A practical solution for non-intrusive type II load monitoring based on deep learning and post-processing

This paper presents a practical and effective non-intrusive load monitoring (NILM) solution to estimate the energy consumption for common multi-functional home appliances (type II appliances). Type II home appliances are usually the most challenging cases in load disaggregation because they usually have multiple power consumption states, complex state transitions, and multiple operational modes. The practicality of the proposed deep convolutional neural networks-based approach comes from the minimum prerequisite information from the previously unseen customers. That means no sub-metered information for the target appliances in the NILM service subscriber's house is needed to provide appliance level identification and estimate under the proposed architecture. Our solution also includes a novel post-processing technique that boost the performance significantly on type II home appliances. The effectiveness of the solution is evaluated on a public dataset to allow comparison with the previous works.

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