Multi-Channel Recurrent Convolutional Neural Networks for Energy Disaggregation

Power consumption signals of household appliances are characterized by randomly occurring events (e.g. switch-on events), making timeseries modeling a demanding process. In this paper, we propose a convolutional neural network (CNN)-based architecture with inputs and outputs formed as data sequences taking into consideration an appliance's previous states for better estimation of its current state. Furthermore, the proposed model endows CNN models with a recurrent property in order to better capture energy signal interdependencies. Using a multi-channel CNN architecture fed with additional variables related to power consumption (current, reactive, and apparent power), additionally to active power, overall performance, robustness to noise and convergence times are improved. The experimental results prove the proposed method's superiority compared to the current state of the art.