Non-intrusive load monitoring using ensemble machine learning techniques

Embodiments implement non-intrusive load monitoring using ensemble machine learning techniques. A first trained machine learning model configured to disaggregate target device energy usage from source location energy usage and a second trained machine learning model configured to detect device energ...

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Bibliographische Detailangaben
Hauptverfasser:	Benjamin, Oren, Mimaroglu, Selim, Shen, Anqi, Gunel, Arhan
Format:	Patent
Sprache:	eng
Schlagworte:	CALCULATING CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTINGELECTRIC POWER COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING CONVERSION OR DISTRIBUTION OF ELECTRIC POWER COUNTING ELECTRICITY GENERATION PHYSICS SYSTEMS FOR STORING ELECTRIC ENERGY
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Zusammenfassung:	Embodiments implement non-intrusive load monitoring using ensemble machine learning techniques. A first trained machine learning model configured to disaggregate target device energy usage from source location energy usage and a second trained machine learning model configured to detect device energy usage from source location energy usage can be stored, where the first trained machine learning model is trained to predict an amount of energy usage for the target device and the second trained machine learning model is trained to predict when a target device has used energy. Source location energy usage over a period of time can be received, where the source location energy usage includes energy consumed by the target device. An amount of disaggregated target device energy usage over the period of time can be predicted, using the first and second trained machine learning models, based on the received source location energy usage.