A deep learning framework using multi-feature fusion recurrent neural networks for energy consumption forecasting
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DOI: 10.1016/j.apenergy.2023.121563
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Cited by:
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- Khan, Zulfiqar Ahmad & Khan, Shabbir Ahmad & Hussain, Tanveer & Baik, Sung Wook, 2024. "DSPM: Dual sequence prediction model for efficient energy management in micro-grid," Applied Energy, Elsevier, vol. 356(C).
- Dalia Mohammed Talat Ebrahim Ali & Violeta Motuzienė & Rasa Džiugaitė-Tumėnienė, 2024. "AI-Driven Innovations in Building Energy Management Systems: A Review of Potential Applications and Energy Savings," Energies, MDPI, vol. 17(17), pages 1-35, August.
- Hesen Zuo & Wengang Zheng & Mingfei Wang & Xin Zhang, 2023. "Prediction of Heat and Cold Loads of Factory Mushroom Houses Based on EWT Decomposition," Sustainability, MDPI, vol. 15(21), pages 1-19, October.
- Peng, Shiliang & Fan, Lin & Zhang, Li & Su, Huai & He, Yuxuan & He, Qian & Wang, Xiao & Yu, Dejun & Zhang, Jinjun, 2024. "Spatio-temporal prediction of total energy consumption in multiple regions using explainable deep neural network," Energy, Elsevier, vol. 301(C).
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Keywords
Energy consumption forecasting; Adaptive decomposition; Multi-feature fusion; Signal processing methods; Recurrent neural networks;All these keywords.
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