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US 2025/0031292 A1Published application
Lighting load classification and dimmer configuration based thereon
AssigneeLeviton Manufacturing Co., Inc.
InventorsA M Sarwar Jahan, Wen Gu, Tianjian Huang
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Abstract
Approaches are provided for lighting load classification using machine learning models, and selection of parameters for dimmer operation based on the classification. Approaches provide requests and/or use of updated models, building datasets for model construction, model construction, evaluation, and selection, application of machine learning models and handling unsuccessful classification attempts or classification into a class corresponding to an unknown lighting load type, and selection of operating parameters based on the foregoing.
Claims20
A dimmer for controlling conduction of a supply of power to a lighting load, the dimmer including:
- a line input terminal and a load output terminal, the line input terminal configured to be electrically coupled to the supply of power, and the load output terminal configured to be electrically coupled to the lighting load;
- a switching circuit electrically coupled in series between the line input terminal and the load output terminal, the switching circuit configured to be selectively controlled between an ON state and an OFF state;
- a memory;
- and
- a processing circuit in communication with the memory, wherein the dimmer is configured to perform a method including:
- based on conducting the supply of power to the lighting load, obtaining electrical data representing properties of electrical current through the lighting load over a duration of time;
- communicating with a remote entity for application of an updated machine learning model to classify the lighting load based on the obtained electrical data;
- receiving, from the remote entity, a response to the communicating, the response being dependent on compute resources of the dimmer;
- and
- based on the response from the remote entity, configuring the dimmer with one or more dimmer operating parameters that control operation of the dimmer.
7 more elements
A method including:
- building a dataset of electrical data for machine learning model training, the building the dataset including:
- sampling and storing, to the dataset, and for each different lighting load of a collection of different lighting loads, electrical data representing properties of electrical current through the lighting load, wherein for each different lighting load, the sampling is taken over a plurality of periods of an electrical waveform having a phase and the sampled electrical data is sampled at various angles of the phase across the plurality of periods, wherein the collection of different lighting loads include a plurality of different light-emitting diode (LED) lighting loads and at least one compact fluorescent lamp lighting load;
- and
- augmenting the dataset, the augmenting including:
- iteratively applying a phase shift to values of the sampled electrical data to produce additional values at corresponding shifted angles of the phase;
- and
- adding the produced additional values to the dataset;
- wherein the dataset includes time-domain data and frequency-domain data;
- and
- constructing a machine learning model to use in classifying target lighting loads into a plurality of different lighting load classes based on properties of electrical current through the target lighting loads, the plurality of different lighting load classes corresponding to different lighting load types of the collection of different lighting loads, and wherein the constructing includes:
- building and training, using the dataset, including the time-domain data and the frequency-domain data of the dataset, a plurality of different neural networks, the plurality of different neural networks having varying model parameters and being built and trained using varying artificial intelligence platforms;
- evaluating performance of the different neural networks based on performance requirements that include accuracy;
- and
- selecting a neural network of the built and trained plurality of different neural networks as the machine learning model to use, wherein the selected neural network includes a multilayer perceptron having at least one hidden layer.
11 more elements
A method including:
- obtaining a machine learning model configured for classifying lighting loads into different lighting load classes based on properties of electrical current through the lighting loads, the different lighting load classes corresponding to different lighting load types;
- obtaining electrical data representing properties of electrical current through a target lighting load to be classified;
- applying the machine learning model using the obtained electrical data to classify the target lighting load into a lighting load class, wherein the applying provides, for each class of one or more of the different lighting load classes, a respective confidence level that the target lighting load is properly classified into the class, and wherein a result of the applying is an unsuccessful classification of the target lighting load or a classification of the target lighting load in a class designated for an unknown type of lighting load;
- and
- based on the applying, determining one or more operating parameters to control operation of a dimmer in dimming the target lighting load.
2 more elements
Dependencies are inferred from the claim text.