Non-Intrusive Load Monitoring Based on Unsupervised Optimization Enhanced Neural Network Deep Learning
Non-Intrusive Load Monitoring Based on Unsupervised Optimization Enhanced Neural Network Deep Learning
复制标题
基于无监督优化增强神经网络深度学习的非侵入式负载监控
DOI：
10.3389/fenrg.2021.718916
复制
发表时间：
2021-09-30
影响因子：
3.4
通讯作者：
Tan, Pengxiang
中科院分区：
文献类型：
--
作者：
Liu, Yu;Wang, Jiarui;Tan, Pengxiang
非侵入式负荷监测因其实现成本低、对能源用户干扰小等特点具有广阔的应用前景，近年来由于学习算法的发展在工业领域被寄予厚望。针对现场负荷监测的实用性和可靠性问题，提出了一种基于增强型神经网络学习算法的非侵入式负荷分解方法。该家电监控方法首先建立遵循有监督学习策略的神经网络模型，然后利用基于无监督学习的优化方法来增强对不同场景的灵活性和适应性，从而提高解聚性能。通过在REDD公共数据集上的验证，该方法在非侵入式负载监测中具有良好的性能。该方法在提高准确率的同时，还具有良好的可扩展性，能够有效地识别新加入的设备。
Non-intrusive load monitoring has broad application prospects because of its low implementation cost and little interference to energy users, which has been highly expected in the industrial field recently due to the development of learning algorithms. Targeting at the investigation of practical and reliable load monitoring in field implementations, a non-intrusive load disaggregation approach based on an enhanced neural network learning algorithm is proposed in this article. The presented appliance monitoring approach establishes the neural network model following the supervised learning strategy at first and then utilizes the unsupervised learning based optimization to enhance the flexibility and adaptability for diverse scenarios, leading to the improvement of disaggregation performance. By verifications on the REDD public dataset, the proposed approach is demonstrated to be with good performance in non-intrusive load monitoring. In addition to the accuracy enhancement, the proposed approach is also with good scalability, which is efficient in recognizing the newly added appliance.