Recurrent LSTM Architecture for Appliance Identification in Non-Intrusive Load Monitoring

Non-Intrusive Load Monitoring (NILM) techniques are commonly used to measure and identify the power consumption of different types of household appliances, starting from an aggregated signal obtained from a single measurement point. Currently, they are often based on the extended deployment of smart meters (SM) carried out in most developed countries in the last decade. Based on the measurements acquired by SMs, it is possible to disaggregate energy consumption, and then to identity the corresponding loads plugged to the mains of a house or building. NILM techniques can be applied in different application fields, such as energy efficiency, active demand response management, or even as a way to infer the behaviour patterns of the people living in a certain household under monitoring, in the context of Ambient Intelligence for Independent Living (AIIL). This paper presents a new approach for energy disaggregation through the use of Recurrent Neural Networks (RNN) based on measurements from a single point at low sampling rates. The proposed framework takes the power signals acquired by an SM as inputs, then pre-processes and detects the on/off switching events of the different appliances considered, and finally classifies them using two different Long Short Term Memory (LSTM)-based topologies. The proposal validation is carried out through the use of the well-known public dataset Building Level fUlly labelled for Electricity Disaggregation (BLUED). Several configurations of classification topologies have been compared, obtaining an average classification accuracy in the experimental results that exceeds 85%.