Toward efficient energy systems based on natural gas consumption prediction with LSTM Recurrent Neural Networks
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DOI: 10.1016/j.energy.2019.04.075
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- Jiang, Wei & Wang, Xin & Zhang, Shu, 2023. "Integrating multi-modal data into AFSA-LSTM model for real-time oil production prediction," Energy, Elsevier, vol. 279(C).
- AL-Alimi, Dalal & AlRassas, Ayman Mutahar & Al-qaness, Mohammed A.A. & Cai, Zhihua & Aseeri, Ahmad O. & Abd Elaziz, Mohamed & Ewees, Ahmed A., 2023. "TLIA: Time-series forecasting model using long short-term memory integrated with artificial neural networks for volatile energy markets," Applied Energy, Elsevier, vol. 343(C).
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- Su, Huai & Chi, Lixun & Zio, Enrico & Li, Zhenlin & Fan, Lin & Yang, Zhe & Liu, Zhe & Zhang, Jinjun, 2021. "An integrated, systematic data-driven supply-demand side management method for smart integrated energy systems," Energy, Elsevier, vol. 235(C).
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Keywords
Hourly natural gas consumption; Clustering; Time series; Artificial neural network; Long short term memory; Day-ahead forecast;All these keywords.
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