A Novel Dual-Scale Deep Belief Network Method for Daily Urban Water Demand Forecasting
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- Wang, H.Z. & Wang, G.B. & Li, G.Q. & Peng, J.C. & Liu, Y.T., 2016. "Deep belief network based deterministic and probabilistic wind speed forecasting approach," Applied Energy, Elsevier, vol. 182(C), pages 80-93.
- Zhang, Gioqinang & Hu, Michael Y., 1998. "Neural network forecasting of the British Pound/US Dollar exchange rate," Omega, Elsevier, vol. 26(4), pages 495-506, August.
- Guoqiang Chen & Tianyu Long & Jiangong Xiong & Yun Bai, 2017. "Multiple Random Forests Modelling for Urban Water Consumption Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(15), pages 4715-4729, December.
- Chengdong Li & Zixiang Ding & Jianqiang Yi & Yisheng Lv & Guiqing Zhang, 2018. "Deep Belief Network Based Hybrid Model for Building Energy Consumption Prediction," Energies, MDPI, vol. 11(1), pages 1-26, January.
- Xiaomin Xu & Dongxiao Niu & Lihui Zhang & Yongli Wang & Keke Wang, 2017. "Ice Cover Prediction of a Power Grid Transmission Line Based on Two-Stage Data Processing and Adaptive Support Vector Machine Optimized by Genetic Tabu Search," Energies, MDPI, vol. 10(11), pages 1-18, November.
- Wen-chuan Wang & Kwok-wing Chau & Dong-mei Xu & Xiao-Yun Chen, 2015. "Improving Forecasting Accuracy of Annual Runoff Time Series Using ARIMA Based on EEMD Decomposition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(8), pages 2655-2675, June.
- Tang, Ling & Yu, Lean & Wang, Shuai & Li, Jianping & Wang, Shouyang, 2012. "A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting," Applied Energy, Elsevier, vol. 93(C), pages 432-443.
- Dedinec, Aleksandra & Filiposka, Sonja & Dedinec, Aleksandar & Kocarev, Ljupco, 2016. "Deep belief network based electricity load forecasting: An analysis of Macedonian case," Energy, Elsevier, vol. 115(P3), pages 1688-1700.
- Wang, Shouxiang & Zhang, Na & Wu, Lei & Wang, Yamin, 2016. "Wind speed forecasting based on the hybrid ensemble empirical mode decomposition and GA-BP neural network method," Renewable Energy, Elsevier, vol. 94(C), pages 629-636.
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- Yu-Ting Bai & Wei Jia & Xue-Bo Jin & Ting-Li Su & Jian-Lei Kong & Zhi-Gang Shi, 2023. "Nonstationary Time Series Prediction Based on Deep Echo State Network Tuned by Bayesian Optimization," Mathematics, MDPI, vol. 11(6), pages 1-22, March.
- Caspar V. C. Geelen & Doekle R. Yntema & Jaap Molenaar & Karel J. Keesman, 2021. "Burst Detection by Water Demand Nowcasting Based on Exogenous Sensors," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(4), pages 1183-1196, March.
- Bokde, Neeraj Dhanraj & Tranberg, Bo & Andresen, Gorm Bruun, 2021. "Short-term CO2 emissions forecasting based on decomposition approaches and its impact on electricity market scheduling," Applied Energy, Elsevier, vol. 281(C).
- Xin Liu & Xuefeng Sang & Jiaxuan Chang & Yang Zheng, 2021. "Multi-Model Coupling Water Demand Prediction Optimization Method for Megacities Based on Time Series Decomposition," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 4021-4041, September.
- Yang, Weifei & Xiao, Changlai & Zhang, Zhihao & Liang, Xiujuan, 2022. "Identification of the formation temperature field of the southern Songliao Basin, China based on a deep belief network," Renewable Energy, Elsevier, vol. 182(C), pages 32-42.
- Jing Liu & Xin-Lei Zhou & Lu-Qi Zhang & Yue-Ping Xu, 2023. "Forecasting Short-term Water Demands with an Ensemble Deep Learning Model for a Water Supply System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(8), pages 2991-3012, June.
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Keywords
daily water demand forecasting; ensemble empirical mode decomposition; deep belief network; dual-scale;All these keywords.
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