ANN-LSTM-A Water Consumption Prediction Based on Attention Mechanism Enhancement
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- Yuan, Ran & Wang, Bo & Mao, Zhixin & Watada, Junzo, 2021. "Multi-objective wind power scenario forecasting based on PG-GAN," Energy, Elsevier, vol. 226(C).
- Lyu, Yizheng & Gao, Hanbo & Yan, Kun & Liu, Yingjie & Tian, Jinping & Chen, Lyujun & Wan, Mei, 2022. "Carbon peaking strategies for industrial parks: Model development and applications in China," Applied Energy, Elsevier, vol. 322(C).
- Chung, Won Hee & Gu, Yeong Hyeon & Yoo, Seong Joon, 2022. "District heater load forecasting based on machine learning and parallel CNN-LSTM attention," Energy, Elsevier, vol. 246(C).
- Singh, Sanjeet & Bansal, Pooja & Hosen, Mosharrof & Bansal, Sanjeev K., 2023. "Forecasting annual natural gas consumption in USA: Application of machine learning techniques- ANN and SVM," Resources Policy, Elsevier, vol. 80(C).
- Fazlipour, Zahra & Mashhour, Elaheh & Joorabian, Mahmood, 2022. "A deep model for short-term load forecasting applying a stacked autoencoder based on LSTM supported by a multi-stage attention mechanism," Applied Energy, Elsevier, vol. 327(C).
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Keywords
water consumption prediction; artificial neural network (ANN); long short-term memory (LSTM); attention mechanism (AM);All these keywords.
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