Daily Electricity Consumption Forecasting Based on Lazy Learning

摘要：

　　Daily electricity consumption is varying randomly.To improve forecasting accuracy,a Lazy Learning(LL)model is proposed.LL aims to build the regression forecasting models upon vectors which are chosen by K-vector nearest neighbors(K-VNN)method.K-VNN can solve overfitting problem and high accuracy can be ensured.Since there are many factors related to electricity consumption,Grey Ts correlation degree is used to determine key indexes to further improve the running efficiency of the model.In addition,fuzzy C-means(FCM)clustering is applied to explore the similar scenarios,then the searching scope of LL is reduced.A case studied in one building in Shanghai shows the proposed method can enhance the accuracy and efficiency of electricity consumption forecasting.

作者: Haiying Li Bingfang Yang

作者单位: Department of Electrical Engineering,University of Shanghai for Science and Technology,Yangpu District,Shanghai 200093,China

会议类型: 国际会议

会议名称: 2018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)(2018第二届电子信息技术与计算机工程国际会议)(EITCE2018)

会议地点: 上海

会议语种:英文

页码: 1-4

在线出版日期: 2018-10-12（万方平台首次上网日期，不代表论文的发表时间）