Significant wave height and energy flux range forecast with machine learning classifiers

Research areas:

• Methodology - Machine learning

Year:

2015

Type of Publication:

Article

Keywords:

Wave energy prediction, Ordinal classification, Multi-class classification, Significant wave height, Flux of energy, Wave energy converters

Authors:

• Fernández, Juan Carlos
• Salcedo-Sanz, Sancho
• Gutiérrez, Pedro Antonio
• Alexandre, E.
• Hervás-Martínez, César

Journal:

Engineering Applications of Artificial Intelligence

Volume:

43

Pages:

44-53

ISSN:

0952-1976

BibTex:

@article{EAAI_Waves2015, author = "Juan Carlos Fern{\'a}ndez and Sancho Salcedo-Sanz and Pedro Antonio Guti{\'e}rrez and E. Alexandre and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "In this paper, the performance of different ordinal and nominal multi-class classifiers is evaluated, in a problem of wave energy range prediction using meteorological variables from numerical models. This prediction could be used in problems of wave energy conversion in renewable and sustainable systems for energy supply. Specifically, the work is focused on ordinal classifiers, that have provided excellent performance in previous applications. The proposed techniques are novel with respect to alternative classification and regression techniques used up to date, the former not considering the order relation between classes in a multi-class problem and the latter needing, in general, more complex models. Another important novelty of the paper is to consider meteorological variables from numerical models as inputs of the classifiers, which has not been done before, to our knowledge, in this context. For this, a data matching is carried out between meteorological data, obtained from NCEP/NCAR Reanalysis Project in four points around the two buoys subjected to study (a buoy in the Gulf of Alaska and another one in the Southeast of United States), and the wave height or wave period collected by sensors in each buoy. Using this matching, the problem is tackled as an ordinal multi-class classification problem and the objective is to predict the range of height of the wave produced in each buoy and the range of energy flux generated. The classifiers to be compared and the model proposed are fully evaluated in both buoys. The results obtained are promising, showing an acceptable reconstruction by ordinal methods with respect to nominal ones in terms of wave height and energy flux. ", awards = "JCR(2015): 2.368 Position: 32/130 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", comments = "JCR(2015): 2.368 Position: 32/130 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", doi = "10.1016/j.engappai.2015.03.012", issn = "0952-1976", journal = "Engineering Applications of Artificial Intelligence", keywords = "Wave energy prediction, Ordinal classification, Multi-class classification, Significant wave height, Flux of energy, Wave energy converters", note = "JCR(2015): 2.368 Position: 32/130 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE", pages = "44-53", title = "{S}ignificant wave height and energy flux range forecast with machine learning classifiers", url = "http://dx.doi.org/10.1016/j.engappai.2015.03.012", volume = "43", year = "2015", }

Note:

JCR(2015): 2.368 Position: 32/130 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Abstract:

In this paper, the performance of different ordinal and nominal multi-class classifiers is evaluated, in a problem of wave energy range prediction using meteorological variables from numerical models. This prediction could be used in problems of wave energy conversion in renewable and sustainable systems for energy supply. Specifically, the work is focused on ordinal classifiers, that have provided excellent performance in previous applications. The proposed techniques are novel with respect to alternative classification and regression techniques used up to date, the former not considering the order relation between classes in a multi-class problem and the latter needing, in general, more complex models. Another important novelty of the paper is to consider meteorological variables from numerical models as inputs of the classifiers, which has not been done before, to our knowledge, in this context. For this, a data matching is carried out between meteorological data, obtained from NCEP/NCAR Reanalysis Project in four points around the two buoys subjected to study (a buoy in the Gulf of Alaska and another one in the Southeast of United States), and the wave height or wave period collected by sensors in each buoy. Using this matching, the problem is tackled as an ordinal multi-class classification problem and the objective is to predict the range of height of the wave produced in each buoy and the range of energy flux generated. The classifiers to be compared and the model proposed are fully evaluated in both buoys. The results obtained are promising, showing an acceptable reconstruction by ordinal methods with respect to nominal ones in terms of wave height and energy flux.

Comments:

JCR(2015): 2.368 Position: 32/130 (Q1) Category: COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE