Short-term Power Load Forecasting Based on Balanced KNN
To improve the accuracy of load forecasting,a short-term load forecasting model based on balanced KNN algorithm is proposed; According to the load characteristics,the historical data of massive power load are divided into scenes by the K-means algorithm; In view of unbalanced load scenes,the balanced KNN algorithm is proposed to classify the scene accurately; The local weighted linear regression algorithm is used to fitting and predict the load; Adopting the Apache Hadoop programming framework of cloud computing,the proposed algorithm model is parallelized and improved to enhance its ability of dealing with massive and high-dimension data.The analysis of the household electricity consumption data for a residential district is done by 23-nodes cloud computing cluster,and experimental results show that the load forecasting accuracy and execution time by the proposed model are the better than those of traditional forecasting algorithm.
Xianlong Lv Xingong Cheng YanShuang TANG Yan-mei
School of Electrical Engineering,University of Jinan,Jinan,China Zaozhuang power supply company of Shandong province,Zaozhuang,China China Electric Power Research Institute,Beijing,China
国际会议
上海
英文
1-9
2017-12-28（万方平台首次上网日期，不代表论文的发表时间）