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.

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

Variables Table

See description of variables under Variables Table: https://archive.ics.uci.edu/dataset/882/large-scale+wave+energy+farm