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Enhancing power grid stability with a hybrid framework for wind power forecasting: Integrating Kalman Filtering, Deep Residual Learning, and Bidirectional LSTM
Research article (Energy, 2025) · cited 19× · AI/ML
Enhancing power grid stability with a hybrid framework for wind power forecasting: Integrating Kalman Filtering, Deep Residual Learning, and Bidirectional LSTM
Summary
Enhancing power grid stability with a hybrid framework for wind power forecasting: Integrating Kalman Filtering, Deep Residual Learning, and Bidirectional LSTM is a scholarly article[1].
Key Facts
Enhancing power grid stability with a hybrid framework for wind power forecasting: Integrating Kalman Filtering, Deep Residual Learning, and Bidirectional LSTM's instance of is recorded as scholarly article[2].
References
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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.
APA4ort.xyz Knowledge Graph. (2026). Enhancing power grid stability with a hybrid framework for wind power forecasting: Integrating Kalman Filtering, Deep Residual Learning, and Bidirectional LSTM. Retrieved May 24, 2026, from https://4ort.xyz/entity/enhancing-power-grid-stability-with-a-hybrid-framework-for-wind-power-forecasting-integrating-kalman-filtering-deep-resi
MLA“Enhancing power grid stability with a hybrid framework for wind power forecasting: Integrating Kalman Filtering, Deep Residual Learning, and Bidirectional LSTM.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/enhancing-power-grid-stability-with-a-hybrid-framework-for-wind-power-forecasting-integrating-kalman-filtering-deep-resi.
BibTeX@misc{4ortxyz_enhancing-power-grid-stability-with-a-hybrid-framework-for-wind-power-forecasting-integrating-kalman-filtering-deep-resi_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Enhancing power grid stability with a hybrid framework for wind power forecasting: Integrating Kalman Filtering, Deep Residual Learning, and Bidirectional LSTM}}, year = {2026}, url = {https://4ort.xyz/entity/enhancing-power-grid-stability-with-a-hybrid-framework-for-wind-power-forecasting-integrating-kalman-filtering-deep-resi}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Enhancing power grid stability with a hybrid framework for wind power forecasting: Integrating Kalman Filtering, Deep Residual Learning, and Bidirectional LSTM — https://4ort.xyz/entity/enhancing-power-grid-stability-with-a-hybrid-framework-for-wind-power-forecasting-integrating-kalman-filtering-deep-resi (retrieved 2026-05-24)
