Autonomous and semantic optimization approach for real-time performance management in a built environment

Abstract

Certain aspects of the present disclosure provide techniques for semantically contextualizing structured and unstructured data, extracting and making sense of domain knowledge, and performing semantic-driven optimization to provide generic, scalable, autonomous and real-time performance management within a built environment domain.

What is claimed is: 
     
         1 . A method for semantically-driven optimization, comprising:
 receiving, from a user, a request for an optimization event;   obtaining one or more domain and user requirements associated with the optimization event for a semantic domain model;   determining a prediction model and one or more prediction model parameters associated with the optimization event;   determining a simulation model and one or more simulation model parameters associated with the optimization event;   generating a generalized optimization problem based on the optimization event and the one or more domain and user requirements;   determining an optimization model based on the generalized optimization problem;   generating an optimization output from the optimization model; and   providing the optimization output to the user.   
     
     
         2 . The method of  claim 1 , further comprising modelling the one or more domain and user requirements associated with the optimization event based on a generalized characteristics representation. 
     
     
         3 . The method of  claim 1 , wherein determining the prediction model and the one or more prediction model parameters based on the one or more domain and user requirements, comprises:
 identifying the prediction model from a plurality of prediction models associated with the optimization event and the one or more domain and user requirements;   determining the one or more prediction model parameters based on the prediction model and the one or more domain and user requirements;   determining historical data associated with the one or more domain and user requirements; and   providing the prediction model and the prediction model parameters to the optimization model.   
     
     
         4 . The method of  claim 1 , wherein determining the prediction model and the one or more prediction model parameters based on the one or more domain and user requirements, comprises:
 determining no prediction model of a plurality of prediction models is associated with the optimization event and the one or more user and domain requirements,   constructing a new predictive model semantically based on historical data, comprising:   obtaining the historical data associated with the one or more domain and user requirements associated with the optimization event and the optimization event;   determining, via a variable selection service the one or more prediction model parameters based on the one or more domain and user requirements; and   training the new predictive model based on the one or more prediction model parameters and the historical data; and   providing the new prediction model and the one or more prediction model parameters to the optimization model.   
     
     
         5 . The method of  claim 1 , wherein determining the simulation model and the one or more simulation model parameters associated with the optimization event, comprises:
 identifying the simulation model from a plurality of simulation models associated with the optimization event and the one or more domain and user requirements;   determining the one or more simulation model parameters based on the simulation model and the one or more domain and user requirements;   determining simulation data associated with the one or more domain and user requirements; and   providing the simulation model and the simulation model parameters to the optimization model.   
     
     
         6 . The method of  claim 1 , wherein determining the simulation model and the one or more simulation model parameters based on the one or more domain and user requirements, comprises:
 determining no simulation model of a plurality of simulation models is associated with the optimization event and the one or more user and domain requirements, constructing a new simulation model semantically based on simulation data, comprising:
 obtaining the simulation data associated with the one or more domain and user requirements associated with the optimization event and the optimization event; 
 determining, via a variable selection service the one or more simulation model parameters based on the one or more domain and user requirements; and 
 training the new simulation model based on the one or more simulation model parameters and the simulation data; and 
   providing the new simulation model and the one or more simulation model parameters to the optimization model.   
     
     
         7 . The method of  claim 1 , wherein generating the optimized output based on the optimization model, comprises;
 executing the prediction model with one or more intermediate optimized solutions generated by the optimization model and prediction data;   executing the simulation model with the one or more intermediate optimized solutions generated by the optimization model and simulation data; and   obtaining the optimized output from the optimization model based on the executed prediction model and the executed simulation model.   
     
     
         8 . The method of  claim 1 , wherein the request for the optimization event comprises a structured natural language statement. 
     
     
         9 . A method for semantically-driven optimization, comprising:
 receiving, from a user, a request for an optimization event;   obtaining one or more domain and user requirements associated with the optimization event for a semantic domain model;   determining a prediction model and one or more prediction model parameters based on the one or more domain and user requirements;   generating a generalized optimization problem based on the optimization event and the one or more domain and user requirements;   determining an optimization model based on the generalized optimization problem;   generating an optimization output from the optimization model; and   providing the optimization output to the user.   
     
     
         10 . The method of  claim 9 , further comprising: modelling the one or more domain and user requirements based on a generalized characteristics representation. 
     
