Review on Deep Learning Research and Applications in Wind and Wave Energy
Wind energy and wave energy are considered to have enormous potential as renewable energy sources in the energy system to make great contributions in transitioning from fossil fuel to renewable energy. However, the uncertain, erratic, and complicated scenarios, as well as the tremendous amount of in...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-02-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/15/4/1510 |
| _version_ | 1797480707235250176 |
|---|---|
| author | Chengcheng Gu Hua Li |
| author_facet | Chengcheng Gu Hua Li |
| author_sort | Chengcheng Gu |
| collection | DOAJ |
| description | Wind energy and wave energy are considered to have enormous potential as renewable energy sources in the energy system to make great contributions in transitioning from fossil fuel to renewable energy. However, the uncertain, erratic, and complicated scenarios, as well as the tremendous amount of information and corresponding parameters, associated with wind and wave energy harvesting are difficult to handle. In the field of big data handing and mining, artificial intelligence plays a critical and efficient role in energy system transition, harvesting and related applications. The derivative method of deep learning and its surrounding prolongation structures are expanding more maturely in many fields of applications in the last decade. Even though both wind and wave energy have the characteristics of instability, more and more applications have implemented using these two renewable energy sources with the support of deep learning methods. This paper systematically reviews and summarizes the different models, methods and applications where the deep learning method has been applied in wind and wave energy. The accuracy and effectiveness of different methods on a similar application were compared. This paper concludes that applications supported by deep learning have enormous potential in terms of energy optimization, harvesting, management, forecasting, behavior exploration and identification. |
| first_indexed | 2024-03-09T22:03:57Z |
| format | Article |
| id | doaj.art-d25e730ff60642728c4675fedee6e489 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T22:03:57Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-d25e730ff60642728c4675fedee6e4892023-11-23T19:45:18ZengMDPI AGEnergies1996-10732022-02-01154151010.3390/en15041510Review on Deep Learning Research and Applications in Wind and Wave EnergyChengcheng Gu0Hua Li1Mechanical and Industrial Engineering Department, Texas A&M University-Kingsville, Kingsville, TX 78363, USAMechanical and Industrial Engineering Department, Texas A&M University-Kingsville, Kingsville, TX 78363, USAWind energy and wave energy are considered to have enormous potential as renewable energy sources in the energy system to make great contributions in transitioning from fossil fuel to renewable energy. However, the uncertain, erratic, and complicated scenarios, as well as the tremendous amount of information and corresponding parameters, associated with wind and wave energy harvesting are difficult to handle. In the field of big data handing and mining, artificial intelligence plays a critical and efficient role in energy system transition, harvesting and related applications. The derivative method of deep learning and its surrounding prolongation structures are expanding more maturely in many fields of applications in the last decade. Even though both wind and wave energy have the characteristics of instability, more and more applications have implemented using these two renewable energy sources with the support of deep learning methods. This paper systematically reviews and summarizes the different models, methods and applications where the deep learning method has been applied in wind and wave energy. The accuracy and effectiveness of different methods on a similar application were compared. This paper concludes that applications supported by deep learning have enormous potential in terms of energy optimization, harvesting, management, forecasting, behavior exploration and identification.https://www.mdpi.com/1996-1073/15/4/1510deep learningwave energywind energylong short-term memory |
| spellingShingle | Chengcheng Gu Hua Li Review on Deep Learning Research and Applications in Wind and Wave Energy Energies deep learning wave energy wind energy long short-term memory |
| title | Review on Deep Learning Research and Applications in Wind and Wave Energy |
| title_full | Review on Deep Learning Research and Applications in Wind and Wave Energy |
| title_fullStr | Review on Deep Learning Research and Applications in Wind and Wave Energy |
| title_full_unstemmed | Review on Deep Learning Research and Applications in Wind and Wave Energy |
| title_short | Review on Deep Learning Research and Applications in Wind and Wave Energy |
| title_sort | review on deep learning research and applications in wind and wave energy |
| topic | deep learning wave energy wind energy long short-term memory |
| url | https://www.mdpi.com/1996-1073/15/4/1510 |
| work_keys_str_mv | AT chengchenggu reviewondeeplearningresearchandapplicationsinwindandwaveenergy AT huali reviewondeeplearningresearchandapplicationsinwindandwaveenergy |