Patents by Inventor Selim MIMAROGLU
Selim MIMAROGLU has filed for patents to protect the following inventions. This listing includes patent applications that are pending as well as patents that have already been granted by the United States Patent and Trademark Office (USPTO).
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Publication number: 20240144004Abstract: Embodiments generate machine learning predictions to discover target device energy usage. One or more trained machine learning models configured to discover target device energy usage from source location energy usage can be stored. Multiple instances of source location energy usage over a period of time can be received for a given source location. Using the trained machine learning model, multiple discovery predictions for the received instances of source location energy usage can be generated, the discovery predictions comprising a prediction about a presence of target device energy usage within the instances of source location energy usage. And based on the multiple discovery predictions, an overall prediction about a presence of target device energy usage within the given source location's energy usage over the period of time can be generated.Type: ApplicationFiled: December 20, 2023Publication date: May 2, 2024Inventors: Selim MIMAROGLU, Oren BENJAMIN, Arhan GUNEL, Anqi SHEN, Ziran FENG
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Patent number: 11893487Abstract: Embodiments generate machine learning predictions to discover target device energy usage. One or more trained machine learning models configured to discover target device energy usage from source location energy usage can be stored. Multiple instances of source location energy usage over a period of time can be received for a given source location. Using the trained machine learning model, multiple discovery predictions for the received instances of source location energy usage can be generated, the discovery predictions comprising a prediction about a presence of target device energy usage within the instances of source location energy usage. And based on the multiple discovery predictions, an overall prediction about a presence of target device energy usage within the given source location's energy usage over the period of time can be generated.Type: GrantFiled: June 23, 2021Date of Patent: February 6, 2024Assignee: Oracle International CorporationInventors: Selim Mimaroglu, Oren Benjamin, Arhan Gunel, Anqi Shen, Ziran Feng
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Publication number: 20230419106Abstract: Embodiments select households using machine learning predictions. One or more trained machine learning models can be stored. For example, at least one machine learning model can be trained to predict household income using time-series energy usage data. Input data including time-series energy usage data for a plurality of households can be received. Using the trained machine learning models, a household income is predicted per household. A subset of the households with a predicted household income that meets one or more campaign criteria can be selected. For example, the selected subset of the households can be targeted by an energy campaign that corresponds to the campaign criteria, and the energy campaign comprise one or more actions to alter energy usage for the targeted households.Type: ApplicationFiled: November 14, 2022Publication date: December 28, 2023Inventors: Selim MIMAROGLU, Anqi SHEN, Oren BENJAMIN, Arhan GUNEL, Dmitriy FRADKIN, Ziran FENG, Zheng YANG
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Publication number: 20230244963Abstract: 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.Type: ApplicationFiled: April 5, 2023Publication date: August 3, 2023Applicant: Oracle International CorporationInventors: Selim MIMAROGLU, Arhan GUNEL, Oren BENJAMIN, Anqi SHEN
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Patent number: 11636356Abstract: 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.Type: GrantFiled: November 27, 2019Date of Patent: April 25, 2023Assignee: Oracle International CorporationInventors: Selim Mimaroglu, Arhan Gunel, Oren Benjamin, Anqi Shen
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Patent number: 11593645Abstract: Embodiments implement non-intrusive load monitoring using machine learning. A trained convolutional neural network (CNN) can be stored, where the CNN includes a plurality of layers, and the CNN is trained to predict disaggregated target device energy usage data from within source location energy usage data based on training data including labeled energy usage data from a plurality of source locations. Input data can be received including energy usage data at a source location over a period of time. Disaggregated target device energy usage can be predicted, using the trained CNN, based on the input data.Type: GrantFiled: November 27, 2019Date of Patent: February 28, 2023Assignee: Oracle International CorporationInventors: Selim Mimaroglu, Oren Benjamin, Arhan Gunel, Anqi Shen
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Patent number: 11544632Abstract: 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.Type: GrantFiled: November 27, 2019Date of Patent: January 3, 2023Assignee: Oracle International CorporationInventors: Selim Mimaroglu, Anqi Shen, Arhan Gunel, Oren Benjamin
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Publication number: 20220414446Abstract: Embodiments generate machine learning predictions to discover target device energy usage. One or more trained machine learning models configured to discover target device energy usage from source location energy usage can be stored. Multiple instances of source location energy usage over a period of time can be received for a given source location. Using the trained machine learning model, multiple discovery predictions for the received instances of source location energy usage can be generated, the discovery predictions comprising a prediction about a presence of target device energy usage within the instances of source location energy usage. And based on the multiple discovery predictions, an overall prediction about a presence of target device energy usage within the given source location's energy usage over the period of time can be generated.Type: ApplicationFiled: June 23, 2021Publication date: December 29, 2022Inventors: Selim MIMAROGLU, Oren BENJAMIN, Arhan GUNEL, Anqi SHEN, Ziran FENG
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Publication number: 20210158186Abstract: 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.Type: ApplicationFiled: November 27, 2019Publication date: May 27, 2021Applicant: Oracle International CorporationInventors: Selim MIMAROGLU, Arhan GUNEL, Oren BENJAMIN, Anqi SHEN
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Publication number: 20210158225Abstract: 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.Type: ApplicationFiled: November 27, 2019Publication date: May 27, 2021Inventors: Selim MIMAROGLU, Anqi SHEN, Arhan GUNEL, Oren BENJAMIN
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Publication number: 20210158150Abstract: Embodiments implement non-intrusive load monitoring using machine learning. A trained convolutional neural network (CNN) can be stored, where the CNN includes a plurality of layers, and the CNN is trained to predict disaggregated target device energy usage data from within source location energy usage data based on training data including labeled energy usage data from a plurality of source locations. Input data can be received including energy usage data at a source location over a period of time. Disaggregated target device energy usage can be predicted, using the trained CNN, based on the input data.Type: ApplicationFiled: November 27, 2019Publication date: May 27, 2021Inventors: Selim MIMAROGLU, Oren BENJAMIN, Arhan GUNEL, Anqi SHEN