  Individual device loads and energy consumption feedback is one of the
important approaches for pursuing users to save energy in residences. This can
help in identifying faulty devices and wasted energy by devices when left On
unused. The main challenge is to identity and estimate the energy consumption
of individual devices without intrusive sensors on each device. Non-intrusive
load monitoring (NILM) or energy disaggregation, is a blind source separation
problem which requires a system to estimate the electricity usage of individual
appliances from the aggregated household energy consumption. In this paper, we
propose a novel deep neural network-based approach for performing load
disaggregation on low frequency power data obtained from residential
households. We combine a series of one-dimensional Convolutional Neural
Networks and Long Short Term Memory (1D CNN-LSTM) to extract features that can
identify active appliances and retrieve their power consumption given the
aggregated household power value. We used CNNs to extract features from main
readings in a given time frame and then used those features to classify if a
given appliance is active at that time period or not. Following that, the
extracted features are used to model a generation problem using LSTM. We train
the LSTM to generate the disaggregated energy consumption of a particular
appliance. Our neural network is capable of generating detailed feedback of
demand-side, providing vital insights to the end-user about their electricity
consumption. The algorithm was designed for low power offline devices such as
ESP32. Empirical calculations show that our model outperforms the
state-of-the-art on the Reference Energy Disaggregation Dataset (REDD).
