Massive dataset from solar panels, wind turbines, and smart grid infrastructure for energy forecasting, demand prediction, and efficiency optimization using big data analytics, machine learning, Hadoop, and Spark distributed processing frameworks.
Detailed energy monitoring dataset from smart home testbed with five common household appliances (refrigerator, washing machine, microwave, air conditioner, TV) each connected to individual smart meters for appliance-level consumption analysis and NILM research.
Multi-resolution smart building energy dataset for forecasting competition with three versions: 1-year at 5-min intervals (v1.0), 40-day at 5-min (v2.0), and 1-day hourly (v3.x), designed to benchmark state-of-the-art energy prediction techniques.
Standardized energy flexibility data model enabling uniform communication of energy flexibility potentials within industrial companies and in exchange with external energy systems (grids, aggregators), supporting demand response and sector coupling.
Multimodal dataset from 24 participants with synchronized wrist-worn PPG from Galaxy Watch 5 and Empatica E4, plus chest ECG from Polar H10, collected during diverse activities in semi-naturalistic settings for evaluating consumer-grade wearable performance.
Large public dataset from Microsoft Research Aurora Project with ECG and PPG signals from wrist-worn wearables, balanced for gender, age, and hypertension status, enabling causal inference research for non-invasive blood pressure prediction with 205 extracted features.
Deep learning-ready dataset combining real vehicle CAN bus traffic and simulated attack scenarios (DoS, fuzzing, spoofing) for training intrusion detection systems to protect autonomous and connected vehicles from cyber-attacks.
Real operational electricity system records from ISO New England (ISONE) covering urban smart city energy activity including demand, generation, pricing, and grid operations across multiple metropolitan areas for energy analytics and forecasting.
Curated collection of 10,000+ realistic IoT sensor readings from Bangladesh representing diverse environmental conditions including temperature, humidity, soil moisture, rainfall, and air quality with timestamps and location tags for smart agriculture and climate research.
AI-IoT-based Smart Drip Irrigation System (SDIS) dataset specifically designed for rice plants considering local agronomic characteristics, featuring soil moisture, weather data, and irrigation control decisions for precision agriculture.
Comprehensive multi-sensor dataset with 9 parameters including environmental (temperature, humidity, light) and soil measurements (moisture, temperature, pH) plus solar battery voltage and water TDS, collected via Arduino-ESP8266 system with cloud integration.
Cold storage monitoring dataset from IoT-enabled system designed for smallholder farmers in Uganda, featuring temperature, humidity, door events, and power status for training predictive models to classify environmental conditions and assess post-harvest food spoilage risk.