Research on variable speed constant frequency energy generation based on deep learning for disordered ocean current energy

Wei, Hongbo; Su, Wenbin; Shi, Junxiao

doi:10.1016/j.egyr.2022.10.300

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…Berdasarkan SLR diketahui Amerika menduduki posisi nomor dua dalam meneliti artikel terkait energi gelombang laut. Dengan 2 artikel [4], [5], terkait pemanfaatan gelombang laut, 3 artikel [20], [25], [26], terkait pemodelan energi gelombang laut, dan masing-masing 1 artikel [36] terkait peralatan energi gelombang laut, [39] terkait potensi energi gelombang laut, [41] terkait desipasi energi gelombang laut dan [43] [4], [5], [20], [25], [26], [36], [39], [41], [43] [2], [6]- [9], [13], [17], [18], [22], [23], [30], [34], [35], [38]…”

Section: Hasil Dan Pembahasan Pertanyaanunclassified

Systematic Literature Review of Ocean Wave Renewable Energy

Nuryadi,

Yendri Sudiar,

Hamdi

2023

jccs

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Renewable energy is a concern to overcome the limitations of fossil energy, one type of renewable energy is ocean wave energy. There are various research topics related to the development of ocean wave energy potential carried out by countries in the world, for this reason the authors conducted a review related to journal papers that discuss ocean wave renewable energy. The method for this study used a Systematic Literature Review (SLR) on journal papers published from 2008-2023, a total of 44 journal papers. The three research questions contained in this study so as to obtain research results that the most countries that carry out research related to ocean wave renewable energy are China. There are a total of nine topics discussed related to ocean wave renewable energy with the most frequently discussed topic trends related to the utilization and modeling of ocean wave renewable energy.

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Section: Hasil Dan Pembahasan Pertanyaanunclassified

Systematic Literature Review of Ocean Wave Renewable Energy

Nuryadi,

Yendri Sudiar,

Hamdi

2023

jccs

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“…Ref. [79] explored a constant frequency control algorithm based on a deep learning prediction model to improve the steady-state accuracy of the hydraulic motor speed. This paper proposed a prediction model based on empirical wavelet transform LSTM-CNN, which improved the prediction accuracy by 12.26% compared to the short-term memory neural network.…”

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confidence: 99%

Artificial Intelligence and Machine Learning in Energy Conversion and Management

Mira,

Bugiotti,

Morosuk

2023

Energies

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In the modern era, where the global energy sector is transforming to meet the decarbonization goal, cutting-edge information technology integration, artificial intelligence, and machine learning have emerged to boost energy conversion and management innovations. Incorporating artificial intelligence and machine learning into energy conversion, storage, and distribution fields presents exciting prospects for optimizing energy conversion processes and shaping national and global energy markets. This integration rapidly grows and demonstrates promising advancements and successful practical implementations. This paper comprehensively examines the current state of applying artificial intelligence and machine learning algorithms in energy conversion and management evaluation and optimization tasks. It highlights the latest developments and the most promising algorithms and assesses their merits and drawbacks, encompassing specific applications and relevant scenarios. Furthermore, the authors propose recommendations to emphasize the prioritization of acquiring real-world experimental and simulated data and adopting standardized, explicit reporting in research publications. This review paper includes details on data size, accuracy, error rates achieved, and comparisons of algorithm performance against established benchmarks.

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