A multi-output LSTM-CNN learning scheme for power disaggregation within a NILM framework

Abstract

The non-deterministic home appliances’ behaviour makes aggregated power consumption hard to be explored and to identify individual appliances’ consumption (disaggregation) in residential buildings. This paper presents a deep neural network learning scheme in order to disaggregate a main meter’s aggregated signal into 11 appliances’ signals and estimate their individual power consumption. A 1-Dimensional Convolution Neural Network (1D CNN) and Long Short-Term Memory (LSTM) layers are used together to form a sequence-to-point (S2P) and a sequence-to-sequence (S2S) Multi-Target Regressor (MTR) for learning and recognizing the loads. Our model is fed with the home total real power (P), total reactive power (Q) and total current (I) and outputs the disaggregated real power (P) for each appliance. The model was trained and evaluated on the AMPds2 public dataset which results in a global disaggregation accuracy of 93.27% for the S2P model and 87.79% for the S2S model. The S2P model outperforms the existing methods in terms of disaggregation accuracy and the number of disaggregated appliances (11 appliances instead of 9) on the used database.