Dimensionality Expansion and Transfer Learning for Next Generation Energy Management Systems
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Electrical management systems (EMS) are playing a central role in enabling energy savings. They can be deployed within an everyday household where they monitor and manage appliances and help residents be more energy efficient and subsequently also more economical. One of they key functionalities of EMS is to automatically detect and identify appliances within a household through the process of load monitoring. In this paper, we propose a new transfer learning approach for building EMS (BEMS) and study the trade-offs in terms of numbers of samples and target classes in adapting a backbone model during the transfer process. We also perform a first time analysis of feature expansion through video-like transformation of time series data for device classification in non intrusive load monitoring (NILM) and propose a deep learning architecture enabling accurate appliance identification. We examine the relative performance of our method on 5 different representative low-frequency datasets and show that our method performs with an average F1 score of 0.88 on these datasets.
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