Significant wave height prediction through artificial intelligent mode decomposition for wave energy management

Chen, Yaoran; Zhang, Dan; Li, Xiaowei; Yan, Ping; Wu, Chuhan; Pu, Huayan; Dai, Zhijun; Cao, Yong; Zhang, Jiujun

doi:10.1016/j.egyai.2023.100257

Cited by 8 publications

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Forecasting of significant wave height is an important element for wave energy management and requires heavy computational power in conventional numerical simulation methods. ANNs have found applications in this field, with recent advances including empirical mode decomposition techniques and transformer-based encoders [81]. LSTM has been used for the prediction of power generation from wave energy converters and has been shown to be faster and more accurate than the utilization of numerical simulations [82].…”

Section: Marine Power Forecastingmentioning

confidence: 99%

Artificial Intelligence for Management of Variable Renewable Energy Systems: A Review of Current Status and Future Directions

Yousef,

Rocha-Meneses

2023

Energies

View full text Add to dashboard Cite

This review paper provides a summary of methods in which artificial intelligence (AI) techniques have been applied in the management of variable renewable energy (VRE) systems, and an outlook to future directions of research in the field. The VRE types included are namely solar, wind and marine varieties. AI techniques, and particularly machine learning (ML), have gained traction as a result of data explosion, and offer a method for integration of multimodal data for more accurate forecasting in energy applications. The VRE management aspects in which AI techniques have been applied include optimized power generation forecasting and integration of VRE into power grids, including the aspects of demand forecasting, energy storage, system optimization, performance monitoring, and cost management. Future directions of research in the applications of AI for VRE management are proposed and discussed, including the issue of data availability, types and quality, in addition to explainable artificial intelligence (XAI), quantum artificial intelligence (QAI), coupling AI with the emerging digital twins technology, and natural language processing.

show abstract

Section: Marine Power Forecastingmentioning

confidence: 99%