International Business Machines Corporation

Johnson Controls Tyco IP Holdings Llp

Johnson Controls Technology Company

Ul LLC

Globalfoundries Inc.

Young Min Lee

Sugumar Murugesan

Kyong Min Yeo

Zhongyi Jin

Viswanath Ramamurti

Kirk H Drees

Jayant R Kalagnanam

Markus Ettl

Zhanhong Jiang

Youngchoon Park

Steven R Vitullo

Michael Risbeck

Jaume Amores

Jian Wang

Jie Ma

Rui Zhang

Robert J Baseman

Christian Makaya

Rajiv Ramanasankaran

Nizar Lethif

Guan Zhang

Nam H Nguyen

Feng Cheng

Edward John Pring

Jaume Amores Llopis

Hui Qi

Young T Chae

Sudhi R Sinha

Fei Liu

Pawan Raghunath Chowdhary

Jin Dong

Karl F Reichenberger

Raya Horesh

Kelsey Carle Schuster

Soumyadip Ghosh

Anshul Sheopuri

Thomas Robert Ervolina

Changrui Ren

Chandrasekhara K Reddy

Karthik Sourirajan

Hong Wei Ding

Lianjun An

Daniel John Peters

William N Schroeder

Hongxia Yang

Vish Ramamurti

Dhaval Patel

Henan Wang

Sudhi Ranjan Sinha

Devendra K Sadana

Wei Wang

Jeehwan Kim

Vaidhyanathan Venkiteswaran

Augustin J Hong

Yada Zhu

Walter Andrew Martin

Miguel Galvez

Ambuj Shatdal

Ian Christopher Westmacott

Ryan A Piaskowski

Sajjad Pourmohammad

Clay G Nesler

Ada L Ma

Shawn D Schubert

Dhavalkumar C Patel

Abu Bakr Khan

Surajit Borah

Andrew J Boettcher

Jason T Sawyer

Donald R Albinger

Daniel M Curtis

Chenlu Zhang

Thomas M Seneczko

Amit Rozner

Yohay Falik

Youngdeok Hwang

Minmin Qiu

Edward W Armstrong

Siddharth Goyal

Huijing Jiang

Priti Shinde

Kathiresan Rajagopal

Joseph P DeMarco

Leo S Liberti

Stephen John Buckley

Kenneth M Cyprys

Santle Camilus

Wei Crl Wang

Clifford H Copass

Systems and methods for assessing AI safety scores for AI-based products and facilitating the certification of these products based on their adherence to established AI safety principles, are provided. A trained machine learning model determines the relevancy of specific safety requirements to a given AI-based product. A degree of conformance of the AI-based product to these relevant requirements is determined and relative weights to each requirement are assigned. The AI safety score is calculated based on these parameters, with the AI-based product achieving AI safety certification if the score meets or exceeds a configurable safety threshold.

Automated technologies for assessing and certifying the safety of AI-based products based on machine learning analysis

A building system including one or more storage devices storing instructions thereon that, when executed by one or more processors, cause the one or more processors to receive an indication to execute an artificial intelligence (AI) agent of a digital twin, the digital twin including the AI agent and a graph data structure, the graph data structure including nodes representing entities of a building and edges between the nodes representing relationships between the entities of the building. The instructions cause the one or more processors to execute the AI agent based on data of the building to generate an inference that is a prediction of a future data value of a data point of the building for a future time and store at least one of the inference, or a link to the inference, in the graph data structure.

Building data platform with digital twin inferences

A building system including one or more memory devices configured to store instructions that, when executed on one or more processors, cause the one or more processors to collect building device data of a building device, the building device data comprising a plurality of data samples of a data point and generate a time correlated data stream for the data point, the time correlated data stream comprising values of the plurality of data samples of the data point. The instructions cause the one or more processors to generate a time correlated reliability data stream for the data point, the time correlated reliability data stream comprising a plurality of reliability values indicating reliability of the values of the plurality of data samples of the data point.

