Parallel LSTM Architectures for Non-Intrusive Load Monitoring in Smart Homes

Non-Intrusive Load Monitoring (NILM) is becoming popular as an appliance monitoring technique that can extract detailed power consumption information related to the different appliances used in a household. NILM utilises time series analysis methods to disaggregate signals of operating appliances from a single point in houses. Based on that it can help to provide energy consumption advice for household owners. NILM is known to be a challenging task from a computational aspect. This study proposes deep neural network models with a Parallel Long Short-Term Memory Topology (PLT) and evaluates them on the public REDD dataset. The new models' experimental evaluation results exhibit a significant improvement in four main state-based and energy-based metrics in comparison to previous results obtained using deep denoising autoencoders.