Low frequency-based energy disaggregation using sliding windows and deep learning

The issue of controlling energy use is becoming extremely important. People’s behavior is one of the most important elements influencing electric energy usage in the residential sector, one of the most significant energy consumers globally. The building’s energy usage could be reduced by using feedback programs. Non-Intrusive Load Monitoring (NILM) approaches have emerged as one of the most viable options for energy disaggregation. This paper presents a deep learning algorithm using Long Short-Term Memory (LSTM) models for energy disaggregation. It employs low-frequency sampling power data collected in a private house. The aggregated active and reactive powers are used as inputs in a sliding window. The obtained results show that the proposed approach gives high performances in term of recognizing the devices' operating states and predicting the energy consumed by each device.