     
         11 . The method of  claim 9 , wherein determining the prediction model and the one or more prediction model parameters based on the one or more domain and user requirements, comprises:
 determining no prediction model of a plurality of prediction models is associated with the optimization event and the one or more user and domain requirements,   constructing a new predictive model semantically based on historical data, comprising:
 determining, via a variable selection service, the one or more prediction model parameters based on the one or more domain and user requirements; and 
 training the new predictive model based on the historical data and simulated data; and 
 providing the new prediction model to the optimization model. 
   
     
     
         12 . The method of  claim 11 , wherein determining, via the variable selection service, the one or more prediction model parameters based on the one or more domain and user requirements, comprises:
 determining one or more variables and constraints based on the one or more domain and user requirements;   determining input variables from the one or more variables and constraints based on a semantic relationship between the one or more variables and constraints and output variables for the prediction model; and   selecting, the one or more prediction model parameters from the input variables via the variable selection service.   
     
     
         13 . The method of  claim 9 , wherein generating the optimized output based on the optimization model, comprises;
 executing the prediction model with one or more intermediate optimized solutions generated by the optimization model and prediction data; and   obtaining the optimized output from the optimization model based on the executed prediction model.   
     
     
         14 . A processing system for a semantically-driven optimization, comprising: a memory comprising computer-executable instructions; and a processor configured to execute the computer-executable instructions and cause the processing system to:
 receive, from a user, a request for an optimization event;   obtain one or more domain and user requirements associated with the optimization event for a semantic domain model;   determine a prediction model and one or more prediction model parameters associated with the optimization event;   determine a simulation model and one or more simulation model parameters associated with the optimization event;   generate a generalized optimization problem based on the optimization event and the one or more domain and user requirements;   determine an optimization model based on the generalized optimization problem;   generate an optimization output from the optimization model; and   provide the optimization output to the user.   
     
     
         15 . The processing system of  claim 14 , wherein the processing system is further configured to model the one or more domain and user requirements associated with the optimization event based on a generalized characteristics representation. 
     
     
         16 . The processing system of  claim 14 , wherein in order to determine the prediction model and the one or more prediction model parameters based on the one or more domain and user requirements, the processing system is configured to:
 identify the prediction model from a plurality of prediction models associated with the optimization event and the one or more domain and user requirements;   determine the one or more prediction model parameters based on the prediction model and the one or more domain and user requirements;   determine historical data associated with the one or more domain and user requirements; and   provide the prediction model and the prediction model parameters to the optimization model.   
     
     
         17 . The processing system of  claim 14 , wherein in order to determine the prediction model and the one or more prediction model parameters based on the one or more domain and user requirements, the processing system is configured to:
 determine no prediction model of a plurality of prediction models is associated with the optimization event and the one or more user and domain requirements,   construct a new predictive model semantically based on historical data, comprising:   obtain the historical data associated with the one or more domain and user requirements associated with the optimization event and the optimization event;   determine, via a variable selection service the one or more prediction model parameters based on the one or more domain and user requirements; and   train the new predictive model based on the one or more prediction model parameters and the historical data; and   provide the new prediction model and the one or more prediction model parameters to the optimization model.   
     
     
         18 . The processing system of  claim 14 , wherein in order to determine simulation model and the one or more simulation model parameters associated with the optimization event, the processing system is configured to:
 identify the simulation model from a plurality of simulation models associated with the optimization event and the one or more domain and user requirements;   determine the one or more simulation model parameters based on the simulation model and the one or more domain and user requirements;   determine simulation data associated with the one or more domain and user requirements; and   provide the simulation model and the simulation model parameters to the optimization model.   
     
     
         19 . The processing system of  claim 14 , wherein in order to determine the simulation model and the one or more simulation model parameters based on the one or more domain and user requirements, the processing system is configured to:
 determine no simulation model of a plurality of simulation models is associated with the optimization event and the one or more user and domain requirements,   construct a new simulation model semantically based on simulation data, comprising:
 obtain the simulation data associated with the one or more domain and user requirements associated with the optimization event and the optimization event; 
 determine, via a variable selection service the one or more simulation model parameters based on the one or more domain and user requirements; and 
 train the new simulation model based on the one or more simulation model parameters and the simulation data; and 
   provide the new simulation model and the one or more simulation model parameters to the optimization model.   
     
     
         20 . The processing system of  claim 15 , wherein in order to generate the optimized output based on the optimization model, the processing system is configured to;
 execute the prediction model with one or more intermediate optimized solutions generated by the optimization model and prediction data;   execute the simulation model with the one or more intermediate optimized solutions generated by the optimization model and simulation data; and   obtain the optimized output from the optimization model based on the executed prediction model and the executed simulation model.