Building system with a time correlated reliability data stream

A method of operating a building management system is disclosed. The method includes determining, by a processing circuit, policy rankings for a plurality of control policies based on building operation data of a first previous time period, selecting, by the processing circuit, a set of control policies from among the plurality of control policies based on the policy rankings of the set of control policies satisfying a ranking threshold, generating, by the processing circuit, a plurality of prediction models for the set of control policies, selecting, by the processing circuit, a first prediction model of the plurality of prediction models based on building operation data of a second previous time period, and responsive to selecting the first prediction model, operating, by the processing circuit, the building management system using the first prediction model.

Building control system using reinforcement learning

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.

Cost savings from fault prediction and diagnosis

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.

Methods and systems for training building control using simulated and real experience data

A building system for training a prediction model with augmented training data. The building system comprising 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 obtain a first training data set comprising data values associated with a data point of the building system and with a plurality of time-steps and energy values associated with consumption of the building system at each of the plurality of time-steps; generate an augmented training data set comprising a second training data set, the second training data set comprising the energy values and the data values of the first training data set but with a data value replaced with a predetermined value at a time-step of the plurality of time-steps; and generate a prediction model by training the prediction model.

Building system with model training to handle selective forecast data

A building system including one or more storage devices storing instructions thereon that, when executed by one or more processors, cause the one or more processors to store a data structure of a digital twin of an entity of a building, the data structure storing information associated with at least one of the entity or entities of the building and determine that a trigger rule of the digital twin is triggered by comparing at least some of the information of the data structure against one or more conditions of the trigger rule. The instructions cause the one or more processors to determine an action of an action rule of the digital twin responsive to determining that the trigger rule is triggered by executing the action rule and cause one or more devices to operate based on the action determined by the action rule.

Building data platform with digital twin triggers and actions

A fault prediction system for building equipment includes one or more memory devices configured to store instructions that, when executed on one or more processors, cause the one or more processors to receive device data for a plurality of devices of the building equipment, the device data indicating performance of the plurality of devices; generate, based on the received device data, a plurality of prediction models comprising at least one of single device prediction models generated for each of the plurality of devices or cluster prediction models generated for device clusters of the plurality of devices; label each of the plurality of prediction models as an accurately predicting model or an inaccurately predicting model based on a performance of each of the plurality of prediction models; and predict a device fault with each of the plurality of prediction models labeled as an accurately predicting model.

Adaptive training and deployment of single device and clustered device fault detection models for connected equipment

A building system for detecting faults in an operation of building equipment. The building system comprising one or more memory devices configured to store instructions thereon that cause the one or more processors to perform a cumulative sum (CUSUM) analysis on actual building data and corresponding predicted building data to obtain cumulative sum values for a first plurality of times within a first time period; analyze cumulative sum values associated with a second plurality of times occurring before the first time to identify a second time of the second plurality of times at which a second cumulative sum value is at a local minimum; and determine that a first fault began at the second time.

Building system with early fault detection

A building system including one or more storage devices storing instructions thereon that, when executed by one or more processors, cause the one or more processors to receive an indication to execute an artificial intelligence (AI) agent, execute the AI agent based on the digital twin to generate an inference or a prediction, cause a graphical model of the building including graphical representations of the entities to include information based on the inference or the prediction, and cause a display device of a user device to display the graphical model.

Building data platform with digital twin based inferences and predictions for a graphical building model

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.

Building system with string mapping based on a sequence to sequence neural network

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.

Building risk analysis system with geographic risk scoring

A building system for generating input forecast data. The building system comprising 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 retrieve a current prediction set of measurements comprising current values associated with a plurality of time-steps; identify time-steps of the plurality of time-steps for which historical prediction values of a historical prediction set of measurements are outside of a tolerance of corresponding historical actual values of a historical actual set of measurements; replace each current value of the current prediction set of measurements that is associated with the identified time-steps with a predetermined value to generate an updated prediction set of measurements; and provide the updated prediction set of measurements to a prediction model.

Building system with selective use of data for probabilistic forecasting

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Old Westbury, NY, United States of America

Young Min Lee