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appliances_energy_prediction

appliances_energy_prediction

active ARFF CC BY 4.0 Visibility: public Uploaded 23-07-2024 by Bruno Belucci Teixeira
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From original source: ----- Additional Information 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). For more information about the house, data collection, R scripts and figures, please refer to the paper and to the following github repository: https://github.com/LuisM78/Appliances-energy-prediction-data Has Missing Values? No ----- Columns with index 0 were deleted from the dataset, usually because they related to some kind of index.

28 features

Appliances (target)numeric92 unique values
0 missing
lightsnumeric8 unique values
0 missing
T1numeric722 unique values
0 missing
RH_1numeric2547 unique values
0 missing
T2numeric1650 unique values
0 missing
RH_2numeric3376 unique values
0 missing
T3numeric1426 unique values
0 missing
RH_3numeric2618 unique values
0 missing
T4numeric1390 unique values
0 missing
RH_4numeric2987 unique values
0 missing
T5numeric2263 unique values
0 missing
RH_5numeric7571 unique values
0 missing
T6numeric4446 unique values
0 missing
RH_6numeric9709 unique values
0 missing
T7numeric1955 unique values
0 missing
RH_7numeric5891 unique values
0 missing
T8numeric2228 unique values
0 missing
RH_8numeric6649 unique values
0 missing
T9numeric924 unique values
0 missing
RH_9numeric3388 unique values
0 missing
T_outnumeric1730 unique values
0 missing
Press_mm_hgnumeric2189 unique values
0 missing
RH_outnumeric566 unique values
0 missing
Windspeednumeric189 unique values
0 missing
Visibilitynumeric413 unique values
0 missing
Tdewpointnumeric1409 unique values
0 missing
rv1numeric19735 unique values
0 missing
rv2numeric19735 unique values
0 missing

19 properties

19735
Number of instances (rows) of the dataset.
28
Number of attributes (columns) of the dataset.
0
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
28
Number of numeric attributes.
0
Number of nominal attributes.
-28.57
Average class difference between consecutive instances.
0
Percentage of missing values.
0
Number of attributes divided by the number of instances.
100
Percentage of numeric attributes.
0
Percentage of nominal attributes.
Percentage of instances belonging to the most frequent class.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.

1 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: Appliances
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