Data
wave_energy

wave_energy

active ARFF CC BY 4.0 Visibility: public Uploaded 22-12-2022 by Sebastian Fischer
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  • Machine Learning Medicine study_353
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Data Description This data set consists of positions and absorbed power outputs of wave energy converters (WECs) in four real wave scenarios from the southern coast of Australia. The data is obtained from an optimization method (blackbox optimization) with the goal of finding the optimal buoys placement. Each instance represents wave energy returns for different placements of 16 buoys. Attribute Description 1. *x[1-16]* - WECs positions (0-566) 2. *y[1-16]* - WECs positions (0-566) 3. *energy[1-16]* - WECs absorbed power (should be ignored if *energy_total* is considered as the target variable) 4. *energy_total* - total power output of the farm: Powerall, target feature

49 features

energy_total (target)numeric71993 unique values
0 missing
x1numeric58248 unique values
0 missing
x2numeric60168 unique values
0 missing
x3numeric60223 unique values
0 missing
x4numeric59910 unique values
0 missing
x5numeric58153 unique values
0 missing
x6numeric59301 unique values
0 missing
x7numeric60758 unique values
0 missing
x8numeric60915 unique values
0 missing
x9numeric60305 unique values
0 missing
x10numeric59705 unique values
0 missing
x11numeric58960 unique values
0 missing
x12numeric57550 unique values
0 missing
x13numeric58693 unique values
0 missing
x14numeric59247 unique values
0 missing
x15numeric60182 unique values
0 missing
x16numeric59851 unique values
0 missing
y1numeric58489 unique values
0 missing
y2numeric60861 unique values
0 missing
y3numeric60607 unique values
0 missing
y4numeric60573 unique values
0 missing
y5numeric60032 unique values
0 missing
y6numeric60328 unique values
0 missing
y7numeric60907 unique values
0 missing
y8numeric60606 unique values
0 missing
y9numeric59689 unique values
0 missing
y10numeric59838 unique values
0 missing
y11numeric59162 unique values
0 missing
y12numeric59248 unique values
0 missing
y13numeric59798 unique values
0 missing
y14numeric60547 unique values
0 missing
y15numeric61057 unique values
0 missing
y16numeric61036 unique values
0 missing
energy1numeric71985 unique values
0 missing
energy2numeric71981 unique values
0 missing
energy3numeric71986 unique values
0 missing
energy4numeric71982 unique values
0 missing
energy5numeric71988 unique values
0 missing
energy6numeric71986 unique values
0 missing
energy7numeric71983 unique values
0 missing
energy8numeric71986 unique values
0 missing
energy9numeric71980 unique values
0 missing
energy10numeric71983 unique values
0 missing
energy11numeric71987 unique values
0 missing
energy12numeric71987 unique values
0 missing
energy13numeric71982 unique values
0 missing
energy14numeric71985 unique values
0 missing
energy15numeric71984 unique values
0 missing
energy16numeric71983 unique values
0 missing

19 properties

72000
Number of instances (rows) of the dataset.
49
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.
49
Number of numeric attributes.
0
Number of nominal attributes.
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.
-67995.95
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.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.

1 tasks

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