Smart Thermal Tech for India
Neural Simulation, Material Intelligence & Heat Gain Audits — what they mean, how they work, and how they align with India's building codes and green certification bodies.
Neural Simulation
Running 10,000+ thermal scenarios to predict indoor temperature before construction.
Think of it like the IMD weather forecast — but instead of predicting tomorrow's rain across Delhi, it predicts "will the third floor of this office block hit 38°C on a May afternoon?" It runs every possible weather + design scenario to find problems before they happen.
10,000 Thermal Scenarios
An AI model tests every combination of sun angle, wind speed, occupancy level, and wall material — covering edge cases a human engineer would miss.
Indoor Volatility
How wildly the indoor temperature swings during the day. A room that goes from 22°C at 8 AM to 36°C by 3 PM has high volatility — uncomfortable and expensive to cool.
Microclimate
The local temperature and wind conditions around a specific building. A building on a narrow Mumbai lane has a different microclimate than an open site in GIFT City.
Monte Carlo Method
Running thousands of random scenarios reveals risks that deterministic calculations miss. Crucial for climate extremes like Delhi's 45°C May heatwaves.
Collect local weather data
IMD hourly data for that building's exact district — temperature, humidity, solar radiation, wind speed across 8,760 hours.
Feed the building design into the AI model
Wall thickness, glazing ratio, orientation, floor plan, shading devices. The neural network was trained on thousands of real buildings.
Run 10,000+ scenarios automatically
Vary inputs randomly within realistic ranges and record the indoor temperature outcome for each to build a probability distribution.
Flag risks and recommend fixes
Output: "In 23% of scenarios, Room 4B exceeds 34°C. Adding a 600mm overhang on the west facade reduces this to 4%." Actionable advice.
Material Intelligence
Identifying what your building's skin is made of — and exactly how much heat each surface radiates.
Think of a nutrition label on packaged food. It tells you how much fat and sugar is in there. Material Intelligence gives every wall and roof surface a similar label: how much heat it absorbs, holds, and radiates back into the building.
Emissivity (ε)
A number from 0 to 1. High emissivity (dark brick, ε ≈ 0.90) radiates strongly. Low emissivity reflects most heat away.
U-value (W/m²·K)
How fast heat flows through a wall. Lower = better. ECBC specifications vary by climate zone; Jodhpur limit is 0.4.
Spectral Analysis
Thermal infrared cameras or hyperspectral scanners read reflected wavelengths. Materials have unique signatures allowing non-contact identification.
SHGC for Indian Windows
Solar Heat Gain Coefficient measures solar transmission. ECBC requires low SHGC glass in hot zones. Material Intelligence measures actual performance.
Heat Gain Audits
A full-body check-up for a building — finding the hidden, non-obvious spots where heat sneaks in.
A doctor doesn't just say "you feel hot" — they run tests. A Heat Gain Audit does the same for buildings: "this 2-metre gap in the stairwell insulation is responsible for 11% of your cooling bill — seal it."
Thermal Bridges
A spot where heat bypasses insulation through conductive material. Exposed columns on Indian concrete frames are a common issue.
Ventilation Gaps
Hot air trapped behind cladding or service penetrations. Gaps around AC pipe routes are prime candidates.
Quick Compare
| Feature | What it does (simple) | Key output | ECBC 2017 | GRIHA Link |
|---|---|---|---|---|
| Neural Simulation | Runs 10,000 weather scenarios via AI models | Overheating probability | Whole Building Method | Criterion 10 |
| Material Intelligence | Scans surfaces with spectral sensors | Material properties (U-value, ε) | Envelope Prescriptive | Criterion 8 |
| Heat Gain Audits | Physical scanning & leakage testing | ROI-ranked list of leaks | EPF inputs for retrofits | Criterion 13 & 18 |