Short- and long-term energy flux prediction using Multi-Task Evolutionary Artificial Neural Networks

Research areas:

• Applications - Weather forecasting
• Applications - Evolutionary computation software
• Methodology - Multiobjective evolutionary algorithms
• Methodology - Machine learning
• Applications - Renewable Energy

Year:

2020

Type of Publication:

Article

Keywords:

Ocean Engineering, Flux of energy prediction

Authors:

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

Journal:

Ocean Engineering

Volume:

216

Month:

108089

ISSN:

0029-8018

BibTex:

@article{OceanEngineeringGuijo2020, author = "David Guijo-Rubio and Antonio Manuel G{\'o}mez-Orellana and Pedro Antonio Guti{\'e}rrez and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "This paper presents a novel approach to tackle simultaneously short- and long-term energy flux prediction (specifically, at 6h, 12h, 24h and 48h time horizons). The methodology proposed is based on the Multi-Task Learning paradigm in order to solve the four problems with a single model. We consider Multi-Task Evolutionary Artificial Neural Networks (MTEANN) with four outputs, one for each time prediction horizon. For this purpose, three buoys located at the Gulf of Alaska are considered. Measurements collected by these buoys are used to obtain the target values of energy flux, whereas, only reanalysis data are used as input values, allowing the applicability to other locations. The performance of three different basis functions (Sigmoidal Unit, Radial Basis Function and Product Unit) are compared against some popular stateof-the-art approaches such as Extreme Learning Machines and Support Vector Regressors. The results show that MTEANN methodology using Sigmoidal Units in the hidden layer and a linear output achieves the best performance. In this way, the multi-task methodology is an excellent and lower-complexity approach for energy flux prediction at both short- and long-term prediction time horizons. Furthermore, the results also confirm that reanalysis data is enough for describing well the problem tackled.", awards = "JCR(2020): 3.795 Position: 1/16 (Q1) Category: MARINE ENGINEERING", comments = "JCR(2020): 3.795 Position: 1/16 (Q1) Category: MARINE ENGINEERING", doi = "10.1016/j.oceaneng.2020.108089", issn = "0029-8018", journal = "Ocean Engineering", keywords = "Ocean Engineering, Flux of energy prediction", month = "108089", note = "JCR(2020): 3.795 Position: 1/16 (Q1) Category: MARINE ENGINEERING", title = "{S}hort- and long-term energy flux prediction using {M}ulti-{T}ask {E}volutionary {A}rtificial {N}eural {N}etworks", url = "doi.org/10.1016/j.oceaneng.2020.108089", volume = "216", year = "2020", }

Note:

JCR(2020): 3.795 Position: 1/16 (Q1) Category: MARINE ENGINEERING

Abstract:

This paper presents a novel approach to tackle simultaneously short- and long-term energy flux prediction (specifically, at 6h, 12h, 24h and 48h time horizons). The methodology proposed is based on the Multi-Task Learning paradigm in order to solve the four problems with a single model. We consider Multi-Task Evolutionary Artificial Neural Networks (MTEANN) with four outputs, one for each time prediction horizon. For this purpose, three buoys located at the Gulf of Alaska are considered. Measurements collected by these buoys are used to obtain the target values of energy flux, whereas, only reanalysis data are used as input values, allowing the applicability to other locations. The performance of three different basis functions (Sigmoidal Unit, Radial Basis Function and Product Unit) are compared against some popular stateof-the-art approaches such as Extreme Learning Machines and Support Vector Regressors. The results show that MTEANN methodology using Sigmoidal Units in the hidden layer and a linear output achieves the best performance. In this way, the multi-task methodology is an excellent and lower-complexity approach for energy flux prediction at both short- and long-term prediction time horizons. Furthermore, the results also confirm that reanalysis data is enough for describing well the problem tackled.

Comments:

JCR(2020): 3.795 Position: 1/16 (Q1) Category: MARINE ENGINEERING