Smart Home Dataset with Weather Information for Energy Monitoring
Catalog Summary
"Comprehensive energy consumption dataset from a smart home with detailed weather information, containing readings from 17 different appliances and devices with 13 weather parameters for advanced consumption forecasting."
Catalog Notes
Dataset Overview
A highly detailed dataset combining smart home energy consumption from 17 different appliances and devices with comprehensive weather information (13 weather parameters). The dataset provides minute-level granularity for understanding the relationship between energy usage patterns and environmental conditions.
Key Features
- 139.5 MB of detailed energy consumption data
- 17 different home appliances and areas monitored
- 13 comprehensive weather parameters
- Minute-level time series data for high-resolution analysis
- Solar power generation tracking
- Multi-year data collection period
Data Structure/Columns
The dataset contains 33 columns organized into three main categories:
Energy Consumption (19 columns):
- use [kW]: Total energy consumption
- gen [kW]: Energy generated from solar
- House overall [kW]: Overall house consumption
- Dishwasher [kW]: Dishwasher power usage
- Furnace 1 [kW]: First furnace consumption
- Furnace 2 [kW]: Second furnace consumption
- Home office [kW]: Home office consumption
- Fridge [kW]: Refrigerator power usage
- Wine cellar [kW]: Wine cellar consumption
- Garage door [kW]: Garage door power usage
- Kitchen 12 [kW]: Kitchen circuit 12
- Kitchen 14 [kW]: Kitchen circuit 14
- Kitchen 38 [kW]: Kitchen circuit 38
- Barn [kW]: Barn power consumption
- Well [kW]: Water well pump consumption
- Microwave [kW]: Microwave power usage
- Living room [kW]: Living room consumption
- Solar [kW]: Solar power generation
Weather Parameters (13 columns):
- temperature: Outdoor temperature
- icon: Weather condition icon/description
- humidity: Relative humidity
- visibility: Visibility distance
- summary: Weather summary description
- apparentTemperature: Feels-like temperature
- pressure: Atmospheric pressure
- windSpeed: Wind speed
- cloudCover: Cloud coverage percentage
- windBearing: Wind direction
- precipIntensity: Precipitation intensity
- dewPoint: Dew point temperature
- precipProbability: Probability of precipitation
Temporal:
- time: Timestamp (minute-level)
Data Collection Method
Data was collected using IoT energy monitoring sensors installed on individual circuits and appliances throughout the home, integrated with a weather API service providing comprehensive meteorological data synchronized with energy readings at minute-level intervals.
Research Applications
- Multi-appliance energy consumption forecasting
- Weather impact analysis on energy usage patterns
- Anomaly detection in individual appliance consumption
- Smart home automation and optimization strategies
- Solar energy integration and grid interaction analysis
- Load disaggregation and Non-Intrusive Load Monitoring (NILM)
- Demand response and peak load management
Machine Learning Use Cases
- Multi-variate time series forecasting with weather features
- Regression models for energy prediction per appliance
- LSTM/RNN models for temporal sequence prediction
- Clustering for usage pattern identification across devices
- Change point detection for abnormal consumption
- Feature importance analysis for weather vs usage correlation
- Transfer learning for similar smart home deployments
Data Preview
| time | use [kW] | gen [kW] | House overall [kW] | Dishwasher [kW] | Furnace 1 [kW] | Home office [kW] | Fridge [kW] | Solar [kW] | temperature | humidity |
|---|---|---|---|---|---|---|---|---|---|---|
| 2016-01-01 05:00:00 | 0.758 | 0.000 | 0.758 | 0.000 | 0.000 | 0.019 | 0.079 | 0.000 | 6.2 | 0.83 |
| 2016-01-01 05:01:00 | 0.726 | 0.000 | 0.726 | 0.000 | 0.000 | 0.017 | 0.079 | 0.000 | 6.2 | 0.83 |
| 2016-01-01 05:02:00 | 0.752 | 0.000 | 0.752 | 0.000 | 0.000 | 0.019 | 0.079 | 0.000 | 6.1 | 0.83 |
Showing first few rows for preview
Provided Citation
Taranveer Singh, Smart Home Dataset with Weather Information, Kaggle, 2019.
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Source metadata: Kaggle (2026)
Indexed by IoTDataset.com on Jan 16, 2026
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