Deep Neural Network Based Energy Disaggregation

被引：0

作者：

Sirojan, T. ^{[1
]}

Phung, B. T. ^{[1
]}

Ambikairajah, E. ^{[1
]}

机构：

[1] Univ New South Wales, Sch Elect Engn & Telecommun, Sydney, NSW, Australia

来源：

2018 THE 6TH IEEE INTERNATIONAL CONFERENCE ON SMART ENERGY GRID ENGINEERING (SEGE 2018) | 2018年

关键词：

energy disaggregation; deep learning; pattern recognition; non-intrusive load monitoring; convolutional neural networks; variational autoencoders;

D O I：

暂无

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

In smart electricity grids, energy disaggregation significantly contributes to better demand side management, load forecasting and energy savings via estimating appliance level energy consumption from the aggregated smart meter data. This paper proposes a deep neural network based system by combining convolutional neural networks and variational auto-encoders for energy disaggregation. Domestic Appliance-Level Electricity dataset (UK-DALE) is used along with the standard error measures such as Mean Absolute Error (MAE) and Signal Aggregate Error (SAE) in order to evaluate the proposed system performance. Test results show that the proposed system improves the state-of-the-art performance by 44% and 19% based on SAE and MAE respectively.

引用

页码：73 / 77

页数：5

共 21 条

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