Systems and methods for reducing energy usage

• US 9,190,844 B2
• Filed: 11/04/2012
• Issued: 11/17/2015
• Est. Priority Date: 11/04/2012
• Status: Active Grant

• Current Assignee: Building Lens Incorporated
• Original Assignee: Bao Tran
• Inventors: Tran, Bao
• Primary Examiner(s): Lau, Tung S
• Application Number:
  US13/668,302
• Publication Number:
  US 20140129160A1
• Time in Patent Office: 1,108 Days
• Field of Search: 702/61
• US Class Current: 1/1
• CPC Class Codes: G01R 21/007 Adapted for special tariff measuring G01R21/01, G01R21/02, G01R21/08, G01R21/10, G01R21/1278 and G01R21/1333 take precedence
• CPC Class Codes: G01W 1/00 Meteorology
• CPC Class Codes: G06Q 50/163 Real estate management
• CPC Class Codes: G16H 40/67 for remote operation
• CPC Class Codes: H02J 2310/52 The controlling of the operation of the load not being the total disconnection of the load, i.e. entering a degraded mode or in current limitation
• CPC Class Codes: H02J 2310/70 Load identification
• CPC Class Codes: H02J 3/004 Generation forecast, e.g. methods or systems for forecasting future energy generation
• CPC Class Codes: H02J 3/14 by switching loads on to, or off from, network, e.g. progressively balanced loading
• CPC Class Codes: Y02B 70/3225 Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y04S 20/222 Demand response systems, e.g. load shedding, peak shaving

Abstract

A system for detecting individual appliance energy loads from a building composite load profile includes an electric meter to capture building composite load profile; a detector coupled to the meter to detect transitions in the load profile to determine an appliance state machine for each appliance; a clusterizer to detect clusters of patterns in the load profile; and an analyzer coupled to the detector to receive the transitions and appliance state machines from the detector, the analyzer matching each transition to a predetermined appliance state machine to disaggregate the building composite load profile into individual appliance energy loads.

Claims

1. A system for detecting individual appliance energy loads from a building composite load profile, comprising:

an electric meter to capture building composite load profile;

a detector coupled to the electric meter to detect transitions in the load profile to determine an appliance state machine for each appliance;

a clusterizer to detect clusters of patterns in the load profile; and

an analyzer coupled to the detector to receive the transitions and appliance state machines from the detector, the analyzer matching each transition to a predetermined appliance state machine to disaggregate the building composite load profile into individual appliance energy loads, wherein the analyzer sends messages on energy saving to a consumer based on predetermined weather factors and a prior success history of the message with a predetermined group members and captures feedback on the sent messages to see if a change has occurred since a communication, wherein the feedback includes a user action taken with respect to the message; and

presenting to the user cost savings based on the user action.

2. The system of claim 1, wherein the analyzer receives non-electrical information to supplement assigning transitions to specific state machines.

3. The system of claim 1, wherein the electric meter comprises a utility smart meter and the analyzer comprises a processor in communication with the utility meter.

4. The system of claim 2, wherein the non-electrical information includes third party data and user input.

5. The system of claim 2, wherein the non-electrical information includes household square footage;

household occupancy, temperature data, heating degree days, cooling degree days, neighborhood data, and municipality data.

6. A method for detecting individual appliance energy loads from a building composite load profile, comprising:

determining transitions within the building composite load profile;

clusterizing patterns in the load profile and determining specific appliance state machines for each appliance in the building based on the clusterized patterns; and

disaggregating the building composite load profile into individual appliance energy loads by assigning the determined transitions to the determined specific appliance state machines;

sending messages on energy saving to a consumer based on predetermined weather factors and a prior success history of the message with a predetermined group members;

capturing feedback on the sent messages to see if a change has occurred since a communication, wherein the feedback includes a user action taken with respect to the message; and

presenting to the user cost savings based on the user action.

7. The method of claim 6, wherein the transitions are determined by clustering data into one or more groups.

8. The method of claim 6, wherein the transitions are assigned based in part on non-electrical information used to assist in properly assigning transitions to the appropriate state machines.

9. The method of claim 6, wherein the disaggregating the building composite load profile comprises assigning the determined transitions based upon one or more rules selected from the group consisting of:

(i) assigning transitions to only one state machine; and

(ii) assigning transitions active state machines over non-active state machines.

10. The method of claim 6, comprising:

determining an appliance load signature from a user'"'"'s existing appliance from a building composite load signature;

determining a substitute appliance for the existing appliance; and

presenting to the user cost savings between the user'"'"'s existing appliance and the substitute appliance.

11. The method of claim 6, further comprising generating an incentive for the user based at least in part on the difference in energy usage and difference in costs between the user'"'"'s existing appliance and the substitute appliance.

12. The method of claim 6, further comprising identifying heat usage and air conditioning usage from a thermostat.

13. The method of claim 6, comprising suggesting actions to take to reduce energy consumption or recommending changes to air conditioning or heating appliances based on the disaggregated energy consumption to save energy.

14. The method of claim 6, comprising performing adaptive daily life activity tracking of a user'"'"'s new activities or habits, making an assessment in accordance with a protocol and permitting a physician to prescribe a treatment, permitting the physician to refer the patient to a third party for treatment via an electronic link in a relevant segment of the protocol, communicating a prescription over the network to a pharmacy or communicating the prescription over the network to the pharmacy.

15. The method of claim 6, comprising mailing users to engage in energy saving and motivating action from users.

16. The method of claim 6, comprising deriving from utility meter readings energy usage for air conditioning, air heating, refrigerating, lighting, or water heating.

17. The method of claim 6, comprising predicting energy usage and performing demand response in accordance with the predicted energy usage.

18. The method of claim 6, comprising segmenting consumers into different subsets based upon a plurality of demographic variables;

segmenting consumers into different subsets based upon energy usage normalized to relevant peer groups;

prioritizing messages based upon a historical rate of uptake multiplied by expected energy savings value;

offering resource efficient products discounted by private industry through rebates, coupons, and discounts for government subsidies of efficient products;

integrating with an Internet website for online and offline viewing of reports;

integrating with resource or utility databases;

combining information about a distribution network topology with data from smart-grid devices including as transformer meters, low-voltage and medium voltage sensors and metered data from smart meters and grid sensors for situational awareness, loading, losses, phase imbalance and utilization of the distribution network assets, optimizing utilization of existing assets to defer capital expenditure for new assets.

19. The method of claim 6, comprising performing adaptive daily life activity tracking to adjust to a user'"'"'s new activities or habits, flagging sudden changes for follow up and prompting a call center agent to follow up with the user to make sure he or she does not need help, where the tracking uses one or more models including parametric statistical models, non-parametric statistical models, clustering models, nearest neighbor models, regression methods, artificial neural networks.

20. The method of claim 8, wherein the non-electrical information includes third party data and user input.