Large-scale Wave Energy Farm

Donated on 9/16/2023

Wave energy is a rapidly advancing and promising renewable energy source that holds great potential for addressing the challenges of global warming and climate change. However, optimizing energy output in large wave farms presents a complex problem due to the expensive calculations required to account for hydrodynamic interactions between wave energy converters (WECs). Developing a fast and accurate surrogate model is crucial to overcome these challenges. In light of this, we have compiled an extensive WEC dataset that includes 54,000 and 9,600 configurations involving 49 and 100 WECs, coordination, power, q-factor, and total farm power output. The dataset was derived from a study published at the GECCO conference and received the prestigious Best Paper award. We want to acknowledge the support of the University of Adelaide Phoenix HPC service in conducting this research. For more details, please refer to the following link: https://dl.acm.org/doi/abs/10.1145/3377930.3390235.

Dataset Characteristics

Multivariate

Subject Area

Engineering

Associated Tasks

Regression

Feature Type

Real

# Instances

63600

# Features

149

Dataset Information

For what purpose was the dataset created?

This dataset was created to develop a fast and effective surrogate model for estimating the total power out of the large wave farm accurately.

Who funded the creation of the dataset?

This work was supported by Phoenix HPC service at the University of Adelaide.

What do the instances in this dataset represent?

Each instance represents the coordination of wave energy converters in a wave farm plus the total power output and individual power of each converter and q-factor.

Does the dataset contain data that might be considered sensitive in any way?

No.

Was there any data preprocessing performed?

No.

Has Missing Values?

No

Introductory Paper

Optimisation of large wave farms using a multi-strategy evolutionary framework

By M. Neshat, Bradley Alexander, N. Sergiienko, Markus Wagner. 2020

Published in Annual Conference on Genetic and Evolutionary Computation

Variables Table

Variable NameRoleTypeDescriptionUnitsMissing Values
X1FeatureIntegerno
Y1FeatureIntegerno
X2FeatureIntegerno
Y2FeatureIntegerno
X3FeatureIntegerno
Y3FeatureIntegerno
X4FeatureIntegerno
Y4FeatureIntegerno
X5FeatureIntegerno
Y5FeatureIntegerno

0 to 10 of 149

Additional Variable Information

The dataset includes 4 CSV files for 49 and 100 wave energy converters based on Perth and Sydney wave scenarios. The main goal is predicting the total power output of the wave farm based on the coordination of WECs (X1, Y1, X2, Y2,..., Xn, Yn). As the second plan, predicting the power output of each converter in the wave farm can be interesting.

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Creators

Mehdi Neshat

neshat.mehdi@gmail.com

University of Adelaide

Bradley Alexander

alexander.bradley@adelaide.edu.au

University of Adelaide

Nataliia Sergiienko

nataliia.sergiienko@adelaide.edu.au

University of Adelaide

Markus Wagner

Markus.Wagner@monash.edu

Monash University

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