Non-invasive load monitoring (NILM) is a vital step to realize the smart grid. Although the existing various NILM algorithms have made significant progress in energy consumption feedback, there are still some problems need to further addressed, such as the exponential growth of state space with the increase of the number of multi-state devices, which leads to the dimension disaster; and it is difficult to capture the power fluctuation information effectively because of the neglect of time-dependency problem load disaggregation; traditional disaggregation involves a process of one sequence to one sequence optimization, which is inefficient. In our study, a composite deep LSTM is proposed for load disaggregation. The proposed algorithm considers the process of load disaggregation as a signal separation process and establishes regression learning from a single sequence to multiple sequences to avoid dimension disaster. In addition, an encoder-separation-decoder structure is introduced for load disaggregation. Encoder completes the effective encoding of the mains power and differential power information, the time-dependency of the encoding process implemented by a deep LSTM, separation realizes the disaggregation process by separating the encoded information, and decoder decode the separated signal into the sequences of corresponding electrical appliances. Compared with the one sequence to one sequence disaggregation method, the proposed method simplified disaggregation complexity and improves the efficiency of disaggregation. The experimental results on WikiEnergy and REDD datasets show that the proposed method can reduce the disaggregation error and improve the comprehensive performance of event detection. Besides, our study can provide conditions for the realization of the bidirectional interaction of the smart grid and the improvement of the smart grid scheduling.

无创负载监控（NILM）是实现智能电网的重要一步。尽管现有的各种NILM算法在能耗反馈方面取得了显着进展，但仍然存在一些需要进一步解决的问题，例如随着多状态设备数量的增加，状态空间呈指数增长，这导致尺寸灾害; 由于忽略了时间相关问题负载分解，难以有效地捕获功率波动信息；传统的分解涉及一个序列到一个序列优化的过程，这效率低下。在我们的研究中，提出了一种复合深LSTM用于负荷分解。所提出的算法将负载分解过程视为信号分离过程，并建立了从单个序列到多个序列的回归学习，以避免维度灾难。另外，引入了编码器-分离-解码器结构以用于负载分解。编码器完成主电源和差分电源信息的有效编码，由深度LSTM实现的编码过程与时间有关，分离通过分离编码信息来实现分解过程，解码器将分离出的信号解码为相应的序列电器。与一序列一序列分解方法相比，该方法简化了分解复杂度，提高了分解效率。在WikiEnergy和REDD数据集上的实验结果表明，该方法可以减少分类错误并提高事件检测的综合性能。此外，我们的研究可以为实现智能电网的双向交互和改善智能电网调度提供条件。