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A convolutional autoencoder-based approach with batch normalization for energy disaggregation
The Journal of Supercomputing ( IF 3.3 ) Pub Date : 2020-07-10 , DOI: 10.1007/s11227-020-03375-y
Huan Chen , Yue-Hsien Wang , Chun-Hung Fan

Non-intrusive loading monitoring (NILM) is a load analyzing algorithm that performs the energy dis-aggregation of power load for the smart meter technology. NILM is a highly valuable application due to its cost effectiveness, but it is a very challenging research because the noisy low-level features are not easily distinguishable when multiple appliances are used together. This paper proposes a deep learning-based scheme, named the CAEBN-HC, to address this issue. The proposed CAEBN-HC is designed based on the one-dimensional convolutional neural networks (1D-CNN) autoencoder and uses advanced training techniques, particularly the batch normalization (BN) and hill climbing (HC) algorithm to solve the NILM problem. The 1D-CNN autoencoder is used to extract the temporal features, and the BN is used to re-adjust the output distribution of each layer to prevent the gradient vanishing or explosion problem in the training process. In addition, the HC is used to perform the hyperparameter tuning. The NILM problem is first modeled as a regression problem, and the proposed method can predict the target signal correctly. To validate the effectiveness of the proposed scheme, the REDD appliance and power usage dataset is applied as a benchmark for performance comparison. Results showed that the proposed CAEBN-HC performed the best when compared with the LSTM and the conventional convolutional autoencoder (CAE) scheme without batch normalization and hyperparameter optimization.

中文翻译:

一种基于卷积自编码器的能量分解批量归一化方法

非侵入式负载监测 (NILM) 是一种负载分析算法,可为智能电表技术执行电力负载的能量分解。由于其成本效益,NILM 是一个非常有价值的应用程序,但它是一项非常具有挑战性的研究,因为当多个设备一起使用时,嘈杂的低级特征不容易区分。本文提出了一种名为 CAEBN-HC 的基于深度学习的方案来解决这个问题。所提出的 CAEBN-HC 是基于一维卷积神经网络 (1D-CNN) 自动编码器设计的,并使用先进的训练技术,特别是批量归一化 (BN) 和爬山 (HC) 算法来解决 NILM 问题。1D-CNN 自动编码器用于提取时间特征,BN用于重新调整每一层的输出分布,以防止训练过程中出现梯度消失或爆炸问题。此外,HC 用于执行超参数调整。首先将NILM问题建模为回归问题,提出的方法可以正确预测目标信号。为了验证所提出方案的有效性,REDD 设备和电源使用数据集被用作性能比较的基准。结果表明,与 LSTM 和没有批量归一化和超参数优化的传统卷积自动编码器 (CAE) 方案相比,所提出的 CAEBN-HC 表现最好。首先将NILM问题建模为回归问题,提出的方法可以正确预测目标信号。为了验证所提出方案的有效性,REDD 设备和电源使用数据集被用作性能比较的基准。结果表明,与 LSTM 和没有批量归一化和超参数优化的传统卷积自动编码器 (CAE) 方案相比,所提出的 CAEBN-HC 表现最好。首先将NILM问题建模为回归问题,提出的方法可以正确预测目标信号。为了验证所提出方案的有效性,REDD 设备和电源使用数据集被用作性能比较的基准。结果表明,与 LSTM 和没有批量归一化和超参数优化的传统卷积自动编码器 (CAE) 方案相比,所提出的 CAEBN-HC 表现最好。
更新日期:2020-07-10
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