文章基本信息

标题：Daily Electricity Consumption Forecasting Based on Lazy Learning
本地全文：下载
作者：Haiying Li ; Bingfang Yang
期刊名称：MATEC Web of Conferences
电子版ISSN：2261-236X
出版年度：2018
卷号：232
DOI：10.1051/matecconf/201823204041
语种：English
出版社：EDP Sciences
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 byK-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 T's 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.