A novel micro power generation system to efficiently harvest hydroelectric energy for power supply to water intelligent networks of urban water pipelines

Shen, Zhicheng; Yao, Yao; Wang, Qiliang; Lu, Lin; Yang, Hongxing

doi:10.1016/j.energy.2023.126694

Cited by 5 publications

(1 citation statement)

References 21 publications

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“…The interaction that occurred among wind and tides resulted in high conversion rates and internal wave frequencies However, the energy conversion system requires extensive calibration and testing to provide stability to the model Shen et al [76] A micro power generation system which tends to generate hydroelectric energy for supplying water through intelligent networks…”

Section: Author Proposed Methods Advantage Limitationsmentioning

confidence: 99%

A Review on Sustainable Energy Sources Using Machine Learning and Deep Learning Models

Bhansali,

Narasimhulu,

Pérez de Prado

et al. 2023

Energies

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Today, methodologies based on learning models are utilized to generate precise conversion techniques for renewable sources. The methods based on Computational Intelligence (CI) are considered an effective way to generate renewable instruments. The energy-related complexities of developing such methods are dependent on the vastness of the data sets and number of parameters needed to be covered, both of which need to be carefully examined. The most recent and significant researchers in the field of learning-based approaches for renewable challenges are addressed in this article. There are several different Deep Learning (DL) and Machine Learning (ML) approaches that are utilized in solar, wind, hydro, and tidal energy sources. A new taxonomy is formed in the process of evaluating the effectiveness of the strategies that are described in the literature. This survey evaluates the advantages and the drawbacks of the existing methodologies and helps to find an effective approach to overcome the issues in the existing methods. In this study, various methods based on energy conversion systems in renewable source of energies like solar, wind, hydro power, and tidal energies are evaluated using ML and DL approaches.

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Section: Author Proposed Methods Advantage Limitationsmentioning

confidence: 99%