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 optimization of the energy extraction, by operating at the best efficiency point, which can be achieved through optimal control strategies. In particular, wave forecasting models can be valuable for control stra...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-10-01
|
| Series: | IET Renewable Power Generation |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/rpg2.12289 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1839636671368265728 |
|---|---|
| author | Jorge Marques Silva Susana M. Vieira Duarte Valério João C. C. Henriques Paul D. Sclavounos |
| author_facet | Jorge Marques Silva Susana M. Vieira Duarte Valério João C. C. Henriques Paul D. Sclavounos |
| author_sort | Jorge Marques Silva |
| collection | DOAJ |
| description | Abstract The high variability and unpredictability of renewable energy resources require optimization of the energy extraction, by operating at the best efficiency point, which can be achieved through optimal control strategies. In particular, wave forecasting models can be valuable for control strategies in wave energy converter devices. This work intends to exploit the short‐term wave forecasting potential on an oscillating water column equipped with 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 real signal. Regressive linear algorithms were executed for reference. The experimental data was obtained at the Mutriku wave power plant in the Basque Country, Spain. Results have shown LS‐SVM prediction errors varying from 9% to 25%, for horizons ranging from 1 to 3 s in the future. There is no need for extensive training data sets for which computational effort is higher. However, best results were obtained for models with a relatively small number of LS‐SVM features. Regressive models have shown slightly better performance (8–22%) at a significantly lower computational cost. Ultimately, these research findings may play an essential role in model predictive control strategies for the wave power plant. |
| format | Article |
| id | doaj-art-93f605fdf1a24f8e80ebaea9c76c927c |
| institution | Matheson Library |
| issn | 1752-1416 1752-1424 |
| language | English |
| publishDate | 2021-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Renewable Power Generation |
| spelling | doaj-art-93f605fdf1a24f8e80ebaea9c76c927c2025-07-07T09:11:48ZengWileyIET Renewable Power Generation1752-14161752-14242021-10-0115143485350310.1049/rpg2.12289Air pressure forecasting for the Mutriku oscillating‐water‐column wave power plant: Review and case studyJorge Marques Silva0Susana M. Vieira1Duarte Valério2João C. C. Henriques3Paul D. Sclavounos4IDMEC Instituto Superior Técnico Universidade de Lisboa PortugalIDMEC Instituto Superior Técnico Universidade de Lisboa PortugalIDMEC Instituto Superior Técnico Universidade de Lisboa PortugalIDMEC Instituto Superior Técnico Universidade de Lisboa PortugalLaboratory for Ship and Platform Flows Massachusetts Institute of Technology Cambridge Massachusetts USAAbstract The high variability and unpredictability of renewable energy resources require optimization of the energy extraction, by operating at the best efficiency point, which can be achieved through optimal control strategies. In particular, wave forecasting models can be valuable for control strategies in wave energy converter devices. This work intends to exploit the short‐term wave forecasting potential on an oscillating water column equipped with 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 real signal. Regressive linear algorithms were executed for reference. The experimental data was obtained at the Mutriku wave power plant in the Basque Country, Spain. Results have shown LS‐SVM prediction errors varying from 9% to 25%, for horizons ranging from 1 to 3 s in the future. There is no need for extensive training data sets for which computational effort is higher. However, best results were obtained for models with a relatively small number of LS‐SVM features. Regressive models have shown slightly better performance (8–22%) at a significantly lower computational cost. Ultimately, these research findings may play an essential role in model predictive control strategies for the wave power plant.https://doi.org/10.1049/rpg2.12289Probability theory, stochastic processes, and statisticsReliabilityWave powerOptimisation techniquesVelocity, acceleration and rotation controlControl of electric power systems |
| spellingShingle | Jorge Marques Silva Susana M. Vieira Duarte Valério João C. C. Henriques Paul D. Sclavounos Air pressure forecasting for the Mutriku oscillating‐water‐column wave power plant: Review and case study IET Renewable Power Generation Probability theory, stochastic processes, and statistics Reliability Wave power Optimisation techniques Velocity, acceleration and rotation control Control of electric power systems |
| title | Air pressure forecasting for the Mutriku oscillating‐water‐column wave power plant: Review and case study |
| title_full | Air pressure forecasting for the Mutriku oscillating‐water‐column wave power plant: Review and case study |
| title_fullStr | Air pressure forecasting for the Mutriku oscillating‐water‐column wave power plant: Review and case study |
| title_full_unstemmed | Air pressure forecasting for the Mutriku oscillating‐water‐column wave power plant: Review and case study |
| title_short | Air pressure forecasting for the Mutriku oscillating‐water‐column wave power plant: Review and case study |
| title_sort | air pressure forecasting for the mutriku oscillating water column wave power plant review and case study |
| topic | Probability theory, stochastic processes, and statistics Reliability Wave power Optimisation techniques Velocity, acceleration and rotation control Control of electric power systems |
| url | https://doi.org/10.1049/rpg2.12289 |
| work_keys_str_mv | AT jorgemarquessilva airpressureforecastingforthemutrikuoscillatingwatercolumnwavepowerplantreviewandcasestudy AT susanamvieira airpressureforecastingforthemutrikuoscillatingwatercolumnwavepowerplantreviewandcasestudy AT duartevalerio airpressureforecastingforthemutrikuoscillatingwatercolumnwavepowerplantreviewandcasestudy AT joaocchenriques airpressureforecastingforthemutrikuoscillatingwatercolumnwavepowerplantreviewandcasestudy AT pauldsclavounos airpressureforecastingforthemutrikuoscillatingwatercolumnwavepowerplantreviewandcasestudy |