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Optimizing electricity demand forecasting with a novel RNN-LSTM hybrid model

Yıldırım ÖZÜPAK & Shuhratjon Mansurov

Energy Sources, Part B: Economics, Planning, and Policy2025https://doi.org/10.1080/15567249.2025.2531448article
ABDC C
Weight
0.63

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https://doi.org/https://doi.org/10.1080/15567249.2025.2531448

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@article{yıldırım2025,
  title        = {{Optimizing electricity demand forecasting with a novel RNN-LSTM hybrid model}},
  author       = {Yıldırım ÖZÜPAK & Shuhratjon Mansurov},
  journal      = {Energy Sources, Part B: Economics, Planning, and Policy},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1080/15567249.2025.2531448},
}

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Optimizing electricity demand forecasting with a novel RNN-LSTM hybrid model

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Evidence weight

0.63

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

F · citation impact0.66 × 0.4 = 0.26
M · momentum0.93 × 0.15 = 0.14
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.