基于基因表达式编程的中长期电力负荷预测挖掘  

Mid-Long Term Load Forecasting Model Mining Based on Gene Expression Programming

作　　者：傅靖 王栋 白阳 胡楠 FU Jing;WANG Dong;BAI Yang;HU Nan(Nantong Power Supply Company,State Grid Jiangsu Electric Power Co.,Ltd.,Nantong 226000,China)

机构地区：[1]国网江苏省电力有限公司南通供电分公司,江苏南通226000

出　　处：《电力信息与通信技术》2020年第5期7-12,共6页Electric Power Information and Communication Technology

基　　金：国网江苏省电力有限公司科技项目资助“典型高比例分布式电源接入沿海地区的能源互联关键技术研究”(No.J2019098);国家自然科学基金项目“网络攻击下能源互联网数据容侵评估及可靠存储机制研究”(No.51977113)。

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。Accurate,timely mid-long term load forecasting is of great significance for formulating economical and reasonable power distribution schemes.Traditional power load forecasting algorithms based on statistics and artificial intelligence have low calculation efficiency and low prediction accuracy.This paper proposes a mid-long term load forecasting model mining algorithm based on gene expression programming(LFMM-GEP).Experimental results on the standard EUNITE dataset show that the proposed LFMM-GEP algorithm is superior to other traditional artificial intelligence and statistical methods in terms of MAE,MAPE and prediction accuracy.

关 键 词：负荷预测 基因表达式编程 人工智能 函数挖掘 

分 类 号：TM715[电气工程—电力系统及自动化]