Topic Editors

Mechanical and Industrial Engineering, Texas A&M University-Kingsville, Kingsville, TX 78363, USA
Department of Management, Marketing and Information Systems, College of Business Administration, Texas A&M University Kingsville, Kingsville, TX 78363, USA

Research and Application of Artificial Intelligence in Wind and Wave Energy

Abstract submission deadline
closed (30 November 2023)
Manuscript submission deadline
closed (31 January 2024)
Viewed by
1634

Topic Information

Dear Colleagues,

Renewable energy utilization is widely recognized as one of the main strategies to reduce civilization’s carbon footprint and prevent the most deleterious impacts of climate change. Wind energy is a mature technology that in many locations offers the lowest cost for electric generation. Wind turbines are exponentially growing in power generation capacity in offshore locations, increasingly supplying a larger proportion of electricity consumption. Simultaneously, wave energy is being developed and tested at an accelerated pace in many locations, considering its capability of replacing large portions of current hydrocarbon electricity generation. Collocating harvesters for both wind and wave has been studied with great interest, providing significant benefits and synergies. However, many of the challenges presented by wind and wave energy are difficult to address by applying traditional methods. Selecting optimal placements, layouts, equipment, equipment mixture and collocation, operational times, deactivations, energy curtailing and maintenance strategies are in most cases NP-hard problems. Applications and development of artificial intelligence (AI) in wind and wave energy have shown a number of advantages, generating optimal solutions that can be implemented in real-world scenarios. Furthermore, these solutions may be implemented in diverse geographical locations and for diverse power generation demands with more flexibility. The aim of this Topic is to provide researchers, practitioners, developers, organizations, and governmental agencies with a platform to present the work they are performing in the research and application of artificial intelligence in wind and wave energy. This Topic is open to all lifecycle stages of wind and wave energy utilization, including prediction, design, manufacturing, harvesting, operation and control, and transmission, with the goal to advance more sustainable renewable energy generation and help to reduce greenhouse gases emission worldwide in an accelerated timeline.

Prof. Dr. Hua Li
Dr. Francisco Haces-Fernandez
Topic Editors

Keywords

  • wind energy
  • wave energy
  • wind and wave energy collocation
  • optimization
  • artificial intelligence (AI)
  • offshore renewable energy
  • site selection
  • layout optimization

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Energies
energies
3.2 5.5 2008 16.1 Days CHF 2600
Journal of Marine Science and Engineering
jmse
2.9 3.7 2013 15.4 Days CHF 2600
Sci
sci
- 3.1 2019 47.7 Days CHF 1200
Sustainability
sustainability
3.9 5.8 2009 18.8 Days CHF 2400

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Published Papers (1 paper)

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22 pages, 2839 KiB  
Article
Wind Power Forecasting Based on WaveNet and Multitask Learning
Sustainability 2023, 15(14), 10816; https://doi.org/10.3390/su151410816 - 10 Jul 2023
Viewed by 1087
Abstract
Accurately predicting the power output of wind turbines is crucial for ensuring the reliable and efficient operation of large-scale power systems. To address the inherent limitations of physical models, statistical models, and machine learning algorithms, we propose a novel framework for wind turbine [...] Read more.
Accurately predicting the power output of wind turbines is crucial for ensuring the reliable and efficient operation of large-scale power systems. To address the inherent limitations of physical models, statistical models, and machine learning algorithms, we propose a novel framework for wind turbine power prediction. This framework combines a special type of convolutional neural network, WaveNet, with a multigate mixture-of-experts (MMoE) architecture. The integration aims to overcome the inherent limitations by effectively capturing and utilizing complex patterns and trends in the time series data. First, the maximum information coefficient (MIC) method is applied to handle data features, and the wavelet transform technique is employed to remove noise from the data. Subsequently, WaveNet utilizes its scalable convolutional network to extract representations of wind power data and effectively capture long-range temporal information. These representations are then fed into the MMoE architecture, which treats multistep time series prediction as a set of independent yet interrelated tasks, allowing for information sharing among different tasks to prevent error accumulation and improve prediction accuracy. We conducted predictions for various forecasting horizons and compared the performance of the proposed model against several benchmark models. The experimental results confirm the strong predictive capability of the WaveNet–MMoE framework. Full article
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