基于基因表达式编程的中长期电力负荷预测挖掘

被引：11

作者：

傅靖

王栋

白阳

胡楠

机构：

[1] 国网江苏省电力有限公司南通供电分公司

来源：

电力信息与通信技术 | 2020年 / 18卷 / 05期

关键词：

负荷预测; 基因表达式编程; 人工智能; 函数挖掘;

D O I：

10.16543/j.2095-641x.electric.power.ict.2020.05.002

中图分类号：

TM715 [电力系统规划]; TP18 [人工智能理论];

学科分类号：

080802 [电力系统及其自动化]; 140502 [人工智能];

摘要：

准确、及时的中长期负荷预测对于制定经济合理的电力分配方案具有十分重要的意义。传统基于统计和人工智能的电力负荷预测算法存在计算效率低、预测准确率不高的问题。文章结合基因表达式编程的思想,提出基于基因表达式编程的中长期电力负荷预测算法(Mid-long Term Load Forecasting Model Mining based on Gene Expression Programming,LFMM-GEP)。在标准EUNITE数据集上的仿真实验结果表明,文章提出的LFMM-GEP算法在MAE和MAPE以及预测精度上,要优于传统的其他人工智能和统计方法。

引用

页码：7 / 12

页数：6

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