An efficient power load forecasting model based on the optimized combination

被引:6
作者
Fang, Jicheng [1 ]
Shen, Dongqin [2 ]
Li, Xiuyi [3 ]
Li, Huijia [4 ]
机构
[1] Shanghai Univ Elect Power, Coll Elect & Informat Engn, Shanghai 200090, Peoples R China
[2] Nanjing Univ Sci & Technol, Sch Comp Sci & Engn, Nanjing 210094, Peoples R China
[3] Nanjing Univ Finance & Econ, Sch Informat Engn, Nanjing 210003, Peoples R China
[4] Cent Univ Finance & Econ, Sch Management Sci & Engn, Beijing 100080, Peoples R China
来源
MODERN PHYSICS LETTERS B | 2020年 / 34卷 / 12期
基金
中国国家自然科学基金; 北京市自然科学基金;
关键词
Wind power; load forecasting; optimized combination; accuracy;
D O I
10.1142/S0217984920501146
中图分类号
O59 [应用物理学];
学科分类号
摘要
The new energy industry gains more and more attention since the problem of resource scarcity and utilization of the renewable energy has become a global highlight issue. In this paper, we propose a new load forecasting model under the development of new energy industry by choosing the typical wind power as the key subject, which is also an important reference for other energy industries. The wind power load forecasting model is built based on optimized combination, which is forecasted and analyzed by the time series, the Markov and the gray forecasting models individually, and then combined by the optimized weighting coefficients. The method has overcome the limitations of poor adaptability of the single forecasting models and come out with an ideal result. Experimental results show our method has better performance compared with other related algorithms in different datasets.
引用
收藏
页数:13
相关论文
共 30 条
[1]   Evolutionary rough feature selection in gene expression data [J].
Banerjee, Mohua ;
Mitra, Sushmita ;
Banka, Haider .
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART C-APPLICATIONS AND REVIEWS, 2007, 37 (04) :622-632
[2]   Link prediction in temporal networks: Integrating survival analysis and game theory [J].
Bu, Zhan ;
Wang, Yuyao ;
Li, Hui-Jia ;
Jiang, Jiuchuan ;
Wu, Zhiang ;
Cao, Jie .
INFORMATION SCIENCES, 2019, 498 :41-61
[3]   Dynamic Cluster Formation Game for Attributed Graph Clustering [J].
Bu, Zhan ;
Li, Hui-Jia ;
Cao, Jie ;
Wang, Zhen ;
Gao, Guangliang .
IEEE TRANSACTIONS ON CYBERNETICS, 2019, 49 (01) :328-341
[4]   GLEAM: a graph clustering framework based on potential game optimization for large-scale social networks [J].
Bu, Zhan ;
Cao, Jie ;
Li, Hui-Jia ;
Gao, Guangliang ;
Tao, Haicheng .
KNOWLEDGE AND INFORMATION SYSTEMS, 2018, 55 (03) :741-770
[5]  
Cao G. J., 2004, POWER SYST TECHNOL, V28, P49
[6]   Detecting Prosumer-Community Groups in Smart Grids From the Multiagent Perspective [J].
Cao, Jie ;
Bu, Zhan ;
Wang, Yuyao ;
Yang, Huan ;
Jiang, Jiuchuan ;
Li, Hui-Jia .
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2019, 49 (08) :1652-1664
[7]  
Ding Qia, 2004, Automation of Electric Power Systems, V28, P83
[8]   Locating the source node of diffusion process in cyber-physical networks via minimum observers [J].
Hu, Z. L. ;
Wang, L. ;
Tang, C. B. .
CHAOS, 2019, 29 (06)
[9]  
Huang Wen-tao, 2003, Proceedings of the CSEE, V23, P150
[10]  
[焦慧敏 JIAO Huimin], 2006, [计算机仿真, Computer Simulation], V23, P218