A review of machine learning and deep learning applications in wave energy forecasting and WEC optimization

Shadmani, Alireza; Nikoo, Mohammad Reza; Gandomi, Amir H.; Wang, Ruo-Qian; Golparvar, Behzad

doi:10.1016/j.esr.2023.101180

Cited by 21 publications

(4 citation statements)

References 118 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are several papers in the literature that review some applications of AI techniques in smart grids and power systems such as energy management [23,24], energy market [25,26], security and stability assessment [27], and cyber security systems [28]. The aforementioned trend necessitates creating a systematic overview of the application fields of AI to enhance the resilience of microgrids.…”

Section: Motivation For This Workmentioning

confidence: 99%

AI Applications to Enhance Resilience in Power Systems and Microgrids—A Review

Zahraoui,

Korõtko,

Rosin

et al. 2024

Sustainability

View full text Add to dashboard Cite

This paper presents an in-depth exploration of the application of Artificial Intelligence (AI) in enhancing the resilience of microgrids. It begins with an overview of the impact of natural events on power systems and provides data and insights related to power outages and blackouts caused by natural events in Estonia, setting the context for the need for resilient power systems. Then, the paper delves into the concept of resilience and the role of microgrids in maintaining power stability. The paper reviews various AI techniques and methods, and their application in power systems and microgrids. It further investigates how AI can be leveraged to improve the resilience of microgrids, particularly during different phases of an event occurrence time (pre-event, during event, and post-event). A comparative analysis of the performance of various AI models is presented, highlighting their ability to maintain stability and ensure a reliable power supply. This comprehensive review contributes significantly to the existing body of knowledge and sets the stage for future research in this field. The paper concludes with a discussion of future work and directions, emphasizing the potential of AI in revolutionizing power system monitoring and control.

show abstract

Section: Motivation For This Workmentioning

confidence: 99%

AI Applications to Enhance Resilience in Power Systems and Microgrids—A Review

Zahraoui,

Korõtko,

Rosin

et al. 2024

Sustainability

View full text Add to dashboard Cite

show abstract

“…Their research aimed to improve learning methodologies in the domain of computers. The value of optimization in the field of renewable energy is underscored by [2,8], who emphasize its significance in wave energy forecasting, a critical aspect for the effective use of energy resources. In [9] examine the optimization of electric car charging networks within the context of transport infrastructure, highlighting its significance in the realm of sustainable development.…”

Section: Literature Reviewmentioning

confidence: 99%

“…As a result, this approach is well-suited for problem domains that require experiential learning as a crucial component. The works of [8,13]. explore the field of predictive modelling, specifically on the areas of forecasting and dynamic optimization.…”

Section: Comparative Analysismentioning

confidence: 99%

“…The sustainable management of resources, demonstrated in the works of [9,12], underscores machine learning's role in environmental conservation and efficient energy use. Similarly, advancements in renewable energy technologies, as shown by [1,8], contribute to more reliable and efficient renewable energy sources, crucial for sustainable energy futures. In the realm of security, the development of sophisticated biometric systems, researched by [2,11], points towards enhanced security measures in both public and private sectors.…”

Section: Comparative Analysismentioning

confidence: 99%

See 1 more Smart Citation

Recent Advances in Machine Learning-based Optimization Techniques: A Systematic Review

2023

Proceeding Book of 3rd International Conference on Scientific and Academic Research ICSAR 2023

View full text Add to dashboard Cite

Exploring the convergence of machine learning and optimization techniques, this paper delves into the recent progress and wide-ranging applications of these methods in various domains, including chemical engineering, pharmaceuticals, and environmental management. Through a detailed analysis of pivotal studies published, the paper evaluates the effectiveness, efficiency, applicability, and limitations of different machine learning strategies in optimization scenarios. It reveals how the integration of machine learning in optimization transcends disciplinary boundaries, significantly impacting sectors from healthcare to environmental sustainability. The paper highlights significant developments such as the emergence of hybrid and evolutionary models, advancements in personalized healthcare, and breakthroughs in sustainable resource management and security. Additionally, it discusses the challenges and ethical implications inherent in these advancements, particularly the need for comprehensive datasets and substantial computational resources. Ultimately, the insights gathered suggest that machine learning-based optimization is crucial for fostering innovation and addressing complex challenges in various fields, emphasizing the need for its responsible and ethical use in the ever-changing technological landscape.

show abstract

Research progress and prospects of machine learning applications in renewable energy: a comprehensive bibliometric-based review

Wang,

Shen,

2024

Int. J. Environ. Sci. Technol.

View full text Add to dashboard Cite

A review of machine learning and deep learning applications in wave energy forecasting and WEC optimization

Cited by 21 publications

References 118 publications

AI Applications to Enhance Resilience in Power Systems and Microgrids—A Review

AI Applications to Enhance Resilience in Power Systems and Microgrids—A Review

Recent Advances in Machine Learning-based Optimization Techniques: A Systematic Review

Research progress and prospects of machine learning applications in renewable energy: a comprehensive bibliometric-based review

Contact Info

Product

Resources

About