Author:
Fu Yu,Wang Yang,Cai Yongxiang,Liu Anjiang,Wen Yi,Li Hongwei,Ren Jiakuan,Qu Yangquan
Publisher
Springer Nature Singapore
Reference17 articles.
1. Boroojeni, G., Amini, M.H., Bahrami, S., Iyengar, S.S., Sarwat, A.I., Karabasoglu, O.: A novel multi-time-scale modeling for electric power demand forecasting: From short-term to medium-term horizon. Electr. Power Syst. Res. 142, 58–73 (2017)
2. Gross, G., Galiana, F.D.: Short-term load forecasting. Proc. IEEE 75(12), 1558–1573 (1987)
3. Behm, C., Nolting, L., Praktiknjo, A.: How to model European electricity load profiles using artificial neural networks,” Appl. Energy 277, Art. no. 115564 (2020)
4. Dudek, G., Pelka, P., Smyl, S.: A hybrid residual dilated LSTM and exponential smoothing model for midterm electric load forecasting. IEEE Trans. Neural Netw. Learn. Syst. Early Access 8 (2021)
5. Liu, Y., Zhang, H., Zhang, A.: Improved load forecasting method based on load characteristics under demand-side response. Power System Protection and Control 46(13), 126–133 (2018). (in Chinese)