls
Greenhouse-effect glass roofs paid for in electricity
Same setpoint for every floor and orientation
AHUs and boilers that cannot follow real demand
Ice storage melting before the peak, or never used up
Event spaces with sudden large cooling demands
Group control exists on paper, overridden by hand
Tenant energy bills impossible to allocate fairly
25–35%
HVAC savings (A/B tested)
32%
whole-mall reduction (reference case)
Scenario 04

Office Buildings

Empty meeting rooms, vacant floors, staggered overtime — office air conditioning is sized for peak occupancy that almost never happens. Property manages the equipment; the tenant pays the bill; nobody is rewarded for saving.

Element AI ties HVAC and lighting to real presence and booking calendars, automates after-hours set-back, and gives landlords and tenants one shared, trusted view of the savings.

Meeting-room auto set-back Occupancy-based zoning VRF integration without replacement Landlord–tenant aligned incentives

Typical pain points we solve

AC on in empty rooms, floors after hours
Windows open against running air-con
Oversized units cycling poorly in small rooms
Some floors freezing, others sweltering
Everyone sets 16°C; the whole floor pays
Booked-but-unused rooms never release their cooling
No unified scheduling across work patterns
Different comfort needs, one blunt policy
10–25%
monthly electricity reduction
100%
visibility of space utilization
Scenario 05

Parking Facilities

A basement car park burns 24-hour full-power lighting for customers who are only there 2–5 hours a day — or worse, half the circuits are switched off, creating dark, unsafe zones.

T8 smart fixtures with radar sensing brighten ahead of each vehicle ("light follows vehicle"), dim to 30% behind it, and feed an AI platform that also drives ventilation and dehumidification by real traffic. AR navigation and reverse car-finding come built in — at about 1/10 the cost of a conventional smart-parking system.

Light-follows-vehicle tracking Stepless 10–100% dimming Smart ventilation & dehumidification AR navigation & reverse car-finding

Typical pain points we solve

24h full-power lighting for 2–5h of real use
Switched-off circuits leave dark, unsafe areas
Conventional sensor lamps snap off after 30s
Ventilation runs flat-out regardless of CO/traffic
Drivers creep through dim, uneven lighting
No usage or energy data for management
Smart-parking retrofits cost a fortune
Customers can never find their parked car
85%
lighting energy savings
80%
ventilation & dehumidification savings
Scenario 06

Education & Campuses

Classrooms, dormitories, canteens and halls each follow their own timetable — but their air conditioning follows nobody. Old fixed-speed units, doors propped open, and dorm AC running through empty afternoons.

Element AI aligns every zone with the academic calendar and real occupancy: pre-cooling before classes, automatic set-back in vacancies, canteen ventilation matched to meal rushes — a visible ESG story on a tight budget.

Timetable-driven scheduling Dormitory occupancy control Canteen tidal-demand ventilation Server & network-room cooling: up to 70%

Typical pain points we solve

Old fixed-speed units with poor efficiency
Doors and windows open, cooling the campus
Intermittent use patterns no schedule captures
Dorm AC left on through empty afternoons
Unlimited student setpoints and fan speeds
Whole-campus one-size-fits-all switching
Manual patrols instead of load prediction
ESG reporting expectations with no data
4-domain
HVAC · air · lighting · plug loads
THB 21k+
monthly savings per kindergarten (measured)

Which Scenario Looks Like Yours?

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