Abstract: A system described herein may provide a technique for the automated detection of a particular type of lighting fixture, where a given type may refer to a particular make and/or model of the lighting fixture, bulb quantity, bulb technology, power output rating, etc. The detection may be based on measured dimming curves of the lighting fixture, which may indicate actual power consumption at varying brightness levels of the lighting fixture. One or more power consumption models may be selected, generated, and/or refined based on the measured dimming curves, and applied to other lighting fixtures sharing similar attributes and/or under similar conditions. Based on such models, a given lighting fixture may be able to automatically detect anomalous behavior (e.g., excessive power consumption or less power consumption compared to expected power consumption) and automatically take remedial actions.

Abstract: A system described herein may provide a technique for the automated detection of a particular type of lighting fixture, where a given type may refer to a particular make and/or model of the lighting fixture, bulb quantity, bulb technology, power output rating, etc. The detection may be based on measured dimming curves of the lighting fixture, which may indicate actual power consumption at varying brightness levels of the lighting fixture. One or more power consumption models may be selected, generated, and/or refined based on the measured dimming curves, and applied to other lighting fixtures sharing similar attributes and/or under similar conditions. Based on such models, a given lighting fixture may be able to automatically detect anomalous behavior (e.g., excessive power consumption or less power consumption compared to expected power consumption) and automatically take remedial actions.