A novel power-driven fractional accumulated grey model and its application in forecasting wind energy consumption of China
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DOI: 10.1371/journal.pone.0225362
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References listed on IDEAS
- Saidur, R. & Islam, M.R. & Rahim, N.A. & Solangi, K.H., 2010. "A review on global wind energy policy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(7), pages 1744-1762, September.
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- Yang, Zhongshan & Wang, Jian, 2018. "A combination forecasting approach applied in multistep wind speed forecasting based on a data processing strategy and an optimized artificial intelligence algorithm," Applied Energy, Elsevier, vol. 230(C), pages 1108-1125.
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Cited by:
- Atif Maqbool Khan & Magdalena Osińska, 2021. "How to Predict Energy Consumption in BRICS Countries?," Energies, MDPI, vol. 14(10), pages 1-21, May.
- Daren Zhao & Huiwu Zhang & Qing Cao & Zhiyi Wang & Sizhang He & Minghua Zhou & Ruihua Zhang, 2022. "The research of ARIMA, GM(1,1), and LSTM models for prediction of TB cases in China," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-18, February.
- Shen, Meng & Li, Xiang & Lu, Yujie & Cui, Qingbin & Wei, Yi-Ming, 2021. "Personality-based normative feedback intervention for energy conservation," Energy Economics, Elsevier, vol. 104(C).
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