{
    "data_id": "44975",
    "name": "wave_energy",
    "exact_name": "wave_energy",
    "version": 6,
    "version_label": null,
    "description": "**Data Description**\n\nThis 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.\n\nThe data is obtained from an optimization method (blackbox optimization) with the goal of finding the optimal buoys placement.\n\nEach instance represents wave energy returns for different placements of 16 buoys.\n\n**Attribute Description**\n\n1. *x[1-16]* - WECs positions (0-566)\n2. *y[1-16]* - WECs positions (0-566)\n3. *energy[1-16]* - WECs absorbed power (should be ignored if *energy_total* is considered as the target variable)\n4. *energy_total* - total power output of the farm: Powerall, target feature",
    "format": "arff",
    "uploader": "Sebastian Fischer",
    "uploader_id": 30127,
    "visibility": "public",
    "creator": null,
    "contributor": null,
    "date": "2022-12-22 16:14:43",
    "update_comment": null,
    "last_update": "2022-12-22 16:14:43",
    "licence": "CC BY 4.0",
    "status": "active",
    "error_message": null,
    "url": "https:\/\/api.openml.org\/data\/download\/22111839\/file22f16310c401a.arff",
    "default_target_attribute": "energy_total",
    "row_id_attribute": null,
    "ignore_attribute": "\"energy1\",\"energy2\",\"energy3\",\"energy4\",\"energy5\",\"energy6\",\"energy7\",\"energy8\",\"energy9\",\"energy10\",\"energy11\",\"energy12\",\"ener",
    "runs": 0,
    "suggest": {
        "input": [
            "wave_energy",
            "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. 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 i "
        ],
        "weight": 5
    },
    "qualities": {
        "NumberOfInstances": 72000,
        "NumberOfFeatures": 49,
        "NumberOfClasses": 0,
        "NumberOfMissingValues": 0,
        "NumberOfInstancesWithMissingValues": 0,
        "NumberOfNumericFeatures": 49,
        "NumberOfSymbolicFeatures": 0,
        "MajorityClassSize": null,
        "MinorityClassPercentage": null,
        "MinorityClassSize": null,
        "NumberOfBinaryFeatures": 0,
        "PercentageOfBinaryFeatures": 0,
        "PercentageOfInstancesWithMissingValues": 0,
        "AutoCorrelation": -67995.95345178382,
        "PercentageOfMissingValues": 0,
        "Dimensionality": 0.0006805555555555556,
        "PercentageOfNumericFeatures": 100,
        "MajorityClassPercentage": null,
        "PercentageOfSymbolicFeatures": 0
    },
    "tags": [
        {
            "tag": "Machine Learning",
            "uploader": "38960"
        },
        {
            "tag": "Medicine",
            "uploader": "38960"
        },
        {
            "tag": "study_353",
            "uploader": "0"
        }
    ],
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