Simultaneous short-term significant wave height and energy flux prediction using zonal multi-task evolutionary artificial neural networks

Research areas:

• Applications - Renewable Energy

Year:

2022

Type of Publication:

Article

Keywords:

Wave height prediction, Energy flux prediction, Marine energy, Multi-task machine learning, Zonal models, Evolutionary artificial neural networks

Authors:

• Gómez-Orellana, Antonio Manuel
• Guijo-Rubio, David
• Gutiérrez, Pedro Antonio
• Hervás-Martínez, César

Journal:

Renewable Energy

Volume:

184

Pages:

975989

Month:

January

ISSN:

0960-1481

BibTex:

@article{ZonalMultiTaskEANN2022, author = "Antonio Manuel G{\'o}mez-Orellana and David Guijo-Rubio and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "The prediction of wave height and flux of energy is essential for most ocean engineering applications. To simultaneously predict both wave parameters, this paper presents a novel approach using short-term time prediction horizons (6h and 12h). Specifically, the methodology proposed presents a twofold simultaneity: 1) both parameters are predicted by a single model, applying the multi-task learning paradigm, and 2) the prediction tasks are tackled for several neighbouring ocean buoys with such single model by the development of a zonal strategy. Multi-Task Evolutionary Artificial Neural Network (MTEANN) models are applied to two different zones located in the United States, considering measurements collected by three buoys in each zone. Zonal MTEANN models have been compared in a two-phased procedure: 1) against the three individual MTEANN models specifically trained for each buoy of the zone, and 2) against some state-of-the-art regression techniques. Results achieved show that the proposed zonal methodology obtains not only better performance than the individual MTEANN models, but it also requires a lower number of connections. Besides, the zonal MTEANN methodology outperforms state-of-the-art regression techniques. Hence, the proposed approach results in an excellent method for predicting both significant wave height and flux of energy at short-term prediction time horizons.", awards = "JCR(2022): 8.7 Position: 26/115 (Q1) Category: ENERGY {\&}amp; FUELS", comments = "JCR(2022): 8.7 Position: 26/115 (Q1) Category: ENERGY {\&}amp; FUELS", doi = "10.1016/j.renene.2021.11.122", issn = "0960-1481", journal = "Renewable Energy", keywords = "Wave height prediction, Energy flux prediction, Marine energy, Multi-task machine learning, Zonal models, Evolutionary artificial neural networks", month = "January", note = "JCR(2022): 8.7 Position: 26/115 (Q1) Category: ENERGY {\&}amp; FUELS", pages = "975989", title = "{S}imultaneous short-term significant wave height and energy flux prediction using zonal multi-task evolutionary artificial neural networks", url = "doi.org/10.1016/j.renene.2021.11.122", volume = "184", year = "2022", }

Note:

JCR(2022): 8.7 Position: 26/115 (Q1) Category: ENERGY & FUELS

Abstract:

The prediction of wave height and flux of energy is essential for most ocean engineering applications. To simultaneously predict both wave parameters, this paper presents a novel approach using short-term time prediction horizons (6h and 12h). Specifically, the methodology proposed presents a twofold simultaneity: 1) both parameters are predicted by a single model, applying the multi-task learning paradigm, and 2) the prediction tasks are tackled for several neighbouring ocean buoys with such single model by the development of a zonal strategy. Multi-Task Evolutionary Artificial Neural Network (MTEANN) models are applied to two different zones located in the United States, considering measurements collected by three buoys in each zone. Zonal MTEANN models have been compared in a two-phased procedure: 1) against the three individual MTEANN models specifically trained for each buoy of the zone, and 2) against some state-of-the-art regression techniques. Results achieved show that the proposed zonal methodology obtains not only better performance than the individual MTEANN models, but it also requires a lower number of connections. Besides, the zonal MTEANN methodology outperforms state-of-the-art regression techniques. Hence, the proposed approach results in an excellent method for predicting both significant wave height and flux of energy at short-term prediction time horizons.

Comments:

JCR(2022): 8.7 Position: 26/115 (Q1) Category: ENERGY & FUELS