15-minute ESP32 environmental and solar-voltage data: 84 training days and 31 validation days in Zapopan, plus a separate Ulm trial; forecasts are one step ahead.
Residential smart-home power dataset with 2,075,259 one-minute measurements from a French household over 47 months. TXT/CSV-style tabular format. Used for load forecasting, NILM, and energy behavior analysis.
Long-duration smart-home utility dataset with two years of minutely electricity, water, and natural gas measurements plus weather and billing data. CSV/TSV/RData formats. Used for forecasting, NILM, and resource analytics.
A comprehensive, high-quality dataset from a network of ground monitoring stations across Saudi Arabia, measuring solar radiation components, wind resources, and related meteorological parameters. Essential for renewable energy feasibility studies, smart grid planning, and machine learning models in energy forecasting.
UrbanAirNet provides a comprehensive collection of urban air quality and weather parameters measured via IoT sensor networks. It includes pollutants like PM2.5, NO2, and O3 alongside meteorological variables.
A real-world household dataset from 13 residential properties in Portugal over nearly three years, with 15-minute resolution measurements of electrical load, solar PV generation, weather parameters, and electricity market prices, published in Nature Scientific Data.
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.
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.
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.
Large-scale real-time air quality monitoring dataset from Dhaka, Bangladesh with 155,406 records. Captures CO, NO2, SO2, O3, PM2.5, and PM10 using IoT sensors with Arduino integration. Ideal for environmental analytics, pollution prediction, and smart city air quality management.