Abstract
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.
Highlights
Non-Intrusive Load Monitoring (NILM) estimates individual appliance power usage from aggregate measurements, thereby contributing to energy conservation through changing of consumers behavior, waste minimization, carbon footprint reduction, efficient network load handling and financial savings
A number of recently proposed methods are based on deep learning, aiming to leverage the increased representational capabilities of models such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks and Stacked Denoising Autoencoders
According to the already implemented approaches [6], sequence to sequence learning for NILM maps the input sequence of the aggregate signal to a same length output sequence of appliance’s active power via LSTM networks
Summary
Non-Intrusive Load Monitoring (NILM) estimates individual appliance power usage from aggregate measurements, thereby contributing to energy conservation through changing of consumers behavior, waste minimization, carbon footprint reduction, efficient network load handling and financial savings. The significance of the application has attracted the interest of an increasing number of researchers, leading to the proposal of a wide range of machine learning and signal processing techniques for energy disaggregation. We present a novel scalable CNN-based approach for energy disaggregation, called Multi-Channel Recurrent Tapped Delay Line CNN (MR-TDLCNN) which introduces. The architecture employs a second CNN, which utilizes the same inputs, along with the disaggregated signal of the first CNN. This approach allows for further noise reduction resulting in better overall disaggregation performance.
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