  Energy disaggregation or nonintrusive load monitoring (NILM), is a
single-input blind source discrimination problem, aims to interpret the mains
user electricity consumption into appliance level measurement. This article
presents a new approach for power disaggregation by using a deep recurrent long
short term memory (LSTM) network combined with convolutional neural networks
(CNN). Deep neural networks have been shown to be a significant way for these
types of problems because of their complexity and huge number of trainable
paramters. Hybrid method that proposed in the article could significantly
increase the overall accuracy of NILM because it benefits from both network
advantages. The proposed method used sequence-to-sequence learning, where the
input is a window of the mains and the output is a window of the target
appliance. The proposed deep neural network approach has been applied to
real-world household energy dataset "REFIT". The REFIT electrical load
measurements dataset described in this paper includes whole house aggregate
loads and nine individual appliance measurements at 8-second intervals per
house, collected continuously over a period of two years from 20 houses around
the UK. The proposed method achieve significant performance, improving accuracy
and F1-score measures by 95.93% and 80.93% ,respectively which demonstrates the
effectiveness and superiority of the proposed approach for home energy
monitoring. Comparison of proposed method and other recently published method
has been presented and discussed based on accuracy, number of considered
appliances and size of the deep neural network trainable parameters. The
proposed method shows remarkable performance compare to other previous methods.
