Getting the specific energy consumption information of each appliance is an effective way to reduce energy wastage and improve energy efficiency. The non-intrusive load monitoring is a promising solution to achieve this goal. In terms of the event-based non-intrusive load monitoring, the load transient identification models have attracted great attention recently. In this study, a hybrid load transient identification model based on multivariate fusion, time series data augmentation, and deep neural network computation is proposed. In the hybrid model, the multivariate LSTM neural network is utilized to extend the traditional template matching methods to the deep learning field, which is beneficial to improve the identification accuracy and the efficiency in the testing stage. A hybrid time series data augmentation framework is designed to handle the class-imbalance and insufficient sample size, which is often the case in non-intrusive load monitoring. The effectiveness of each component, as well as the whole hybrid model, are evaluated based on the BLUED dataset. The comparison results indicate that (a) multivariate LSTM neural network is effective in modeling the characters of the load transient; (b) the proposed hybrid time series data augmentation framework is effective in improving the overall performance of the multivariate LSTM neural network; (c) taking identification accuracy and the application efficiency in testing stage into consideration, the proposed hybrid model performs better than that of the state of the art Dynamic Time Warping (DTW) based models.

获取每个设备的特定能耗信息是减少能源浪费和提高能源效率的有效方法。非侵入式负载监控是实现此目标的有希望的解决方案。在基于事件的非侵入式负载监控方面，负载瞬态识别模型最近引起了人们的极大关注。本文提出了一种基于多元融合，时间序列数据扩充和深度神经网络计算的混合负荷暂态辨识模型。在混合模型中，利用多元LSTM神经网络将传统的模板匹配方法扩展到深度学习领域，有利于提高测试阶段的识别精度和效率。混合时间序列数据扩充框架旨在处理类不平衡和样本量不足的情况，这在非侵入式负载监视中通常是这种情况。基于BLUED数据集评估每个组件以及整个混合模型的有效性。比较结果表明：（a）多元LSTM神经网络可有效地模拟负载瞬态特性；（b）拟议的混合时间序列数据扩充框架可有效改善多元LSTM神经网络的整体性能；（c）考虑到识别准确性和测试阶段的应用效率，提出的混合模型的性能优于基于动态时间规整（DTW）的模型。在非侵入式负载监视中通常是这种情况。基于BLUED数据集评估每个组件以及整个混合模型的有效性。比较结果表明：（a）多元LSTM神经网络可有效地模拟负载瞬态特性；（b）拟议的混合时间序列数据扩充框架可有效改善多元LSTM神经网络的整体性能；（c）考虑到识别准确性和测试阶段的应用效率，提出的混合模型的性能优于基于动态时间规整（DTW）的模型。在非侵入式负载监视中通常是这种情况。基于BLUED数据集评估每个组件以及整个混合模型的有效性。比较结果表明：（a）多元LSTM神经网络可有效地模拟负载瞬态特性；（b）拟议的混合时间序列数据扩充框架可有效改善多元LSTM神经网络的整体性能；（c）考虑到识别准确性和测试阶段的应用效率，提出的混合模型的性能优于基于动态时间规整（DTW）的模型。比较结果表明：（a）多元LSTM神经网络可有效地模拟负载瞬态特性；（b）拟议的混合时间序列数据扩充框架可有效改善多元LSTM神经网络的整体性能；（c）考虑到识别准确性和测试阶段的应用效率，提出的混合模型的性能优于基于动态时间规整（DTW）的模型。比较结果表明：（a）多元LSTM神经网络可有效地模拟负载瞬态特性；（b）拟议的混合时间序列数据扩充框架可有效改善多元LSTM神经网络的整体性能；（c）考虑到识别准确性和测试阶段的应用效率，提出的混合模型的性能优于基于动态时间规整（DTW）的模型。