Deep Neural Network Based Energy Disaggregation
Deep Neural Network Based Energy Disaggregation
复制标题
基于深度神经网络的能量分解
DOI：
10.1109/sege.2018.8499441
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
E. Ambikairajah
中科院分区：
文献类型：
--
作者：
T. Sirojan;B. Phung;E. Ambikairajah
在智能电网中，能源分解通过从聚合的智能电表数据中估计电器级别的能耗，大大有助于更好地进行需求侧管理、负荷预测和节能。将卷积神经网络和变分自动编码器相结合，提出了一种基于深度神经网络的能量分解系统。使用家用电器级电量数据集(UK-DALE)以及平均绝对误差(MAE)和信号聚合误差(SAE)等标准误差度量来评估所提出的系统性能。测试结果表明，基于SAE和MAE的系统性能分别提高了44%和19%。
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.
使用神经网络进行序列到点学习，用于非侵入式负载监控
DOI：
10.48550/arxiv.1612.09106
发表时间：
2016
期刊：
arXiv e-prints
影响因子：
--
作者：
Zhang Chaoyun
通讯作者：
Zhang Chaoyun