海量数据下的电力负荷短期预测  

Short-term Power Load Forecasting Based on Big Data

作　　者：张素香[1] 赵丙镇[1] 王风雨 张东[3] 

机构地区：[1]国家电网公司信息通信分公司,北京市西城区100761 [2]北京国电通网络技术有限公司,北京市丰台区100070 [3]国家电网公司农电工作部,北京市西城区100031

出　　处：《中国电机工程学报》2015年第1期37-42,共6页Proceedings of the CSEE

基　　金：国家863高技术基金项目(2011AA05A116)~~

摘　　要：该文研究海量数据下的短期电力负荷预测方法,基于局部加权线性回归和云计算平台,建立并行局部加权线性回归模型。同时,为剔除坏数据,采用最大熵建立坏数据分类模型,保证历史数据的有效性。实验数据来自已建的甘肃某智能园区。实验结果表明,提出的并行局部加权模型用于短期电力负荷预测是可行的,平均均方根误差为3.01%,完全满足负荷预测的要求,并极大地减少了负荷预测时间,提高预测精度。The short-term power load forecasting method had been researched based on the big data. And combined the local weighted linear regression and cloud computing platform, the parallel local weighted linear regression model was established. In order to eliminate the bad data, bad data classification model was built based on the maximum entropy algorithm to ensure the effectiveness of the historical data. The experimental data come from a smart industry park of Gansu province. Experimental results show that the proposed parallel local weighted linear regression model for short-term power load forecasting is feasible; and the average root mean square error is 3.01% and fully suitable for the requirements of load forecasting, moreover, it can greatly reduce compute time of load forecasting, and improve the prediction accuracy.

关 键 词：大数据 云计算 负荷预测 局部加权线性回归 

分 类 号：TM715[电气工程—电力系统及自动化]