Wave Energy Converters
Donated on 6/29/2019
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.
Dataset Characteristics
Multivariate
Subject Area
Computer Science
Associated Tasks
Regression
Feature Type
Real
# Instances
288000
# Features
49
Dataset Information
Additional Information
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 (Sydney, Adelaide, Perth and Tasmania). The applied converter model is a fully submerged three-tether converter called CETO [1]. 16 WECs locations are placed and optimized in a size-constrained environment. In terms of optimization, the problem is categorised as an expensive optimization problem that each farm evaluation takes several minutes. The results are derived from several popular and successful Evolutionary optimization methods that are published in [2,3]. The source code of the applied hydrodynamic simulator [4] is available by the below link: https://au.mathworks.com/matlabcentral/fileexchange/71840-wave-energy-converter-wec-array-simulator
Has Missing Values?
No
Variable Information
Attribute: Attribute Range 1. WECs position {X1, X2, …, X16; Y1, Y2,…, Y16} continuous from 0 to 566 (m). 2. WECs absorbed power: {P1, P2, …, P16} 3. Total power output of the farm: Powerall
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pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset wave_energy_converters = fetch_ucirepo(id=534) # data (as pandas dataframes) X = wave_energy_converters.data.features y = wave_energy_converters.data.targets # metadata print(wave_energy_converters.metadata) # variable information print(wave_energy_converters.variables)
Wave Energy Converters. (2019). UCI Machine Learning Repository. https://doi.org/10.24432/C5QS4V.
@misc{misc_wave_energy_converters_534,
title = {{Wave Energy Converters}},
year = {2019},
howpublished = {UCI Machine Learning Repository},
note = {{DOI}: https://doi.org/10.24432/C5QS4V}
}DOI
Notes
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.