Abstract
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.
Highlights
Renewable energy has been catching researchers’ eyes for many years, and its exploitation, harvesting, energy management, efficiency improvement and applications have been the concentration in the energy research fields
With the increasing numbers of publications on wind and wave energy based on the deep learning method, a review paper is needed for recently published research works, which could provide a good insight for exploring any innovation method through comparison of current research
As Chen et al [29] explained in their article, the convolution neural network (CNN) module worked as a feature extractor; it translated the raw data into intrinsic deep features
Summary
Renewable energy has been catching researchers’ eyes for many years, and its exploitation, harvesting, energy management, efficiency improvement and applications have been the concentration in the energy research fields. With the increasing numbers of publications on wind and wave energy based on the deep learning method, a review paper is needed for recently published research works, which could provide a good insight for exploring any innovation method through comparison of current research. This paper summarizes the different deep leaning-based models implemented in wind and wave energy applications and compares the different models in similar applications through datasets, a preprocessing method, model structures, computing time and accuracy in tabular forms. The literature review of current research in wave and wind energy application will provide a valuable evaluation of available datasets from existing case studies, which is a necessary support for future research in similar fields and research directions to explore the effectiveness and efficiency of potential in different applications
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