DeepEdge-NILM: A case study of non-intrusive load monitoring edge device in commercial building

Abstract

Non-intrusive load monitoring (NILM) has become an emerging technology in the energy sector for its effectiveness in the energy disaggregation of individual loads from the measured aggregated energy in main power supply of building. NILM has also evolved gradually over the past few decades, due to the significant advancements in artificial intelligence (AI), embedded/edge devices, and internet of things (IoT). From the review of literature, we observed that most of the NILM systems were only capable of monitoring different loads and it fails to detect and disaggregate the energy consumption of similar loads effectively. Further, most of works in the literature were focused on improving the performance of energy disaggregation of different loads in residential buildings and very limited works have been carried out for the similar loads in NILM system. Therefore, there is a necessity to study and evaluate the state of the art NILM algorithms for similar loads in real case scenarios for effective implementation in the commercial and industrial buildings. In this paper, we present a case study of the developed DeepEdge-NILM device which has been installed in a commercial building (office environment), where multiple similar and identical air conditioners (AC's) are used in the monitoring environment. A deep learning framework, Long Short-Term Memory (LSTM) has been implemented on the new set of features which comprises of electrical features and intrinsic features derived from the basic electric features using geometric mean for capturing the signature of similar loads effectively. From our case study, we observed that the performance of the deployed NILM system outperforms the baseline systems that have been developed using basic electrical features significantly. Finally, the limitations and future scope of the NILM edge device in the commercial buildings are also discussed.