References
- Bai, Y., J. Xie, and C. Liu . 2021. Regression modeling for enterprise electricity consumption: A comparison of recurrent neural network and its variants. International Journal of Electrical Power & Energy Systems 126:106612. doi:10.1016/j.ijepes.2020.106612.
- Bisoi, R., D. R. Dash, and P. K. Dash . 2022. An efficient robust optimized functional link broad learning system for solar irradiance prediction. Applied Energy 319:119277. doi:10.1016/j.apenergy.2022.119277.
- Cao, J. C., and S. H. Cao. 2006. Study of forecasting solar irradiance using neural networks with preprocessing sample data by wavelet analysis. Energy 31 (15):3435–45. doi:10.1016/j.energy.2006.04.001.
- Cao, J., Z. Li, and J. Li. 2019. Financial time series forecasting model based on CEEMDAN and LSTM. Physica A Statistical Mechanics & Its Applications 519:127–39. doi:10.1016/j.physa.2018.11.061.
- Chao, N., W. Cong, and Z. Tingting. 2022. SUPER-SHORT-TERM FORECAST of SOLAR IRRADIANCE BASED on CNN-Bi-LSTM. Acta Energiae Solaris Sinica 43 (3):197.
- Frimane, Â., J. Munkhammar, and D. van der Meer. 2022. Infinite hidden Markov model for short-term solar irradiance forecasting. Solar Energy 244:331–42. doi:10.1016/j.solener.2022.08.041.
- Kaba, K., M. Sarıgül, and M. Avcı . 2018. Estimation of daily global solar radiation using deep learning model. Energy 162:126–35. doi:10.1016/j.energy.2018.07.202.
- Lee, J., W. Wang, and F. Harrou. 2020. Reliable solar irradiance prediction using ensemble learning-based models: A comparative study. Energy Conversion & Management 208:112582. doi:10.1016/j.enconman.2020.112582.
- Li, J., and D. C. Li. 2016. Short-term wind power forecasting based on CEEMDAN-FE-KELM method. Information & Control 45:135–41.
- Li, X., L. Ma, and P. Chen. 2022. Probabilistic solar irradiance forecasting based on XGBoost. Energy Reports 8:1087–95. doi:10.1016/j.egyr.2022.02.251.
- Li, C., Y. Zhang, G. Zhao, and Y. Ren. 2021. Hourly solar irradiance prediction using deep BiLSTM network. Earth Science Informatics 14:299–309. doi:10.1007/s12145-020-00511-3.
- Marinho, F. P., P. A. C. Rocha, A. R. R. Neto, and F. D. V. Bezerra. 2023. Short-term solar irradiance forecasting using CNN-1D, LSTM, and CNN-LSTM deep neural networks: A case study with the Folsom (USA) dataset. Journal of Solar Energy Engineering 145 (4):041002. doi:10.1115/1.4056122.
- Rilling, G., P. Flandrin, and P. Gonçalves. 2007. Bivariate empirical mode decomposition. IEEE Signal Processing Letters. 14(12):936–39. doi:10.1109/LSP.2007.904710.
- Singh, N., S. Jena, and C. K. Panigrahi. 2022. A novel application of decision Tree classifier in solar irradiance prediction. Materials Today: Proceedings 58:316–23. doi:10.1016/j.matpr.2022.02.198.
- Tian, C. X., M. Huang, and Q. B. Zhu. 2018. Hourly solar irradiance forecast based on EMD-LMD-LSSVM joint model. Acta Energiae Solaris Sinica 39:504–12.
- Tong, J., L. Xie, and S. Fang. 2022. Hourly solar irradiance forecasting based on encoder–decoder model using series decomposition and dynamic error compensation. Energy Conversion & Management 270:116049. doi:10.1016/j.enconman.2022.116049.
- Torres, M. E., M. A. Colominas, and G. Schlotthauer. 2011. A complete ensemble empirical mode decomposition with adaptive noise[C]. 2011 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, 4144–47.
- Tran, D., and M. Wagner. 2000. Fuzzy entropy clustering[C]. Ninth IEEE International Conference on Fuzzy Systems. FUZZ-IEEE 2000 (Cat. No. 00CH37063). IEEE, 1, 152–57.
- Vaswani, A., N. Shazeer, N. Parmar, et al. 2017. Attention is all you need. Advances in Neural Information Processing Systems, 5998–6008.