Forecasting power demand in China with a CNN-LSTM model including multimodal information

Research article (Energy, 2022) · cited 94× · AI/ML

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Forecasting power demand in China with a CNN-LSTM model including multimodal information

Summary

Forecasting power demand in China with a CNN-LSTM model including multimodal information is a scholarly article^[1].

Key Facts

Forecasting power demand in China with a CNN-LSTM model including multimodal information's instance of is recorded as scholarly article^[2].

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Instance of scholarly article

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Authored by 3

Delu Wang, Jinqi Mao, Jun Gan

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Delu Wang, Jinqi Mao, Jun Gan

References

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Direct Wikidata claims

[2] ↑ Forecasting power demand in China with a CNN-LSTM model including multimodal information — instance of (P31): scholarly article. wikidata.org.

Class ancestry

[1] ↑ Forecasting power demand in China with a CNN-LSTM model including multimodal information is an instance of scholarly article (scholarly article) [P31, primary]. Wikidata. wikidata.org.

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Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.

APA

4ort.xyz Knowledge Graph. (2026). Forecasting power demand in China with a CNN-LSTM model including multimodal information. Retrieved May 24, 2026, from https://4ort.xyz/entity/forecasting-power-demand-in-china-with-a-cnn-lstm-model-including-multimodal-information

MLA

“Forecasting power demand in China with a CNN-LSTM model including multimodal information.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/forecasting-power-demand-in-china-with-a-cnn-lstm-model-including-multimodal-information.

BibTeX

@misc{4ortxyz_forecasting-power-demand-in-china-with-a-cnn-lstm-model-including-multimodal-information_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Forecasting power demand in China with a CNN-LSTM model including multimodal information}}, year = {2026}, url = {https://4ort.xyz/entity/forecasting-power-demand-in-china-with-a-cnn-lstm-model-including-multimodal-information}, note = {Accessed: 2026-05-24}}

LLM prompt

According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Forecasting power demand in China with a CNN-LSTM model including multimodal information — https://4ort.xyz/entity/forecasting-power-demand-in-china-with-a-cnn-lstm-model-including-multimodal-information (retrieved 2026-05-24)

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