Author:
Deng Fangzhao,Li Hujun,Deng Zhenli,Si Jianan,Yu Boning,Guo Xingwu,Zeng Yizhou
Publisher
Springer Nature Singapore
Reference16 articles.
1. Amjady, N.: Short-term hourly load forecasting using time-series modeling with peak load estimation capability. IEEE Trans. Power Syst. 16(3), 498–505 (2001)
2. Al-Alawi, S.M., Islam, S.M.: Principles of electricity demand forecasting. I. Methodologies. Power Eng. J. 10(3), 139–143 (1996)
3. Gebremeskel, D.H., Ahlgren, E.O., Beyene, G.B.: Long-term evolution of energy and electricity demand forecasting: the case of Ethiopia. Energy Strategy Rev. 36, 100671 (2021)
4. Gonzalez-Briones, A., et al.: Machine learning models for electricity consumption forecasting: a review. In: 2019 2nd International Conference on Computer Applications & Information Security (ICCAIS), pp. 1–6. IEEE (2019)
5. Shi, X., et al.: Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: Advances in Neural Information Processing Systems, vol. 28 (2015)