Skip to main content

2017 | OriginalPaper | Buchkapitel

Bayesian Optimization of a Hybrid Prediction System for Optimal Wave Energy Estimation Problems

verfasst von : Laura Cornejo-Bueno, Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato, Sancho Salcedo-Sanz

Erschienen in: Advances in Computational Intelligence

Verlag: Springer International Publishing

Aktivieren Sie unsere intelligente Suche um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

In the last years, Bayesian optimization (BO) has emerged as a practical tool for high-quality parameter selection in prediction systems. BO methods are useful for optimizing black-box objective functions that either lack an analytical expression, or are very expensive to evaluate. In this paper we show how BO can be used to obtain optimal parameters of a prediction system for a problem of wave energy flux prediction. Specifically, we propose the Bayesian optimization of a hybrid Grouping Genetic Algorithm with an Extreme Learning Machine (GGA-ELM) approach. The system uses data from neighbor stations (usually buoys) in order to predict the wave energy at a goal marine energy facility. The proposed BO methodology has been tested in a real problem involving buoys data in the Western coast of the USA, improving the performance of the GGA-ELM without a BO approach.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat Arinaga, R.A., Cheung, K.F.: Atlas of global wave energy from 10 years of reanalysis and hindcast data. Renew. Energy 39, 49–64 (2012)CrossRef Arinaga, R.A., Cheung, K.F.: Atlas of global wave energy from 10 years of reanalysis and hindcast data. Renew. Energy 39, 49–64 (2012)CrossRef
2.
Zurück zum Zitat Fadaeenejad, M., Shamsipour, R., Rokni, S.D., Gomes, C.: New approaches in harnessing wave energy: with special attention to small Islands. Renew. Sustain. Energy Rev. 29, 345–354 (2014)CrossRef Fadaeenejad, M., Shamsipour, R., Rokni, S.D., Gomes, C.: New approaches in harnessing wave energy: with special attention to small Islands. Renew. Sustain. Energy Rev. 29, 345–354 (2014)CrossRef
3.
Zurück zum Zitat Hong, Y., Waters, R., Boström, C., Eriksson, M., Engström, J., et al.: Review on electrical control strategies for wave energy converting systems. Renew. Sustain. Energy Rev. 31, 329–342 (2014)CrossRef Hong, Y., Waters, R., Boström, C., Eriksson, M., Engström, J., et al.: Review on electrical control strategies for wave energy converting systems. Renew. Sustain. Energy Rev. 31, 329–342 (2014)CrossRef
4.
Zurück zum Zitat Cuadra, L., Salcedo-Sanz, S., Nieto-Borge, J.C., Alexandre, E., Rodríguez, G.: Computational intelligence in wave energy: comprehensive review and case study. Ren. Sustain. Energ. Rev. 58, 1223–1246 (2016)CrossRef Cuadra, L., Salcedo-Sanz, S., Nieto-Borge, J.C., Alexandre, E., Rodríguez, G.: Computational intelligence in wave energy: comprehensive review and case study. Ren. Sustain. Energ. Rev. 58, 1223–1246 (2016)CrossRef
5.
Zurück zum Zitat Deo, M.C., Naidu, C.S.: Real time wave prediction using neural networks. Ocean Eng. 26(3), 191–203 (1998)CrossRef Deo, M.C., Naidu, C.S.: Real time wave prediction using neural networks. Ocean Eng. 26(3), 191–203 (1998)CrossRef
6.
Zurück zum Zitat Tsai, C.P., Lin, C., Shen, J.N.: Neural network for wave forecasting among multi-stations. Ocean Eng. 29(13), 1683–1695 (2002)CrossRef Tsai, C.P., Lin, C., Shen, J.N.: Neural network for wave forecasting among multi-stations. Ocean Eng. 29(13), 1683–1695 (2002)CrossRef
7.
Zurück zum Zitat Castro, A., Carballo, R., Iglesias, G., Rabuñal, J.R.: Performance of artificial neural networks in nearshore wave power prediction. Appl. Soft Comput. 23, 194–201 (2014)CrossRef Castro, A., Carballo, R., Iglesias, G., Rabuñal, J.R.: Performance of artificial neural networks in nearshore wave power prediction. Appl. Soft Comput. 23, 194–201 (2014)CrossRef
8.
