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SML2010数据集  温度、湿度、天气(包含风、雨等)

SML2010数据集 温度、湿度、天气(包含风、雨等)

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Computer Regression

Dr. Francisco Zamora-Martinez, Pablo Romeu-Guallart, Dr. Juan Pardo.francisco.zamora '@' uch.ceu.esEmbedded Syst......

数据结构 ? 202K

    Data Structure ?

    * 以上分析是由系统提取分析形成的结果,具体实际数据为准。

    Dr. Francisco Zamora-Martinez, Pablo Romeu-Guallart, Dr. Juan Pardo.
    francisco.zamora '@'
    Embedded Systems and Artificial Intelligence (ESAI) research group
    Dep. de Ciencias F?-sicas, Matem??ticas y de la Computaci?3n
    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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