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Non-Intrusive Load Monitoring Using Machine Learning and Processed Training Data
Non-Intrusive Load Monitoring Using Machine Learning and Processed Training Data
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机译:使用机器学习和处理训练数据的非侵入式负载监控
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摘要
Embodiments implement non-intrusive load monitoring using a novel learning scheme. A trained machine learning model configured to disaggregate device energy usage from household energy usage can be stored, where the machine learning model is trained to predict energy usage for a target device from household energy usage. Household energy usage over a period of time can be received, where the household energy usage includes energy consumed by the target device and energy consumed by a plurality of other devices. Using the trained machine learning model, energy usage for the target device over the period of time can be predicted based on the received household energy usage.
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