Research and Application of Artificial Intelligence in Wind and Wave Energy

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

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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All Energies JMSE Sci Sustainability

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22 pages, 2839 KiB  

Open AccessArticle

Wind Power Forecasting Based on WaveNet and Multitask Learning

by Hao Wang, Chen Peng, Bolin Liao, Xinwei Cao and Shuai Li

Sustainability 2023, 15(14), 10816; https://doi.org/10.3390/su151410816 - 10 Jul 2023

Viewed by 1263

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

(This article belongs to the Topic Research and Application of Artificial Intelligence in Wind and Wave Energy)

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