中国电机工程学报Issue(1)：37-42,6.DOI:10.13334/j.0258-8013.pcsee.2015.01.005
海量数据下的电力负荷短期预测
Short-term Power Load Forecasting Based on Big Data
摘要
Abstract
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Key words
big data/cloud computing/load forecasting/local weighted linear regression分类
信息技术与安全科学引用本文复制引用
张素香,赵丙镇,王风雨,张东..海量数据下的电力负荷短期预测[J].中国电机工程学报,2015,(1):37-42,6.基金项目
国家863高技术基金项目(2011AA05A116)。The National High Technology Research and Development of China 863 Program (2011AA05A116) （2011AA05A116）
