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SML2010 Data Set
Download: Data Folder, Data Set Description

Abstract: This dataset is collected from a monitor system mounted in a domotic house. It corresponds to approximately 40 days of monitoring data.

Data Set Characteristics:  

Multivariate, Sequential, Time-Series, Text

Number of Instances:




Attribute Characteristics:


Number of Attributes:


Date Donated


Associated Tasks:


Missing Values?


Number of Web Hits:



Dr. Francisco Zamora-Martinez, Pablo Romeu-Guallart, Dr. Juan Pardo.
francisco.zamora '@'
Embedded Systems and Artificial Intelligence (ESAI) research group
Dep. de Ciencias Físicas, Matemáticas y de la Computación
Universidad CEU Cardenal Herrera

Data Set Information:

The dataset could contain missing values. The data was sampled every minute, computing and uploading it smoothed with 15 minute means. The header of the data file is a commentary (begins with #) indicating which data is stored at which column (in Spanish). The data is time information is in UTC.

Attribute Information:

The attributes are:

1. Date: in UTC.
2. Time: in UTC.
3. Indoor temperature (dinning-room), in ºC.
4. Indoor temperature (room), in ºC.
5. Weather forecast temperature, in ºC.
6. Carbon dioxide in ppm (dinning room).
7. Carbon dioxide in ppm (room).
8. Relative humidity (dinning room), in %.
9. Relative humidity (room), in %.
10. Lighting (dinning room), in Lux.
11. Lighting (room), in Lux.
12. Rain, the proportion of the last 15 minutes where rain was detected (a value in range [0,1]).
13. Sun dusk.
14. Wind, in m/s.
15. Sun light in west facade, in Lux.
16. Sun light in east facade, in Lux.
17. Sun light in south facade, in Lux.
18. Sun irradiance, in W/m2.
19. Enthalpic motor 1, 0 or 1 (on-off).
20. Enthalpic motor 2, 0 or 1 (on-off).
21. Enthalpic motor turbo, 0 or 1 (on-off).
22. Outdoor temperature, in ºC.
23. Outdoor relative humidity, in %.
24. Day of the week (computed from the date), 1=Monday, 7=Sunday.

Relevant Papers:


Citation Request:

F. Zamora-Martínez, P. Romeu, P. Botella-Rocamora, J. Pardo, On-line learning of indoor temperature forecasting models towards energy efficiency, Energy and Buildings, Volume 83, November 2014, Pages 162-172, ISSN 0378-7788, [Web Link].

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