Open Access
Issue
E3S Web Conf.
Volume 231, 2021
2020 2nd International Conference on Power, Energy and Electrical Engineering (PEEE 2020)
Article Number 02001
Number of page(s) 5
Section Renewable Energy System and Engineering
DOI https://doi.org/10.1051/e3sconf/202123102001
Published online 25 January 2021
  1. Zhu, Bangzhu. “A novel multiscale ensemble carbon price prediction model integrating empirical mode decomposition, genetic algorithm and artificial neural network.” Energies 5.2 (2012): 355-370. [CrossRef] [Google Scholar]
  2. Yadav, Harendra Kumar, Yash Pal, and Madan Mohan Tripathi. “Short-term PV power forecasting using empirical mode decomposition in integration with back-propagation neural network.” Journal of Information and Optimization Sciences 41.1 (2020): 25-37. [CrossRef] [Google Scholar]
  3. Xie, Tuo, et al. “A hybrid forecasting method for solar output power based on variational mode decomposition, deep belief networks and autoregressive moving average.” Applied Sciences 8.10 (2018):1901. [CrossRef] [Google Scholar]
  4. Liu, Hui, et al. “A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks.” Renewable Energy 48 (2012): 545-556. [CrossRef] [Google Scholar]
  5. Sobri, Sobrina, Sam Koohi-Kamali, and Nasrudin Abd Rahim. “Solar photovoltaic generation forecasting methods: A review.” Energy Conversion and Management 156 (2018): 459-497. [CrossRef] [Google Scholar]
  6. Leva, Sonia, et al. “Analysis and validation of 24 hours ahead neural network forecasting of photovoltaic output power.” Mathematics and computers in simulation 131 (2017): 88-100. [CrossRef] [Google Scholar]
  7. Huang, Norden E., et al. “The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis.” Proceedings of the Royal Society of London. Series A: mathematical, physical and engineering sciences 454. 1971 (1998): 903-995. [NASA ADS] [CrossRef] [MathSciNet] [Google Scholar]
  8. Qiu, Xueheng, et al. “Empirical mode decomposition based ensemble deep learning for load demand time series forecasting.” Applied Soft Computing 54 (2017): 246-255. [CrossRef] [Google Scholar]
  9. El Mghouchi, Y., T. Ajzoul, and A. El Bouardi. “Prediction of daily solar radiation intensity by day of the year in twenty-four cities of Morocco.” Renewable and Sustainable Energy Reviews 53 (2016): 823-831. [CrossRef] [Google Scholar]
  10. Luo, Xianglong, Guohong Niu, and Qianjiao Wu. “Short-Term Traffic Flow Prediction Based on EMD and Artificial Neural Network.” ICCTP 2009: Critical Issues In Transportation Systems Planning, Development, and Management. 2009. 1-6. [Google Scholar]
  11. C Nwokike, Chukwudike, et al. “ARIMA Modelling of Neonatal Mortality in Abia State of Nigeria.” Asian Journal of Probability and Statistics (2020): 54-62. [Google Scholar]
  12. Ali, Mumtaz. and Ramendra Prasad. “Significant wave height forecasting via an extreme learning machine model integrated with improved complete ensemble empirical mode decomposition.” Renewable and Sustainable Energy Reviews 104 (2019): 281-295. [CrossRef] [Google Scholar]

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