Short-term power load forecasting based on I-GWO-KELM algorithm

被引：2
|
作者：
Chen, Xiaoyu [1 ]
Dong, Xiangli [1 ]
Shi, Li [2 ]
机构：
[1] Shandong Xiehe Univ, Comp Coll, Jinan, Peoples R China
[2] Jinan Bodor CNC Machine Co Ltd, Jinan, Peoples R China
来源：
2020 2ND INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE COMMUNICATION AND NETWORK SECURITY (CSCNS2020) | 2021年 / 336卷
关键词：
D O I：
10.1051/matecconf/202133605021
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, I-GWO-KELM algorithm is used for short-term power load forecasting. Normalize the power data and meteorological data of the short-term power load, and use GWO to optimize the regularization coefficient of KELM and the RBF kernel parameters. To apply the model to short-term power load forecasting to obtain simulations for the next 24 hours and 168 hours curve. Experiments show that the improved model I3-GWO-KELM proposed in this paper has the best effect. The improvement of GWO in this paper is effective and feasible. In the application of short-term power load forecasting, the IGWO-KELM model is more accurate than the ELM and KELM models.
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页数：5
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