Zurück zum Zitat Zanaganeh, M., Jamshid-Mousavi, S., Etemad-Shahidi, A.F.: A hybrid genetic algorithm-adaptive network-based fuzzy inference system in prediction of wave parameters. Eng. Appl. Artif. Intell. 22(8), 1194–1202 (2009)CrossRef Zanaganeh, M., Jamshid-Mousavi, S., Etemad-Shahidi, A.F.: A hybrid genetic algorithm-adaptive network-based fuzzy inference system in prediction of wave parameters. Eng. Appl. Artif. Intell. 22(8), 1194–1202 (2009)CrossRef
9.
Zurück zum Zitat Alexandre, E., Cuadra, L., Nieto-Borge, J.C., Candil-García, G., del Pino, M., Salcedo-Sanz, S.: A hybrid genetic algorithm - extreme learning machine approach for accurate significant wave height reconstruction. Ocean Model. 92, 115–123 (2015)CrossRef Alexandre, E., Cuadra, L., Nieto-Borge, J.C., Candil-García, G., del Pino, M., Salcedo-Sanz, S.: A hybrid genetic algorithm - extreme learning machine approach for accurate significant wave height reconstruction. Ocean Model. 92, 115–123 (2015)CrossRef
10.
Zurück zum Zitat Cornejo-Bueno, L., Nieto-Borge, J.C., García-Díaz, P., Rodríguez, G., Salcedo-Sanz, S.: Significant wave height and energy flux prediction for marine energy applications: a grouping genetic algorithm - extreme learning machine approach. Renew. Energy 97, 380–389 (2016)CrossRef Cornejo-Bueno, L., Nieto-Borge, J.C., García-Díaz, P., Rodríguez, G., Salcedo-Sanz, S.: Significant wave height and energy flux prediction for marine energy applications: a grouping genetic algorithm - extreme learning machine approach. Renew. Energy 97, 380–389 (2016)CrossRef
11.
Zurück zum Zitat Mahjoobi, J., Mosabbeb, E.A.: Prediction of significant wave height using regressive support vector machines. Ocean Eng. 36(5), 339–347 (2009)CrossRef Mahjoobi, J., Mosabbeb, E.A.: Prediction of significant wave height using regressive support vector machines. Ocean Eng. 36(5), 339–347 (2009)CrossRef
12.
Zurück zum Zitat Salcedo-Sanz, S., Nieto-Borge, J.C., Carro-Calvo, L., Cuadra, L., Hessner, K., Alexandre, E.: Significant wave height estimation using SVR algorithms and shadowing information from simulated and real measured X-band radar images of the sea surface. Ocean Eng. 101, 244–253 (2015)CrossRef Salcedo-Sanz, S., Nieto-Borge, J.C., Carro-Calvo, L., Cuadra, L., Hessner, K., Alexandre, E.: Significant wave height estimation using SVR algorithms and shadowing information from simulated and real measured X-band radar images of the sea surface. Ocean Eng. 101, 244–253 (2015)CrossRef
13.
Zurück zum Zitat Cornejo-Bueno, L., Nieto-Borge, J.C., Alexandre, E., Hessner, K., Salcedo-Sanz, S.: Accurate estimation of significant wave height with support vector regression algorithms and marine radar images. Coast. Eng. 114, 233–243 (2016)CrossRef Cornejo-Bueno, L., Nieto-Borge, J.C., Alexandre, E., Hessner, K., Salcedo-Sanz, S.: Accurate estimation of significant wave height with support vector regression algorithms and marine radar images. Coast. Eng. 114, 233–243 (2016)CrossRef
14.
Zurück zum Zitat Fernández, J.C., Salcedo-Sanz, S., Gutiérrez, P.A., Alexandre, E., Hervás-Martínez, C.: Significant wave height and energy flux range forecast with machine learning classifiers. Eng. Appl. Artif. Intell. 43, 44–53 (2015)CrossRef Fernández, J.C., Salcedo-Sanz, S., Gutiérrez, P.A., Alexandre, E., Hervás-Martínez, C.: Significant wave height and energy flux range forecast with machine learning classifiers. Eng. Appl. Artif. Intell. 43, 44–53 (2015)CrossRef
15.
Zurück zum Zitat Nitsure, S.P., Londhe, S.N., Khare, K.C.: Wave forecasts using wind information and genetic programming. Ocean Eng. 54, 61–69 (2012)CrossRef Nitsure, S.P., Londhe, S.N., Khare, K.C.: Wave forecasts using wind information and genetic programming. Ocean Eng. 54, 61–69 (2012)CrossRef
16.
