张思,李洋,王波,朱耿,贺旭
ZHANG Si,LI Yang,WANG Bo,ZHU Geng,HE Xu

• 1:国网浙江省电力有限公司
• 2:国网浙江省电力有限公司宁波供电公司

摘要(Abstract)：

针对钢铁行业负荷预测精度不高的问题，提出了基于ICEEMDAN（改进的自适应噪声完备集合经验模态分解）的钢铁行业负荷预测方法。首先，通过ICEEMDAN将钢铁行业电力负荷分解为高、低频模态分量。其次，基于GA（遗传）算法筛选出高、低频分量的主要影响因素，并采用PSO-BP（粒子群优化-反向传播神网络）算法分别构建高、低频分量预测模型。最后，将各组分量的预测结果迭代重组，得到最终的负荷预测结果。算例分析结果表明，相较于其他预测方法，该方法的预测误差小，精度较高。
In response to the challenge of low accuracy in load forecasting for the steel industry, a load forecasting approach is introduced based on ICEEMDAN(improved complete ensemble empirical mode decomposition with adaptive noise). Firstly, ICEEMDAN is employed to decompose the electricity load in the steel industry into high and low-frequency modal components. Subsequently, the genetic algorithm(GA) is used to identify the primary influencing factors of the components. The PSO-BP(particle swarm optimization-backpropagation neural network) algorithm is then applied to construct prediction models for the high and low-frequency components. Finally, the forecasted results of each component are iteratively recombined to derive the ultimate load forecasting outcomes. Case analysis results indicate that this approach exhibits minimal prediction errors and higher precision.

关键词(KeyWords)： 冲击负荷;负荷预测;ICEEMDAN模型;遗传算法;PSO-BP模型
impact load;load forecasting;ICEEMDAN model;GA;PSO-BP model

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金（72371101）;; 国网浙江省电力有限公司科技项目（B311NB220001）

作者(Author): 张思,李洋,王波,朱耿,贺旭
ZHANG Si,LI Yang,WANG Bo,ZHU Geng,HE Xu

DOI: 10.19585/j.zjdl.202405004

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