Abstract: A method includes obtaining a fault prediction model for building equipment, predicting, with the fault prediction model, both (i) whether a fault will occur during a first prediction bin and (ii) whether a fault will occur during a second prediction bin, performing a first mitigating action for the building equipment if the fault is predicted to occur during the first prediction bin, and performing a second mitigating action for the building equipment if the fault is predicted to occur during the second prediction bin.

Abstract: 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.

Abstract: Systems and methods for executing an IAQ analysis of a building. One system includes a controller including memory and one or more processors configured to obtain IAQ data from one or more sensors within the building, wherein the IAQ data is associated with at least one of a plurality of environment species, obtain BAS data, identify one or more unknown parameters from the IAQ data and BAS data of two or more of the plurality of environment species, estimate the one or more unknown parameters based on inputting the IAQ data and the BAS data into an optimization model, and wherein the optimization model analyzes predicted concentrations of the plurality of environment species subject to the two or more of the plurality of environment species evolving according to a single-species concentration model, and provide the estimated one or more unknown parameters to one or more predictive models.

Abstract: 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.

Abstract: Systems and methods for executing an IAQ analysis of a building. One system includes a controller including memory and one or more processors configured to obtain IAQ data from one or more sensors within the building, wherein the IAQ data is associated with at least one of a plurality of environment species, obtain BAS data, identify one or more unknown parameters from the IAQ data and BAS data of two or more of the plurality of environment species, estimate the one or more unknown parameters based on inputting the IAQ data and the BAS data into an optimization model, and wherein the optimization model analyzes predicted concentrations of the plurality of environment species subject to the two or more of the plurality of environment species evolving according to a single-species concentration model, and provide the estimated one or more unknown parameters to one or more predictive models.

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.

Abstract: A method includes providing a net consumption trajectory comprising net consumption targets for one or more subperiods of a time period. Each net consumption target indicates a target difference from a beginning of a time period to an end of the subperiod between total consumption and total production or offset. The method also includes generating, for a subperiod of the plurality of subperiods, a set of curtailment actions predicted to achieve the net consumption target for the subperiod and implementing the set of curtailment actions.

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.

Abstract: A building system of a building operates to receive an indication to generate a digital twin and identify one or more lower level digital twins that depend from the digital twin based on a hierarchy of digital twins, wherein the digital twin includes one or more first operational capabilities and the one or more lower level digital twins include one or more second operational capabilities. The building system operates to cause the digital twin to inherit the one or more second operational capabilities from the one or more lower level digital twins and execute the digital twin causing the one or more first operational capabilities and the one or more second operational capabilities to be performed.

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.

Abstract: A method for balancing space utilization in a building. The method includes identifying a first space and a second space of a building; determining, for a plurality of individuals assigned to the first space, (i) first differences between first preferences of the plurality of individuals for a first characteristic of the second space and a first value of the first characteristic, and (ii) second differences between second preferences of the plurality of individuals for a second characteristic of the second space and a second value of the second characteristic; determining a subset of individuals to move from the first space to the second space based on a combination of the first differences and the second differences; and implementing a reassignment of the subset of individuals to the second space or generating, by the one or more processors, a recommendation to reassign the subset of individuals to the second space.

Abstract: The present disclosure relates to a building management system (BMS) with indoor navigation features. The BMS comprises a processing circuit and a database. The database comprises a building model, an entity model and spatial anchors. The processing circuit receives camera-derived data from an electronic device associated with a user. Further, a current location of the user within a building is determined by comparing the camera-derived data with the building model. Additionally, a target location may be determined using the entity model. Further, one or more spatial anchors between the target location and the current location are identified to determine a shortest navigable path between the current location and the target location. Subsequently, the spatial anchors are interconnected and provided to the electronic device, wherein the user associated with the electronic device navigates towards the target location by following the interconnected spatial anchors.

Abstract: Systems and methods of managing a building are disclosed. In some embodiments, a method includes receiving, by a processing circuit, an indication to execute a digital twin, the digital twin including one or more fault detection or diagnostics functions and a virtual representation of a piece of equipment, the virtual representation including one or more entities of a building and relationships between the entities of the building, executing the digital twin based on the virtual representation of the piece of building equipment to generate an indication of a fault or a diagnosis of the fault for the one or more pieces of building equipment, and storing an indication of the fault or a diagnosis of the fault, or a link to the fault or the diagnosis of the fault, in the virtual representation of the piece of equipment.

Abstract: Building management systems and methods for autonomously tuning rule thresholds are disclosed. In one aspect, the method includes a providing a rule including a threshold, the rule used to determine whether building equipment has a fault. The method further includes receiving a state of the building equipment, assessing using a machine learning model whether the determination of whether the building equipment has a fault is a false positive or a false negative based on the state and the threshold, determining a new threshold based on the assessment of the machine learning model, and replacing the threshold with the new threshold to make subsequent determinations of whether the building equipment or other building equipment has a fault.

Abstract: 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.

Abstract: 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.

Abstract: A method for generating a reliability model, comprising receiving, by a processing circuit, historical operating data associated with one or more chillers or chiller components, the historical operating data including two or more event dates associated with the one or more chillers, calculating, by the processing circuit, a runtime of a chiller of the one or more chillers based on the two or more event dates, calibrating, by the processing circuit, the runtime by determining an idle time associated with the chiller corresponding to a location of the chiller and performing an operation using the runtime and the idle time to generate a calibrated runtime, and training, by the processing circuit, a chiller reliability model using the calibrated runtime to produce a trained model.

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.

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.

Abstract: Systems and methods for training a reinforcement learning (RL) model for HVAC control are disclosed herein. A calibrated simulation model is used to train a surrogate model of the HVAC system operating within a building. The surrogate model is used to generate simulated experience data for the HVAC system. The simulated experience data can be used to train a reinforcement learning (RL) model of the HVAC system. The RL model is used to control the HVAC system based on the current state of the system and the best predicted action to perform in the current state. The HVAC system generates real experience data based on the actual operation of the HVAC system within the building. The real experience data is used to retrain the surrogate model, and additional simulated experience data is generated using the surrogate model. The RL model can be retrained using the additional simulated experience data.