Description
Indoor occupancy ground truth dataset with variable ventilation.
Overview
This dataset contains measurements from a controlled laboratory experiment studying indoor air quality (IAQ) and occupancy detection in a simulated Finnish office environment. The dataset contains several IAQ sensors, and the ventilation rate is also measured. Ventilation rate varies in the dataset.
The data was collected to test ventilation control based on occupant count estimates from an ML model.
Experimental Setup
Test Environment
Location: Laboratory designed to simulate a typical Finnish office room
Capacity: Up to 4 occupants
Dimensions: 2.69m (height) × 2.71m (width) × 3.0m (length)
Ventilation System
Type: Variable Air Volume (VAV) system
Control: Modulated airflow rates via Air Handling Unit (AHU)
Configuration: Supply and exhaust setpoint flows maintained at equal rates
Temperature control: Temperature maintained by chiller
Sensors and Measurements
Sensor
Measurement
Accuracy
Miran DLS IAQ.THB-H9+CO2
CO₂ (ppm)
±30 ppm
Pressure (mbar)
±1.0 mbar
Temperature (°C)
±0.3°C
Relative Humidity (%)
±2.5%
HDK - Duct CO₂ transmitter / controller
CO₂ (ppm)
±40 ppm +3 %
Lindab FTCU
Airflow (l/s)
±5%
Steinel HPD2
Occupant count
** Miran DLS IAQ.THB-H9+CO2 placed inside the room**: 1.75 meters
** HDK - Duct CO₂ transmitter / controller: Measures supply and exhaust air CO₂ inside the duct.
Occupancy ground truth: Camera-based computer vision occupant counting sensors, and later manually recorded.
Dataset Details
The training data includes multiple ventilation control strategies, and in the test data, we controlled ventilation according to occupant count.
Occupancy-based control:
Base ventilation: 3 l/s (unoccupied)
Per-person increment: 6 l/s per detected occupant
Update frequency: Every 5 minutes
Training Data
Note that the test room was renovated after March 2025, including the addition of thermal insulation to the walls.
Training data 1: February 2024 - March 2025
Duration: 5 days, 21 hours
The collection saw various usage, meaning that the data may be noisier than the training data 2.
Sensors: Miran DLS IAQ.THB-H9+CO2, 2x Lindab FTCU, Steinel HPD2
Occupancy data: Camera-based occupancy ground truth
Training data 2: October 2025 - November 2025
Duration: 2 days, 20 hours days
Sensors: Miran DLS IAQ.THB-H9+CO2, 2x Lindab FTCU, 2x HDK - Duct CO₂ transmitter / controller
Occupancy data: Manually recorded occupancy ground truth
Ventilation test
Period 2: October 2025 - November 2025
Duration: 24 hours
Collection method: Manually recorded occupancy ground truth
Testing Data
Ventilation control testing: 23 hours, 50 minutes
Funding
This research was funded by the Promotion Centre for Electrical Engineering and Energy Efficiency (STEK ry) grant KITSI2.0.
Overview
This dataset contains measurements from a controlled laboratory experiment studying indoor air quality (IAQ) and occupancy detection in a simulated Finnish office environment. The dataset contains several IAQ sensors, and the ventilation rate is also measured. Ventilation rate varies in the dataset.
The data was collected to test ventilation control based on occupant count estimates from an ML model.
Experimental Setup
Test Environment
Location: Laboratory designed to simulate a typical Finnish office room
Capacity: Up to 4 occupants
Dimensions: 2.69m (height) × 2.71m (width) × 3.0m (length)
Ventilation System
Type: Variable Air Volume (VAV) system
Control: Modulated airflow rates via Air Handling Unit (AHU)
Configuration: Supply and exhaust setpoint flows maintained at equal rates
Temperature control: Temperature maintained by chiller
Sensors and Measurements
Sensor
Measurement
Accuracy
Miran DLS IAQ.THB-H9+CO2
CO₂ (ppm)
±30 ppm
Pressure (mbar)
±1.0 mbar
Temperature (°C)
±0.3°C
Relative Humidity (%)
±2.5%
HDK - Duct CO₂ transmitter / controller
CO₂ (ppm)
±40 ppm +3 %
Lindab FTCU
Airflow (l/s)
±5%
Steinel HPD2
Occupant count
** Miran DLS IAQ.THB-H9+CO2 placed inside the room**: 1.75 meters
** HDK - Duct CO₂ transmitter / controller: Measures supply and exhaust air CO₂ inside the duct.
Occupancy ground truth: Camera-based computer vision occupant counting sensors, and later manually recorded.
Dataset Details
The training data includes multiple ventilation control strategies, and in the test data, we controlled ventilation according to occupant count.
Occupancy-based control:
Base ventilation: 3 l/s (unoccupied)
Per-person increment: 6 l/s per detected occupant
Update frequency: Every 5 minutes
Training Data
Note that the test room was renovated after March 2025, including the addition of thermal insulation to the walls.
Training data 1: February 2024 - March 2025
Duration: 5 days, 21 hours
The collection saw various usage, meaning that the data may be noisier than the training data 2.
Sensors: Miran DLS IAQ.THB-H9+CO2, 2x Lindab FTCU, Steinel HPD2
Occupancy data: Camera-based occupancy ground truth
Training data 2: October 2025 - November 2025
Duration: 2 days, 20 hours days
Sensors: Miran DLS IAQ.THB-H9+CO2, 2x Lindab FTCU, 2x HDK - Duct CO₂ transmitter / controller
Occupancy data: Manually recorded occupancy ground truth
Ventilation test
Period 2: October 2025 - November 2025
Duration: 24 hours
Collection method: Manually recorded occupancy ground truth
Testing Data
Ventilation control testing: 23 hours, 50 minutes
Funding
This research was funded by the Promotion Centre for Electrical Engineering and Energy Efficiency (STEK ry) grant KITSI2.0.
| Date made available | 19 Jan 2026 |
|---|---|
| Publisher | Zenodo |
Dataset Licences
- CC-BY-4.0
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