Systems and methods for optimizing energy and resource management for building systems

Abstract

The present invention provides a method and system for optimizing building energy usage. The method comprising receiving a plurality of input values associated with a building or plurality of buildings. The method then constructs a thermal and an electrical load model based on the inputs and constructs an overall energy model, the overall energy model being based on the thermal and electrical load models. The method next generates a plurality of demand models and optimizes the demand models using complex multivariate optimization techniques, wherein optimizing is based on usage data and energy rules. Finally, the method displays recommendations based on the optimized model or generating real-time, complementary control instructions based on the optimized model, the determination based on client preferences.

We claim: 
     
         1 . A method for optimizing building energy usage, the method comprising
 receiving a plurality of input values associated with a building or plurality of buildings;   constructing a thermal and an electrical load model based on the inputs;   constructing an overall energy model, the overall energy model being based on the thermal and electrical load models;   generating a plurality of demand models based on the constructed energy model;   optimizing the demand models using complex multivariate optimization techniques, wherein optimizing is based on usage data and energy rules; and   displaying recommendations based on the optimized model or generating real-time, complementary control instructions based on the optimized model.   
     
     
         2 . The method of  claim 1  further comprising cleaning the input values prior to constructing a thermal and electrical load model. 
     
     
         3 . The method of  claim 2  wherein cleaning the input values prior to constructing a thermal and electrical load model comprises detecting abnormal data and invalid inputs. 
     
     
         4 . The method of  claim 3  wherein cleaning the input values prior to constructing a thermal and electrical load model further comprises interpolating invalid data points and performing principle component analysis of the data set. 
     
     
         5 . The method of  claim 1  further comprising generating and optimizing an on-site generation model for variable and consistent on-site generation sources. 
     
     
         6 . The method of  claim 1  wherein the thermal and electrical load models are generated based on built and stored demand models for a plurality of subsystem categories. 
     
     
         7 . The method of  claim 6  wherein the plurality of subsystem categories includes one or more of heating/cooling, ventilation, lighting, water, plug load, and data center demand models. 
     
     
         8 . The method of  claim 1  wherein energy rules comprise client-defined rules/constraints, strategies and general rules and wherein the method further optimizes the models based on client-defined rules/constraints, strategies and strategies include rules for energy management specified by the building manager or owner. 
     
     
         9 . The method of  claim 8  wherein general rules include rules for optimizing building energy management include proprietary rules based on research, rules based on comparisons to peer benchmarks and rules derived by comparing research to manufacturer-supplied data. 
     
     
         10 . A system for optimizing building energy usage, the system comprising
 a plurality of data sources containing a plurality of input values associated with a building or plurality of buildings;   a forecasting and optimization engine operative to:
 construct a thermal and an electrical load model based on the inputs; 
 construct an overall energy model, the overall energy model being based on the thermal and electrical load models; and 
 generate a plurality of demand models based on the constructed energy model; 
   an optimization engine operative to optimize the demand models using complex multivariate optimization techniques, wherein optimizing is based on usage data and energy rules;   a graphical user interface operating on a client device operative to display recommendations based on the optimized model or generating real and an interface to building control systems operative to transmit complementary control instructions based on the optimized model, the determination based on client preferences.   
     
     
         11 . The system of  claim 10  further comprising a data conditioner operative to clean the input values prior to constructing a thermal and electrical load model. 
     
     
         12 . The system of  claim 11  wherein the data conditioner is operative to detect abnormal data and invalid inputs. 
     
     
         13 . The system of  claim 12  wherein the data conditioner is further operative to interpolate invalid data points and performing principle component analysis of the data set. 
     
     
         14 . The system of  claim 10  wherein the system is further operative to generate and optimize an on-site generation model for variable and consistent on-site generation sources. 
     
     
         15 . The system of  claim 10  wherein the forecasting and optimization engine generates the thermal and electrical load models based on built and stored demand models for a plurality of subsystem categories. 
     
     
         16 . The system of  claim 15  wherein the plurality of subsystem categories includes heating/cooling, ventilation, lighting, water, plug load, and data center demand models. 
     
     
         17 . The system of  claim 10  wherein energy rules comprise client defined rules and strategies and general rules and wherein the optimizer further optimizes the models based on client defined rules and strategies include rules for energy management specified by the building manager or owner. 
     
     
         18 . The system of  claim 17  wherein general rules include rules for optimizing building energy management include proprietary rules based on research, rules based on comparisons to peer benchmarks and rules derived by comparing research to manufacturer-supplied data. 
     
     
         19 . A method for optimizing building energy usage, the method comprising
 receiving a plurality of input values associated with a building or plurality of buildings;   constructing a thermal and an electrical load model based on the inputs;   constructing an overall energy model, the overall energy model being based on the thermal and electrical load models;   generating a plurality of demand models;   optimizing the demand models using complex multivariate optimization techniques, wherein optimizing is based on usage data and energy rules; and   displaying recommendations based on the optimized model or generating real-time, complementary control instructions based on the optimized model, the determination based on client preferences.   
     
     
         20 . A method for optimizing building energy usage, the method comprising
 receiving a plurality of input values associated with a building or plurality of buildings;   constructing a thermal model based on the inputs;   constructing an overall energy model, the overall energy model being based on the thermal model;   generating a plurality of demand models, the demand models representing a combination of subsystems wherein the combination of subsystems is selected from one of: heating and cooling and lighting; heating and cooling, lighting and water; heating and cooling, water; heating and cooling, ventilation, water; heating and cooling, ventilation, lighting and water; heating and cooling, ventilation, lighting, plug load; heating and cooling, ventilation, lighting, plug load, water; water; heating and cooling, ventilation, dedicated data center EMS; or all electrical demand across all building subsystems;   optimizing the demand models using complex multivariate optimization techniques, wherein optimizing is based on usage data and energy rules; and   displaying recommendations based on the optimized model or generating real-time, complementary control instructions based on the optimized model, the determination based on client preferences.