Non-Intrusive Load Disaggregation Using Semi-Supervised Learning Method
2019 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)(2019)
摘要
With the emerging of smart metering around the world, there is a growing demand to analyse the residential energy usage. In this paper, we propose a Deep Neural Network (DNN)-based approach for non-intrusive load monitoring (NILM), which can achieve effective and efficient estimation of individual appliance usage according to a single main meter reading in a non-intrusive manner. Considering practical situations, two training methods are provided. The first training approach is fully supervised learning, which requires a ground truth of label, indicating the state of the appliance (ON/OFF), to build a prediction model. The second training approach is semi-supervised learning, leading to better performance by F-Measure metric while only requiring some more unlabelled training data. Experimental results on the low-sample rate REDD dataset demonstrate the superior performance of our proposed DNN-based method compared with Hidden Markov Model (HMM)based and Graph Signal Processing (GSP)-based approaches.
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关键词
Energy Disaggregation,Non-Intrusive Load Monitoring,Deep Neural Network,Machine Learning,Semi-supervised Learning
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