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公开(公告)号：US11859846B2
公开(公告)日：2024-01-02
申请号：US17318877
申请日：2021-05-12
Applicant: Johnson Controls Technology Company
Inventor： Priti Shinde , Kathiresan Rajagopal , Abu Bakr Khan , Young M. Lee
IPC: F24F11/63 , G05B19/042 , G06Q50/06 , G06N20/10
CPC classification number: F24F11/63 , G05B19/042 , G06N20/10 , G06Q50/06
Abstract: A heating, ventilation, and air conditioning (HVAC) fault prediction system for a building including a processing circuit including a processor and memory, the memory having instructions stored thereon that, when executed by the processor, cause the processing circuit to receive HVAC data relating to a plurality of HVAC components, the HVAC data indicating performance of the plurality of HVAC components, generate, based on the received HVAC data, a univariate prediction model and a multivariate prediction model, generate, using the received HVAC data, one or more predicted operational parameters for the plurality of HVAC components corresponding to a future time period, and execute at least one of the univariate prediction model or the multivariate prediction model on the one or more predicted operational parameters to predict a HVAC fault associated with at least one of the plurality of HVAC components to occur during the future time period.
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2.发明授权公开(公告)号：US11163271B2
公开(公告)日：2021-11-02
申请号：US16115282
申请日：2018-08-28
Applicant: Johnson Controls Technology Company
Inventor： Young M. Lee , William N. Schroeder
Abstract: A building energy system includes an energy storage system (ESS) configured to store energy received from an energy source and provide the stored energy to one or more pieces of building equipment. The system includes a local building system configured to collect building data and communicate the building data to a cloud platform and the cloud platform configured to receive the building data from the local building system via the network, determine whether to retrain a trained load prediction model based on at least some of the building data, retrain the trained load prediction model based on at least some of the building data in response to a determination to retrain the trained load prediction model, determine a load prediction for the building based on the retrained load prediction model, and cause the local building system to operate.
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公开(公告)号：US20210190364A1
公开(公告)日：2021-06-24
申请号：US16725961
申请日：2019-12-23
Applicant: Johnson Controls Technology Company
Inventor： Young M. Lee , Zhanhong Jiang , Viswanath Ramamurti , Sugumar Murugesan , Kirk H. Drees , Michael James Risbeck
Abstract: Systems and methods for training a reinforcement learning (RL) model for HVAC control are disclosed herein. Simulated experience data for the HVAC system is generated or received. The simulated experience data is used to initially train the RL model for HVAC control. The HVAC system operates within a building using the RL model and generates real experience data. A determination may be made to retrain the RL model. The real experience data is used to retrain the RL model. In some embodiments, both the simulated and real experience data are used to retrain the RL model. Experience data may be sampled according to various sampling functions. The RL model may be retrained multiple times over time. The RL model may be retrained less frequently over time as more real experience data is used to train the RL model.
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公开(公告)号：US20190302157A1
公开(公告)日：2019-10-03
申请号：US16366862
申请日：2019-03-27
Applicant: Johnson Controls Technology Company
Inventor： Steven R. Vitullo , Youngchoon Park , Sudhi R. Sinha , Karl Reichenberger , Young M. Lee , Ada L. Ma
Abstract: The present disclosure is directed to a method for performing energy analytics in a building management system. The method can include collecting respective data samples of one or more variables from building equipment during a first period of time and a second period of time. The method can include calculating a first plurality of values for one or more energy audit metrics based on the data samples collected during the first period of time and the second period of time. The method can include comparing the first plurality of values and second plurality of values. The method can include displaying, based on the comparison, at least one of the first plurality of values and/or at least one of the second plurality of values on a dashboard to facilitate adjustment of the one or move variables.
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公开(公告)号：US11669794B2
公开(公告)日：2023-06-06
申请号：US16841328
申请日：2020-04-06
Applicant: Johnson Controls Technology Company
Inventor： Sajjad Pourmohammad , Vish Ramamurti , Young M. Lee
IPC: G06F16/245 , G06F16/29 , G06F17/15 , G06F17/18 , G06Q10/0635
CPC classification number: G06Q10/0635 , G06F16/245 , G06F16/29 , G06F17/15 , G06F17/18
Abstract: A building risk analysis system including one or more memory devices storing instructions thereon, that, when executed by one or more processors, cause the one or more processors to receive threats, each of the threats including a location, wherein each of the threats are threats of a particular threat category, determine a number of threats for each of geographic areas based on the location of each of the threats, and generate a distribution based on the number of threats for each of the geographic areas. The instructions further cause the one or more processors to determine a risk score for each of the geographic areas based on one or more characteristics of the distribution and the number of threats for each of the geographic areas.
