IoT Research Center, PNU, Busan, Republic of Korea.
Pusan National University, Busan, Republic of Korea.
Comput Intell Neurosci. 2017;2017:4216281. doi: 10.1155/2017/4216281. Epub 2017 Oct 2.
Monitoring electricity consumption in the home is an important way to help reduce energy usage. Nonintrusive Load Monitoring (NILM) is existing technique which helps us monitor electricity consumption effectively and costly. NILM is a promising approach to obtain estimates of the electrical power consumption of individual appliances from aggregate measurements of voltage and/or current in the distribution system. Among the previous studies, Hidden Markov Model (HMM) based models have been studied very much. However, increasing appliances, multistate of appliances, and similar power consumption of appliances are three big issues in NILM recently. In this paper, we address these problems through providing our contributions as follows. First, we proposed state-of-the-art energy disaggregation based on Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) model and additional advanced deep learning. Second, we proposed a novel signature to improve classification performance of the proposed model in multistate appliance case. We applied the proposed model on two datasets such as UK-DALE and REDD. Via our experimental results, we have confirmed that our model outperforms the advanced model. Thus, we show that our combination between advanced deep learning and novel signature can be a robust solution to overcome NILM's issues and improve the performance of load identification.
监测家庭用电量是帮助减少能源使用的重要方法。非侵入式负载监测（NILM）是一种现有的技术，可以帮助我们有效地监测电力消耗，而不会增加成本。NILM 是一种很有前途的方法，可以从配电系统中电压和/或电流的总测量中获得单个电器的电力消耗估计。在之前的研究中，基于隐马尔可夫模型（HMM）的模型已经得到了广泛的研究。然而，最近 NILM 面临着三个大问题：电器数量增加、电器多态性和类似的电力消耗。在本文中，我们通过提供以下贡献来解决这些问题。首先，我们提出了基于长短期记忆递归神经网络（LSTM-RNN）模型和附加先进的深度学习的最新能源分解方法。其次，我们提出了一种新的特征来提高所提出模型在多态电器情况下的分类性能。我们将所提出的模型应用于两个数据集，如 UK-DALE 和 REDD。通过我们的实验结果，我们已经证实我们的模型优于先进的模型。因此，我们表明，我们将先进的深度学习与新特征相结合，可以为克服 NILM 的问题和提高负载识别性能提供一个稳健的解决方案。
