Residential and commercial building sensor data: temperature, humidity, CO₂, occupancy, appliance-level consumption and home-automation event logs.
928,991 measured rows with 12 columns across 99 recording IDs; the accompanying metadata lists 100 background, wine and banana sessions.
Continuous smart-home sensor events and activity-recognition representations; UCI reports 13,956,534 instances, with raw and engineered-feature files.
10,129 observations with 19 columns, including four sound channels and an occupancy target, from a seven-node indoor sensing installation.
Free CC0 synthetic dataset: 400 rows of indoor temperature, humidity, CO2, light and occupancy readings. 22°C. Reproducible from its seed.
First open Zigbee IoT dataset with fully decrypted payloads, captured from a real smart home with 15 Zigbee devices over 20 days. Distributed as a single archive (dataset.tar.gz, 663.4 MB) of pcap captures with the network key included. Published October 2024 on Zenodo under CC BY 4.0.
Free CC0 synthetic dataset: 1,000 rows of indoor temperature, humidity, CO2, light and occupancy readings. Cold Climate, 20.5°C.
Real smart-home IoT dataset from non-invasive PIR motion, magnetic door/window, and temperature sensors installed in multiple households. CSV format (6.4 MB). Used for occupancy detection, activity recognition, and smart home automation research.
Free CC0 synthetic dataset: 10,000 rows of indoor temperature, humidity, CO2, light and occupancy readings. Tropical Climate, 22°C.
Free CC0 synthetic dataset: 10,000 rows of indoor temperature, humidity, CO2, light and occupancy readings. Cold Climate, 22°C.
Free CC0 synthetic dataset: 2,000 rows of indoor temperature, humidity, CO2, light and occupancy readings. Arid Climate, 21°C. Reproducible from its seed.
Free CC0 synthetic dataset: 100 rows of indoor temperature, humidity, CO2, light and occupancy readings. 26.5°C. Reproducible from its seed.
Free CC0 synthetic dataset: 1,000 rows of indoor temperature, humidity, CO2, light and occupancy readings. Reproducible from its seed.
Comprehensive smart home dataset with 1,048,575 rows and 31 columns including timestamps, device states (TV, oven, lights, fridge) and activity labels for machine learning classification of daily activities.
Energy consumption readings from 5,567 London households participating in the UK Power Networks Low Carbon London project.
BCCC-IoT-IDS-Zwave-2025 is a behavior-centric cybersecurity dataset focusing on Z-wave protocol vulnerabilities and intrusion detection for modern smart home automation systems.
A real-world, hourly dataset of electricity, heating, and cooling consumption for a commercial building, ideal for benchmarking and building energy model calibration.
High-resolution, long-term historical data for solar photovoltaic (PV) and concentrated solar power (CSP) potential assessment anywhere on the globe.
A comprehensive, global dataset of hydropower plants, tracking their status, location, and capacity to support analysis of renewable energy infrastructure.
A high-resolution, three-year dataset from a real office building, integrating whole-building/end-use energy consumption, HVAC system data, environmental parameters, and ground-truth occupant counts. Essential for research in smart building energy prediction, optimization, and occupancy analytics.
Comprehensive smart home dataset with 1,048,575 rows and 31 columns including timestamp, device states (TV, oven, lights, fridge) and activity labels for machine learning applications.
Sensor recordings of activities by a single user in a smart home, using PIR, reed switches, force sensors, light, temperature/humidity and smart plugs; includes three CSV files for analysis.
Longitudinal ambient sensor data collected from 4 community homes featuring motion sensors, door sensors, and temperature readings with activity labels. This dataset captures naturalistic behavior patterns for activity recognition model development in real-world smart home environments. Data includes continuous recordings from PIR motion sensors, magnetic door sensors, and ambient temperature sensors with annotated activities by external annotators. The dataset provides a valuable resource for building activity recognition models that operate in uncontrolled, naturalistic settings.
CeTI-Age-Kinematics is a full-body IMU kinematics dataset of 30 daily tasks recorded with a 19-IMU sensor suit in an age-comparative sample of 32 participants (older adults and younger controls), intended for motion analysis and activity recognition research.
GAITEX is a comprehensive multimodal human motion dataset capturing impaired gait and rehabilitation exercises using nine wearable IMUs and optical motion capture systems, designed for biomechanical analysis and rehabilitation monitoring.