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Patent Agency Ranking
公开(公告)号：US20210373510A1
公开(公告)日：2021-12-02
申请号：US16885968
申请日：2020-05-28
Applicant: Johnson Controls Technology Company
Inventor： Surajit Borah , Santle Camilus , ZhongYi Jin , Vish Ramamurti , Young M. Lee
Abstract: A building system including one or more memory devices configured to store instructions that, when executed by one or more processors, cause the one or more processors to receive training data including acronym strings and tag strings, train a sequence to sequence neural network based on the training data, receive an acronym string for labeling, the acronym string comprising a particular plurality of acronyms, and generate a tag string for the acronym string with the sequence to sequence neural network, wherein the sequence to sequence neural network outputs a tag of the tag string for one acronym of the particular plurality of acronyms based on the one acronym and contextual information of the acronym string, wherein the contextual information includes other acronyms of the particular plurality of acronyms.
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公开(公告)号：US20210312351A1
公开(公告)日：2021-10-07
申请号：US16841328
申请日：2020-04-06
Applicant: Johnson Controls Technology Company
Inventor： Sajjad Pourmohammad , Vish Ramamurti , Young M. Lee
IPC: G06Q10/06 , G06F16/29 , G06F16/245 , G06F17/18 , G06F17/15
Abstract: A building risk analysis system including one or more memory devices storing instructions thereon, that, when executed by one or more processors, cause the one or more processors to receive threats, each of the threats including a location, wherein each of the threats are threats of a particular threat category, determine a number of threats for each of geographic areas based on the location of each of the threats, and generate a distribution based on the number of threats for each of the geographic areas. The instructions further cause the one or more processors to determine a risk score for each of the geographic areas based on one or more characteristics of the distribution and the number of threats for each of the geographic areas.
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8.发明授权公开(公告)号：US10713541B2
公开(公告)日：2020-07-14
申请号：US16125994
申请日：2018-09-10
Applicant: Johnson Controls Technology Company
Inventor： ZhongYi Jin , Young M. Lee , Youngchoon Park
Abstract: A method for classifying an occluded object includes receiving, by one or more processing circuits, an image of the object that is partially occluded by a foreign object and classifying, by the one or more processing circuits, the object of the image into one of one or more classes of interest via an artificial neural network (ANN) by determining a plurality of neuron activations of neurons of the ANN for one or more foreign classes and the one or more classes of interest, subtracting one or more of the neuron activations of the one or more foreign classes from the neuron activations of the one or more classes of interest, wherein the foreign object belongs to one of the one or more foreign classes, and classifying the object of the image into the one of the one or more classes of interest based on the subtracting.
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公开(公告)号：US11268996B2
公开(公告)日：2022-03-08
申请号：US16366862
申请日：2019-03-27
Applicant: Johnson Controls Technology Company
Inventor： Steven R. Vitullo , Youngchoon Park , Sudhi R. Sinha , Karl Reichenberger , Young M. Lee , Ada L. Ma
Abstract: The present disclosure is directed to a method for performing energy analytics in a building management system. The method can include collecting respective data samples of one or more variables from building equipment during a first period of time and a second period of time. The method can include calculating a first plurality of values for one or more energy audit metrics based on the data samples collected during the first period of time and the second period of time. The method can include comparing the first plurality of values and second plurality of values. The method can include displaying, based on the comparison, at least one of the first plurality of values and/or at least one of the second plurality of values on a dashboard to facilitate adjustment of the one or move variables.
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公开(公告)号：US20210373509A1
公开(公告)日：2021-12-02
申请号：US16885959
申请日：2020-05-28
Applicant: Johnson Controls Technology Company
Inventor： Surajit Borah , Santle Camilus , ZhongYi Jin , Vish Ramamurti , Young M. Lee , Jason B. Koh
Abstract: A building system including one or more memory devices configured to store instructions thereon that, when executed by one or more processors, cause the one or more processors to receive training data including acronym strings and tag strings, train a statistical model based on the training data, receive an acronym string for labeling, the acronym string comprising a particular plurality of acronyms, and generate a tag string for the acronym string with the statistical model, wherein the statistical model outputs a tag of the tag string for one acronym of the particular plurality of acronyms based on the one acronym and contextual information of the acronym string, wherein the contextual information includes other acronyms of the particular plurality of acronyms, wherein the statistical model implements a many to many mapping between the particular plurality of acronyms and a plurality of target tags.
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