Short term electricity forecasting using individual smart meter data

被引:72
作者
Gajowniczek, Krzysztof [1 ]
Zabkowski, Tomasz [1 ]
机构
[1] Warsaw Univ Life Sci, Fac Appl Informat & Math, PL-02776 Warsaw, Poland
来源
KNOWLEDGE-BASED AND INTELLIGENT INFORMATION & ENGINEERING SYSTEMS 18TH ANNUAL CONFERENCE, KES-2014 | 2014年 / 35卷
关键词
smart metering; short term electrictity forecasting; neural networks; support vector machines; forecast accuracy; NOISE DETECTION; LOAD;
D O I
10.1016/j.procs.2014.08.140
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Smart metering is a quite new topic that has grown in importance all over the world and it appears to be a remedy for rising prices of electricity. Forecasting electricity usage is an important task to provide intelligence to the smart gird. Accurate forecasting will enable a utility provider to plan the resources and also to take control actions to balance the electricity supply and demand. The customers will benefit from metering solutions through greater understanding of their own energy consumption and future projections, allowing them to better manage costs of their usage. In this proof of concept paper, our contribution is the proposal for accurate short term electricity load forecasting for 24 hours ahead, not on the aggregate but on the individual household level. (C) 2014 Published by Elsevier B.V.
引用
收藏
页码:589 / 597
页数:9
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