Zurück zum Zitat Özger, M.: Prediction of ocean wave energy from meteorological variables by fuzzy logic modeling. Expert Syst. Appl. 38(5), 6269–6274 (2011)CrossRef Özger, M.: Prediction of ocean wave energy from meteorological variables by fuzzy logic modeling. Expert Syst. Appl. 38(5), 6269–6274 (2011)CrossRef
17.
Zurück zum Zitat Goda, Y.: Random Seas and Design of Maritime Structures. World Scientific, Singapore (2010)CrossRefMATH Goda, Y.: Random Seas and Design of Maritime Structures. World Scientific, Singapore (2010)CrossRefMATH
18.
Zurück zum Zitat Falkenauer, E.: The grouping genetic algorithm-widening the scope of the GAs. Belg. J. Oper. Res. Stat. Comput. Sci. 33, 79–102 (1992)MATH Falkenauer, E.: The grouping genetic algorithm-widening the scope of the GAs. Belg. J. Oper. Res. Stat. Comput. Sci. 33, 79–102 (1992)MATH
19.
Zurück zum Zitat Yao, X., Liu, Y., Lin, G.: Evolutionary programming made faster. IEEE Trans. Evol. Comput. 3(2), 82–102 (1999)CrossRef Yao, X., Liu, Y., Lin, G.: Evolutionary programming made faster. IEEE Trans. Evol. Comput. 3(2), 82–102 (1999)CrossRef
20.
Zurück zum Zitat Huang, G.B., Zhu, Q.Y.: Extreme learning machine: theory and applications. Neurocomputing 70, 489–501 (2006)CrossRef Huang, G.B., Zhu, Q.Y.: Extreme learning machine: theory and applications. Neurocomputing 70, 489–501 (2006)CrossRef
22.
Zurück zum Zitat Snoek, J., Hugo L., Adams, R.P.: Practical Bayesian optimization of machine learning algorithms. In: Advances in neural information processing systems (2012) Snoek, J., Hugo L., Adams, R.P.: Practical Bayesian optimization of machine learning algorithms. In: Advances in neural information processing systems (2012)
23.
Zurück zum Zitat Mockus, J., Tiesis, V., Zilinskas, A.: The application of Bayesian methods for seeking the extremum. Towards Glob. Optim. 2(117–129), 2 (1978)MATH Mockus, J., Tiesis, V., Zilinskas, A.: The application of Bayesian methods for seeking the extremum. Towards Glob. Optim. 2(117–129), 2 (1978)MATH
24.
Zurück zum Zitat Brochu, E., Cora, V.M., De Freitas, N.: A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. arXiv preprint arXiv:1012.2599 (2010) Brochu, E., Cora, V.M., De Freitas, N.: A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. arXiv preprint arXiv:​1012.​2599 (2010)
25.
Zurück zum Zitat Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the human out of the loop: a review of Bayesian optimization. Proc. IEEE 104, 148–175 (2016)CrossRef Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., de Freitas, N.: Taking the human out of the loop: a review of Bayesian optimization. Proc. IEEE 104, 148–175 (2016)CrossRef
26.
Zurück zum Zitat Rasmussen, C.E.: Gaussian processes for machine learning (2006) Rasmussen, C.E.: Gaussian processes for machine learning (2006)
27.
Zurück zum Zitat Jones, D.R., Schonlau, M., Welch, W.J.: Efficient global optimization of expensive black-box functions. J. Global Optim. 13(4), 455–492 (1998)MathSciNetCrossRefMATH Jones, D.R., Schonlau, M., Welch, W.J.: Efficient global optimization of expensive black-box functions. J. Global Optim. 13(4), 455–492 (1998)MathSciNetCrossRefMATH
29.
Zurück zum Zitat Albert, A.: Regression and the Moore-Penrose pseudoinverse (No. 519.536) (1972) Albert, A.: Regression and the Moore-Penrose pseudoinverse (No. 519.536) (1972)
30.
31.
Zurück zum Zitat Salcedo-Sanz, S., Rojo, J.L., Martínez-Ramón, M., Camps-Valls, G.: Support vector machines in engineering: an overview. WIREs Data Mining Knowl. Discov. 4(3), 234–267 (2014)CrossRef Salcedo-Sanz, S., Rojo, J.L., Martínez-Ramón, M., Camps-Valls, G.: Support vector machines in engineering: an overview. WIREs Data Mining Knowl. Discov. 4(3), 234–267 (2014)CrossRef
Metadaten
Titel
Bayesian Optimization of a Hybrid Prediction System for Optimal Wave Energy Estimation Problems
verfasst von
Laura Cornejo-Bueno
Eduardo C. Garrido-Merchán
Daniel Hernández-Lobato
Sancho Salcedo-Sanz
Copyright-Jahr
2017
DOI
https://doi.org/10.1007/978-3-319-59153-7_56