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

• US 9,244,444 B2
• Filed: 03/07/2011
• Issued: 01/26/2016
• Est. Priority Date: 03/07/2011
• Status: Expired due to Fees

• Current Assignee: Callida Energy Incorporated
• Original Assignee: Callida Energy Incorporated
• Inventors: Carty, Raphael, Wenzinger, Jeffrey T.
• Primary Examiner(s): ORTIZ RODRIGUEZ, CARLOS R
• Application Number:
  US13/042,377
• Publication Number:
  US 20120232701A1
• Time in Patent Office: 1,786 Days
• Field of Search: 700 28- 33, 700275-277, 700/286, 700/291
• US Class Current: 1/1
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: G05B 2219/2642 Domotique, domestic, home control, automation, smart house
• CPC Class Codes: G05D 23/1917 using digital means
• CPC Class Codes: G05F 1/66 Regulating electric power
• CPC Class Codes: Y02P 90/84 Greenhouse gas [GHG] management systems
• CPC Class Codes: Y02P 90/845 Inventory and reporting systems for greenhouse gases [GHG]

Abstract

The method, according to one embodiment of the present invention, receives 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.

Claims

1. A method for optimizing building energy usage, the method comprising:

receiving a data set comprising a plurality of input values associated with one or more buildings, said input values containing at least thermal input values and electrical input values;

constructing a thermal load model based on at least the thermal input values;

constructing an electrical model based on at least the electrical input values;

constructing an overall energy model, the overall energy model being based on an analysis of interactive effects and trade-offs between the thermal model and the electrical models;

generating a plurality of demand models based on the overall energy model;

generating an on-site generation model for a power generation source located on-site with the one or more buildings, wherein generating the on-site generation model includes;

generating a load predictive model for the on-site power generation source based at least on historical on-site power generation data;

generating a consumption model for the on-site power generation source; and

discounting the load predictive model by the consumption model to generate the on-site generation model;

prioritizing a plurality of constraints, strategies and targets, wherein the constraints, strategies and targets comprise constraints, strategies and targets relating to energy management, occupant comfort, energy efficiency, demand response, and energy usage, at least one constraint, strategy or target related to use of the on-site power generation source in the one or more buildings;

optimizing one or more of the plurality of demand models using complex multivariate optimization techniques, wherein optimizing is based on usage data and the prioritized constraints, strategies, and targets; and

displaying recommendations based on one or more of the optimized demand models or generating realtime, complementary control instructions based on one or more of the optimized demand models.

2. The method of claim 1 further comprising cleaning the input values prior to constructing a thermal model and electrical model.

3. The method of claim 2 wherein cleaning the input values prior to constructing a thermal model and electrical model comprises detecting abnormal data and invalid inputs.

4. The method of claim 3 wherein cleaning the input values prior to constructing a thermal model and electrical model further comprises interpolating invalid data points and performing principle component analysis of the data set.

5. The method of claim 1, wherein generating the on-site generation model comprises determining whether the on-site power generation source comprises a variable or consistent on-site power generation source.

6. The method of claim 1 wherein the thermal model and electrical 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 the constraints, strategies and targets further include general rules, the general rules comprising proprietary rules based on research, rules based on comparisons to peer benchmarks and rules derived by comparing research to manufacturer-supplied data.

9. The method of claim 1 wherein optimizing the one or more demand models uses one of:

nonlinear programming techniques comprising at least one of genetic algorithms, simulated annealing, and artificial neural networks, and linear approximations to a nonlinear problem comprising at least one of Taylor series expansions and artificial neural networks.

10. The method of claim 1 wherein constructing thermal and electrical models comprises using one of memory-based time-series regression analysis using at least one of ARIMA, ANN, and SVM techniques.

11. The method of claim 1 further comprising simulating building systems based on the energy model, comparing simulation outcomes, and generating recommendations based on the comparison.

