Utility consumption disaggregation using low sample rate smart meters

• US 9,256,908 B2
• Filed: 08/17/2012
• Issued: 02/09/2016
• Est. Priority Date: 08/19/2011
• Status: Active Grant

• Current Assignee: Daedalus Blue LLC
• Original Assignee: International Business Machines Corporation
• Inventors: Dai, Jing, Chen, Feng, Naphade, Milind R., Sahu, Sambit
• Primary Examiner(s): BARNES-BULLOCK, CRYSTAL JOY
• Application Number:
  US13/588,634
• Publication Number:
  US 20130103215A1
• Time in Patent Office: 1,271 Days
• US Class Current: 1/1
• CPC Class Codes: G06Q 50/06 Energy or water supply

Abstract

Utility meter readings generated at low sampling rates are disaggregated to identify consumer usage activities. Time intervals between readings can include a plurality of consumer usage activities. By employing a model which recognizes associations among consumer usage activities, effective disaggregation is possible using only aggregated consumption data and interval start times. Consumers and utility managers can design and assess conservation programs based on the disaggregated consumption usage activities.

Claims

1. A method comprising:

obtaining an activity sequence model correlating utility consumption patterns with particular utility consumption activities;

transmitting aggregated utility consumption data including only aggregated volume and start time consumption data over time intervals of fifteen minutes or more from a utility meter operatively associated with a plurality of appliances or fixtures;

obtaining a sequence of the aggregated utility consumption data collected during the time intervals, and

disaggregating the sequence of aggregated utility consumption data into disaggregated consumption activities using the activity sequence model, the step of disaggregating further including detecting anomalous events within the time intervals and detecting parallel consumption activities in the time intervals from the anomalous events.

2. The method of claim 1, wherein the utility consumption patterns are based on fixture usage patterns, household dependent patterns and time dependent patterns.

3. The method of claim 2, wherein the activity sequence model comprises a hidden Markov model.

4. The method of claim 2, wherein the activity sequence model comprises a Gaussian mixture model.

5. The method of claim 2, wherein the time dependent patterns comprise utility consumption activities associated with each other.

6. The method of claim 1, wherein the step of obtaining the activity sequence model includes obtaining meter readings, extracting consumption events from the meter readings, identifying consumption activities from the consumption events, generating consumer activity sequences based on the identified consumption activities, identifying consumer activity sequences having statistically significant frequencies, and using the identified consumer activity sequences having statistically significant frequencies to obtain the activity sequence model.

7. The method of claim 1, wherein the utility meter is a water meter.

8. A system comprising:

an information receiving device configured to receive sequences of aggregated utility interval consumption data including only aggregated volume and start time consumption data over time intervals;

a storage device for electronically storing the received sequences of aggregated utility interval consumption data;

a storage device comprising an activity sequence model correlating utility consumption patterns with particular utility consumption activities, and

a processing device configured to disaggregate the sequences of aggregated utility interval consumption data into disaggregated utility consumption activities by applying the activity sequence model to the aggregated volume and start time consumption data in the received sequences of aggregated utility interval consumption data, the processing device being further configured to;

group intervals from the sequences with continuous consumption to generate events representing one activity or parallel activities,

use the activity sequence model to identify events that are anomalous,

for each anomalous event, estimate the number of parallel activities,

detect events that are abnormal,

for each abnormal event, estimate hidden parallel activities, and

estimate utility consumption of the hidden parallel activities.

9. The system of claim 8, wherein the activity sequence model correlates a first selected consumer consumption activity with a second selected consumer consumption activity.

10. The system of claim 9, further comprising a wireless gateway for collecting the aggregated utility interval consumption data, attaching timestamps thereto, and sending the aggregated utility interval consumption data with attached timestamps to the information receiving device.

11. The system of claim 10, further comprising one or more utility meters for transmitting aggregated utility interval consumption data at time intervals to the wireless gateway.

12. The system of claim 11, further comprising a computing cloud, the computing cloud comprising the information receiving device, the storage device for electronically storing the received sequences of aggregated utility interval consumption data, the storage device comprising the activity sequence model correlating utility consumption patterns with particular utility consumption activities, the processing device configured to disaggregate the sequences of aggregated utility interval consumption data into utility consumption activities, and a service layer for allowing users to browse the utility consumption activities.

13. The apparatus of claim 11, wherein the utility consumption patterns are based on fixture usage patterns, household dependent patterns and time dependent patterns.

14. The method of claim 8, wherein the activity sequence model comprises a hidden Markov model or a Gaussian mixture model.

15. The method of claim 8, wherein the time intervals are fifteen minutes or more.