BUILDING SYSTEM WITH PROBABILISTIC FORECASTING USING A RECURRENT NEURAL NETWORK SEQUENCE TO SEQUENCE MODEL

    公开(公告)号:US20210056452A1

    公开(公告)日:2021-02-25

    申请号:US16549037

    申请日:2019-08-23

    Abstract: A building system for building data point prediction, the building system comprising 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 first building data for a building data point of a building and generate training data, the training data comprising a probability distribution sequence comprising a first probability distribution for the building data point. The instructions cause the one or more processors to train a prediction model based on the training data, receive second building data for the building data point, and predict, for one or more time-steps into the future, one or more second probability distributions with the second building data based on the prediction model, each of the one or more second probability distributions being a probability distribution for the building data point at one of the one or more time-steps.

    BUILDING SYSTEM WITH SELECTIVE USE OF DATA FOR PROBABILISTIC FORECASTING

    公开(公告)号:US20210056386A1

    公开(公告)日:2021-02-25

    申请号:US16549656

    申请日:2019-08-23

    Abstract: 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 EARLY FAULT DETECTION

    公开(公告)号:US20210190354A1

    公开(公告)日:2021-06-24

    申请号:US16725952

    申请日:2019-12-23

    Abstract: 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 MODEL TRAINING TO HANDLE SELECTIVE FORECAST DATA

    公开(公告)号:US20210056409A1

    公开(公告)日:2021-02-25

    申请号:US16549744

    申请日:2019-08-23

    Abstract: 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.

    ADAPTIVE TRAINING AND DEPLOYMENT OF SINGLE CHILLER AND CLUSTERED CHILLER FAULT DETECTION MODELS FOR CONNECTED CHILLERS

    公开(公告)号:US20190385070A1

    公开(公告)日:2019-12-19

    申请号:US16198456

    申请日:2018-11-21

    Abstract: A chiller fault prediction system for a building, including one or more memory devices and one or more processors. The one or more memory devices are configured to store instructions to be executed on the one or more processors. The one or more processors are configured to receive chiller data for a plurality of chillers, the chiller data indicating performance of the plurality of chillers. The one or more processors are configured to generate, based on the received chiller data, a plurality of single chiller prediction models and a plurality of cluster chiller prediction models, the plurality of single chiller prediction models generated for each the plurality of chillers and the plurality of cluster chiller prediction models generated for chiller clusters of the plurality of chillers. The one or more processors are configured to label each of the plurality of single chiller prediction models and the plurality of cluster chiller prediction models as an accurately predicting chiller model or an inaccurately predicting chiller model based on a performance of each of the plurality of single chiller prediction models and a performance of each of the plurality of cluster chiller prediction models. The one or more processors are configured to predict a chiller fault with each of the plurality of single chiller prediction models labeled as the accurately predicting chiller models. The one or more processors are configured to predict a chiller fault for each of a plurality of assigned chillers assigned to one of a plurality of clusters labeled as the accurately predicting chiller model.

    AUTOMATIC THRESHOLD SELECTION OF MACHING LEARNING/DEEP LEARNING MODEL FOR ANOMALY DETECTION OF CONNECTED CHILLERS

    公开(公告)号:US20190383510A1

    公开(公告)日:2019-12-19

    申请号:US16198377

    申请日:2018-11-21

    Abstract: A chiller threshold management system for a building, including one or more memory devices and one or more processors. The one or more memory devices are configured to store instructions to be executed on the one or more processors. The one or more processors are configured to determine whether chiller fault data exists in chiller data used to generate a plurality of chiller prediction models. The one or more processors are further configured to generate a first threshold evaluation value for each of the plurality of chiller prediction models using a first evaluation technique in response to a determination that chiller fault data exists in the chiller data, and generate a second threshold evaluation value for each of the chiller prediction models using a second evaluation technique in response to a determination that chiller fault data does not exist in the chiller data. The one or more processors are configured to select a first threshold for each of the plurality of chiller prediction models based on the first threshold evaluation values in response to the determination that chiller fault data exists in the chiller data, and select a second threshold for each of the plurality of chiller prediction models based on the second threshold evaluation values in response to the determination that chiller fault data does not exist in the chiller data.

    BUILDING MANAGEMENT AUTONOMOUS HVAC CONTROL USING REINFORCEMENT LEARNING WITH OCCUPANT FEEDBACK

    公开(公告)号:US20190353378A1

    公开(公告)日:2019-11-21

    申请号:US15980547

    申请日:2018-05-15

    Abstract: A building management system includes one or more processors, and one or more computer-readable storage media communicably coupled to the one or more processors and having instructions stored thereon that cause the one or more processors to: define a state of a zone or space within a building; control an HVAC system to adjust a temperature of the zone or space corresponding to a first action; receive utterance data from a voice assist device located in the zone or space; analyze the utterance data to identify a sentiment relating to the temperature of the zone or space; calculate a reward based on the state, the first action, and the sentiment; determine a second action to adjust the temperature of the zone or space based on the reward; and control the HVAC system to adjust the temperature of the zone or space corresponding to the second action.

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