Residential Load Forecasting for Flexibility Prediction Using Machine Learning-Based Regression Model

被引：0

作者：

Ahmadiahangar, Roya ^{[1
]}

Haring, Tobias ^{[1
]}

Rosin, Argo ^{[1
]}

Korotko, Tarmo ^{[1
]}

Martins, Jodo ^{[2
]}

机构：

[1] Tallinn Univ Technol, Dept Elect Power Engn & Mechatron, Tallinn, Estonia

[2] Univ Nova Lisboa, Fac Sci & Technol, Dept Elect Engn, Lisbon, Portugal

来源：

2019 IEEE INTERNATIONAL CONFERENCE ON ENVIRONMENT AND ELECTRICAL ENGINEERING AND 2019 IEEE INDUSTRIAL AND COMMERCIAL POWER SYSTEMS EUROPE (EEEIC / I&CPS EUROPE) | 2019年

关键词：

machine learning; flexibility; home energy management; regression models; electricity consumption;

D O I：

暂无

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Flexibility is already an important concept in the context of electricity balancing. Residential customers' consumptions are usually highly volatile and depend on individual behaviour; hence, it complicates forecasts and flexibility extractions. This paper proposes the use of machine learning based regression models to generate load patterns for forecasting the potential flexibility of residential customers and improving both technical and economic smart grid operations. The advantage of proposed method is that it can be used, in online and real-time methods, in a wide range of control approaches.

引用

页数：4

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