Air pressure forecasting for the Mutriku oscillating‐water‐column wave power plant: Review and case study

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.