Air pressure forecasting for the Mutriku oscillating‐water‐column wave power plant: Review and case study
Name
IET Renewable Power Gen - 2021 - Marques Silva - Air pressure forecasting for the Mutriku oscillating‐water‐column wave.pdf
Description
Published version
Size
1.99 MB
Format
Adobe PDF
Checksum (MD5)
80050fc717dedb6d6381c8ccef814885
Author(s)
Marques Silva, Jorge
Vieira, Susana M
Valério, Duarte
Henriques, João CC
Sclavounos, Paul D
Date Issued
October 2021
Journal
IET Renewable Power Generation
Publisher
Institution of Engineering and Technology (IET)
Citation
Marques Silva, Jorge, Vieira, Susana M, Valério, Duarte, Henriques, João CC and Sclavounos, Paul D. 2021. "Air pressure forecasting for the Mutriku oscillating‐water‐column wave power plant: Review and case study." IET Renewable Power Generation, 15 (14).
Version
Final published version
Abstract
The high variability and unpredictability of renewable energy resources require optimiza-tion of the energy extraction, by operating at the best efficiency point, which can beachieved through optimal control strategies. In particular, wave forecasting models canbe valuable for control strategies in wave energy converter devices. This work intends toexploit the short-term wave forecasting potential on an oscillating water column equippedwith the innovative biradial turbine. A Least Squares Support Vector Machine (LS-SVM)algorithm was developed to predict the air chamber pressure and compare it to the realsignal. Regressive linear algorithms were executed for reference. The experimental datawas obtained at the Mutriku wave power plant in the Basque Country, Spain. Results haveshown LS-SVM prediction errors varying from 9% to 25%, for horizons ranging from 1 to3 s in the future. There is no need for extensive training data sets for which computationaleffort is higher. However, best results were obtained for models with a relatively smallnumber of LS-SVM features. Regressive models have shown slightly better performance(8–22%) at a significantly lower computational cost. Ultimately, these research findings mayplay an essential role in model predictive control strategies for the wave power plant.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1049/rpg2.12289
