Summary: Experimental data used to create regression models of appliances energy use in a low energy building.
| Parameter | Value |
|---|---|
| Name | Appliances Energy Prediction |
| Labeled | No |
| Time Series | Yes |
| Simulation | No |
| Missing Values | No |
| Dataset Characteristics | Multivariate, Time-Series |
| Feature Type | Real |
| Associated Tasks | Regression |
| Number of Instances | 19735 |
| Number of Features | 28 |
| Date Donated | 2017-02-14 |
| Source | UCI Machine Learning Repository |
The data set is at 10 min for about 4.5 months. The house temperature and humidity conditions were monitored with a ZigBee wireless sensor network. Each wireless node transmitted the temperature and humidity conditions around 3.3 min. Then, the wireless data was averaged for 10 minutes periods. The energy data was logged every 10 minutes with m-bus energy meters. Weather from the nearest airport weather station (Chievres Airport, Belgium) was downloaded from a public data set from Reliable Prognosis (rp5.ru), and merged together with the experimental data sets using the date and time column. Two random variables have been included in the data set for testing the regression models and to filter out non predictive attributes (parameters).
Indoor environment monitoring, ZigBee wireless network, Temperature data, Humidity data, Weather integration, Energy consumption, M-bus energy meters, Airport weather station