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Edge-cloud collaboration-driven predictive planning of electric vehicle charging load for microgrids

Shuaiyin Ma et al.

Applied Energy2026https://doi.org/10.1016/j.apenergy.2026.127362article
ABDC A
Weight
0.44

Abstract

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3 citations

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https://doi.org/https://doi.org/10.1016/j.apenergy.2026.127362

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@article{shuaiyin2026,
  title        = {{Edge-cloud collaboration-driven predictive planning of electric vehicle charging load for microgrids}},
  author       = {Shuaiyin Ma et al.},
  journal      = {Applied Energy},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1016/j.apenergy.2026.127362},
}

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Edge-cloud collaboration-driven predictive planning of electric vehicle charging load for microgrids

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Evidence weight

0.44

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.32 × 0.4 = 0.13
M · momentum0.57 × 0.15 = 0.09
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.