12. The method of claim 1, wherein generating the on-site generation model comprises generating an on-site generation model for a renewable energy source located on-site with the one or more buildings, and wherein generating the load predictive model for the on-site renewable energy source is based on historical on-site renewable energy data and weather data related to such renewable energy source.

13. A system for optimizing building energy usage, the system comprising:

a plurality of data sources stored on one or more data stores containing a plurality of input values associated with one or more buildings, said input values containing at least thermal input values and electrical input values;

a forecasting and estimation engine operative to;

construct a thermal and an electrical load model based on at least the thermal input values;

construct an electrical model based on at least the electrical input values;

construct an overall energy model, the overall energy model being based on an analysis of interactive effects and trade-offs between the thermal model and the electrical models;

generate a plurality of demand models based on an the overall energy model;

generate an on-site generation model for a power generation source located on-site with the one or more buildings, wherein the forecasting and estimation engine;

generates a load predictive model for the on-site power generation source based at least on historical on-site power generation data;

generates a consumption model for the on-site power generation source; and

discounts the load predictive model by the consumption model to generate the on-site generation model;

prioritize a plurality of constraints, strategies, and targets, wherein the constraints, strategies, and targets comprise constraints, strategies, and target relating to energy management, occupant comfort, energy efficiency, demand response, and energy usage at least one constraint, strategy or target relating to use of the on-site power generation source in the one or more buildings;

generate a plurality of demand models based on the constructed energy model;

an optimization engine operative to optimize one or more of the plurality of demand models using complex multivariate optimization techniques, wherein optimizing is based on usage data and the prioritized constraints, strategies, and targets;

a graphical user interface operating on a client device operative to display recommendations based on one or more of the optimized demand models or an interface to building control systems operative to transmit complementary control instructions based on one or more of the optimized demand models.

14. The system of claim 13 further comprising a data conditioner operative to clean the input values prior to constructing a thermal model and electrical model.

15. The system of claim 14 wherein the data conditioner is operative to detect abnormal data and invalid inputs.

16. The system of claim 15 wherein the data conditioner is further operative to interpolate invalid data points and perform principle component analysis of the data set.

17. The system of claim 13 wherein the forecasting and estimation engine is further operative to determine whether the on-site power generation source is a variable or consistent on-site power generation source.

18. The system of claim 13 wherein the forecasting and estimation engine constructs the thermal model and electrical models based on built and stored demand models for a plurality of subsystem categories.

19. The system of claim 18 wherein the plurality of subsystem categories includes heating, cooling, ventilation, lighting, water, plug load, and data center demand models.

20. The system of claim 13 wherein the constraints, strategies and targets further include general rules, the general rules comprising proprietary rules based on research, rules based on comparisons to peer benchmarks and rules derived by comparing research to manufacturer-supplied data.

21. A method for optimizing building energy usage, the method comprising:

receiving a plurality of input values associated with one or more buildings, said input values containing at least thermal input values and electrical input values;

constructing a thermal model based on at least the thermal input values;

constructing an electrical model based on at least the electrical input values;

constructing an overall energy model, the overall energy model being based on the thermal model and the electrical models;

generating a plurality of demand models;

generating an on-site generation model for a power generation source located on-site with the one or more buildings, wherein generating the on-site generation model includes;

generating a load predictive model for the on-site power generation source based at least on historical on-site power generation data;

generating a consumption model for the on-site power generation source; and

discounting the load predictive model by the consumption model to generate the on-site generation model;

prioritizing a plurality of constraints, strategies, and targets, wherein the constraints, strategies, and targets comprise constraints, strategies, and targets relating to energy management, occupant comfort, energy efficiency, demand response, and energy usage, at least one constraint, strategy or target relating to use of the on-site power generation source in the one or more buildings;

optimizing one or more of the plurality of demand models using complex multivariate optimization techniques, wherein optimizing is based on usage data and the prioritized constraints, strategies, and targets; and

displaying recommendations based on one or more of the optimized models or generating realtime, complementary control instructions based on one or more of the optimized models, the determination of displaying recommendations or generating realtime, complementary control instructions based on client preferences.