Author:
Hu Pan,Qi Jun,Bo Jue,Xia Yu,Jiao Chuan-Ming,Huang Meng-Tong
Publisher
Atlantis Press International BV
Reference9 articles.
1. Moradzadeh, H. Moayyed, B. Mohammadi-Ivatloo, A. P. Aguiar and A. Anvari-Moghaddam, “A Secure Federated Deep Learning-Based Approach for Heating Load Demand Forecasting in Building Environment,” in IEEE Access, vol. 10, pp. 5037-5050, 2022, doi: https://doi.org/10.1109/ACCESS.2021.3139529.
2. Chen H B, Pei L L, Zhao Y F. Forecasting seasonal variations in electricity consumption and electricity usage efficiency of industrial sectors using a grey modeling approach [J]. Energy, 2021, 222: 119952.
3. Dilaver Z, Hunt L C . Industrial electricity demand for Turkey: A structural time series analysis [J]. Energy Economics, 2011, 33(3): 426-436.
4. Fang PENG, Gaoqun PENG, Yaru QI, Tiantian LIU, Xiaolei ZHOU. Nowcasting of China’s industrial added value based on electric power big data [J]. Telecommunications Science, 2021, 37(7): 115-125.
5. Guangtong G U, Bing X U. Half-month forecast of China’s industrial added value: Based on the macro monthly data [J]. Systems Engineering-Theory & Practice, 2018.