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A hybrid model based on selective ensemble for energy consumption forecasting in China

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  1. Fang, Lei & He, Bin, 2023. "A deep learning framework using multi-feature fusion recurrent neural networks for energy consumption forecasting," Applied Energy, Elsevier, vol. 348(C).
  2. Xiao, Jin & Li, Guohao & Xie, Ling & Wang, Shouyang & Yu, Lean, 2021. "Decarbonizing China's power sector by 2030 with consideration of technological progress and cross-regional power transmission," Energy Policy, Elsevier, vol. 150(C).
  3. Liu, Che & Li, Fan & Zhang, Chenghui & Sun, Bo & Zhang, Guanguan, 2023. "A day-ahead prediction method for high-resolution electricity consumption in residential units," Energy, Elsevier, vol. 265(C).
  4. Liyang Tang, 2020. "Application of Nonlinear Autoregressive with Exogenous Input (NARX) neural network in macroeconomic forecasting, national goal setting and global competitiveness assessment," Papers 2005.08735, arXiv.org.
  5. Liu, Che & Sun, Bo & Zhang, Chenghui & Li, Fan, 2020. "A hybrid prediction model for residential electricity consumption using holt-winters and extreme learning machine," Applied Energy, Elsevier, vol. 275(C).
  6. Somu, Nivethitha & M R, Gauthama Raman & Ramamritham, Krithi, 2020. "A hybrid model for building energy consumption forecasting using long short term memory networks," Applied Energy, Elsevier, vol. 261(C).
  7. Roman V. Klyuev & Irbek D. Morgoev & Angelika D. Morgoeva & Oksana A. Gavrina & Nikita V. Martyushev & Egor A. Efremenkov & Qi Mengxu, 2022. "Methods of Forecasting Electric Energy Consumption: A Literature Review," Energies, MDPI, vol. 15(23), pages 1-33, November.
  8. Akdi, Yılmaz & Gölveren, Elif & Okkaoğlu, Yasin, 2020. "Daily electrical energy consumption: Periodicity, harmonic regression method and forecasting," Energy, Elsevier, vol. 191(C).
  9. Zhang, Yaojie & He, Mengxi & Wen, Danyan & Wang, Yudong, 2023. "Forecasting crude oil price returns: Can nonlinearity help?," Energy, Elsevier, vol. 262(PB).
  10. Gao, Feng & Chi, Hong & Shao, Xueyan, 2021. "Forecasting residential electricity consumption using a hybrid machine learning model with online search data," Applied Energy, Elsevier, vol. 300(C).
  11. Xin Ma & Yubin Cai & Hong Yuan & Yanqiao Deng, 2023. "Partially Linear Component Support Vector Machine for Primary Energy Consumption Forecasting of the Electric Power Sector in the United States," Sustainability, MDPI, vol. 15(9), pages 1-26, April.
  12. Seyedeh Narjes Fallah & Mehdi Ganjkhani & Shahaboddin Shamshirband & Kwok-wing Chau, 2019. "Computational Intelligence on Short-Term Load Forecasting: A Methodological Overview," Energies, MDPI, vol. 12(3), pages 1-21, January.
  13. Yongwei, Cheng & Dong, Mu & Huanyu, Ren & Tijun, Fan & Jianbang, Du, 2020. "Using a temporal input-output approach to analyze the ripple effect of China’s energy consumption," Energy, Elsevier, vol. 211(C).
  14. Laib, Oussama & Khadir, Mohamed Tarek & Mihaylova, Lyudmila, 2019. "Toward efficient energy systems based on natural gas consumption prediction with LSTM Recurrent Neural Networks," Energy, Elsevier, vol. 177(C), pages 530-542.
  15. Hu, Huanling & Wang, Lin & Lv, Sheng-Xiang, 2020. "Forecasting energy consumption and wind power generation using deep echo state network," Renewable Energy, Elsevier, vol. 154(C), pages 598-613.
  16. Kasım Zor & Özgür Çelik & Oğuzhan Timur & Ahmet Teke, 2020. "Short-Term Building Electrical Energy Consumption Forecasting by Employing Gene Expression Programming and GMDH Networks," Energies, MDPI, vol. 13(5), pages 1-24, March.
  17. Lu, Yang & Wang, Ying & Zhang, Jing & Xu, Ying & Li, Guoqiang & Zhang, Yongfa, 2019. "Investigation on the catalytic effect of AAEMs in Zhundong coal on the combustion characteristics of Changji oil shale and its kinetics," Energy, Elsevier, vol. 178(C), pages 89-100.
  18. Jiang, Ping & Yang, Hufang & Li, Hongmin & Wang, Ying, 2021. "A developed hybrid forecasting system for energy consumption structure forecasting based on fuzzy time series and information granularity," Energy, Elsevier, vol. 219(C).
  19. Abdulgani Kahraman & Mehmed Kantardzic & Muhammet Mustafa Kahraman & Muhammed Kotan, 2021. "A Data-Driven Multi-Regime Approach for Predicting Energy Consumption," Energies, MDPI, vol. 14(20), pages 1-17, October.
  20. Wenting Zhao & Juanjuan Zhao & Xilong Yao & Zhixin Jin & Pan Wang, 2019. "A Novel Adaptive Intelligent Ensemble Model for Forecasting Primary Energy Demand," Energies, MDPI, vol. 12(7), pages 1-28, April.
  21. Li, Fengyun & Zheng, Haofeng & Li, Xingmei & Yang, Fei, 2021. "Day-ahead city natural gas load forecasting based on decomposition-fusion technique and diversified ensemble learning model," Applied Energy, Elsevier, vol. 303(C).
  22. Zhixiong Weng & Yuqi Song & Hao Ma & Zhong Ma & Tingting Liu, 2023. "Forecasting energy demand, structure, and CO2 emission: a case study of Beijing, China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(9), pages 10369-10391, September.
  23. Kangji Li & Borui Wei & Qianqian Tang & Yufei Liu, 2022. "A Data-Efficient Building Electricity Load Forecasting Method Based on Maximum Mean Discrepancy and Improved TrAdaBoost Algorithm," Energies, MDPI, vol. 15(23), pages 1-18, November.
  24. Somu, Nivethitha & Raman M R, Gauthama & Ramamritham, Krithi, 2021. "A deep learning framework for building energy consumption forecast," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
  25. Ruijin Zhu & Weilin Guo & Xuejiao Gong, 2019. "Short-Term Photovoltaic Power Output Prediction Based on k -Fold Cross-Validation and an Ensemble Model," Energies, MDPI, vol. 12(7), pages 1-15, March.
  26. Fatemi Bushehri, Seyyed Mohammad Mehdi & Dehghan Khavari, Saeed & Mirjalili, Seyed Hossein & Babaei Meybodi, Hamid & Sardari Zarchi, Mohsen, 2022. "Energy Consumption Prediction in Iran: A Hybrid Machine Learning and Genetic Algorithm Method with Sustainable Development Considerations," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 6(2).
  27. Shian-Chang Huang & Cheng-Feng Wu, 2018. "Energy Commodity Price Forecasting with Deep Multiple Kernel Learning," Energies, MDPI, vol. 11(11), pages 1-16, November.
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