Systems, methods and software for adjusting read data from utility meters

• US 8,620,885 B1
• Filed: 02/16/2010
• Issued: 12/31/2013
• Est. Priority Date: 02/13/2009
• Status: Active Grant

• Current Assignee: Landis+Gyr Analytics, LLC (Landis+Gyr Group AG)
• Original Assignee: Ecologic Analytics, LLC (Landis+Gyr Group AG)
• Inventors: Gupta, Anil Kumar, Hubbard, David James, Winzig, Jerome Frank
• Primary Examiner(s): To, Baoquoc N
• Assistant Examiner(s): LODHI, ANDALIB FT
• Application Number:
  US12/706,041
• Time in Patent Office: 1,414 Days
• Field of Search: 707/697, 707/758, 707/769, 340/870.02, 340/870.03
• US Class Current: 707/697
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 50/06 Energy or water supply

Abstract

Many public utilities companies employ meter data management systems (MDMSs) to manage the millions and sometimes billions of daily and interval meter read values that they collect during a single billing period. One problem with some MDMSs estimate missing daily reads based on average daily usage and these estimates do not match daily usage values determined from interval read data, causing sum check exceptions. Such exceptions are undesirable because they can lead to billing errors and lost consumer confidence in the utility, among other things. To address this, the present inventors devised an exemplary system that uses good and estimated interval read data to more accurately estimate the missing daily reads and thereby reduce occurrences of sum check exceptions. The exemplary system also estimates missing time of use and demand data based on interval read data.

Claims

1. A meter data management system comprising:

a processor operable for receiving and storing daily and interval meter read data, with the received daily or interval meter read data having missing daily or interval read data;

a processor operable for processing the received daily and interval meter read data; and

a processor operable for reducing occurrences of sum check exceptions in the processed daily meter read data;

wherein the sum check comprises a match between usage values calculated using the daily meter read data and usage values calculated using the interval meter read data; and

wherein the processor operable for reducing occurrences of sum check exceptions includes a processor operable for estimating missing interval read data by reallocating unaccounted-for usage that would result in a sum check exception across the missing interval read data.

2. The system of claim 1, wherein the unaccounted-for usage is reallocated equally across the missing intervals.

3. The system of claim 1, wherein the unaccounted-for usage is reallocated proportionately across the missing intervals, based on load profile data.

4. The system of claim 1:

wherein the processor operable for processing the received daily and interval meter read data includes a processor for estimating missing daily meter values based on average daily usage; and

wherein the processor operable for reducing sum check exceptions includes a processor operable for re-estimating at least one estimated missing daily meter value, based on interval meter read data.

5. The system of claim 1, further including a processor operable for maintaining a version history of the daily and interval meter read data, wherein one or more daily and interval meter read data values are labeled to indicate that it was estimated based on interval meter read information.

6. The system of claim 5, further including a graphical user interface for enabling user access to the version history.

7. The system of claim 1, further including a processor operable for determining missing time-of-use (TOU) meter read data based on estimates of missing interval data.

8. The system of claim 1, further including a processor operable for determining missing demand meter read data based on estimates of missing interval data.

9. A process comprising:

using a computer processor for receiving and storing daily and interval meter read data, with the received daily or interval meter read data having missing daily or interval read data;

using the computer processor for processing the received daily and interval meter read data; and

using the computer processor for reducing occurrences of sum check exceptions in the processed daily meter read data;

wherein the sum check comprises a match between usage values calculated using the daily meter read data and usage values calculated using the interval meter read data; and

wherein reducing the occurrences of sum check exceptions includes using a processor for estimating missing interval read data by reallocating unaccounted-for usage that would result in a sum check exception across the missing interval read data.

10. The process of claim 9, wherein the unaccounted-for usage is reallocated equally across the missing intervals.

11. The process of claim 9, wherein the unaccounted-for usage is reallocated proportionately across the missing intervals, based on load profile data.

12. The process of claim 9:

wherein the processing the received daily and interval meter read data includes using a processor for estimating missing daily meter values based on average daily usage; and

wherein the reducing sum check exceptions includes using a processor for re-estimating at least one estimated missing daily meter value, based on interval meter read data.

13. The process of claim 9, further including using a computer processor for maintaining a version history of the daily and interval meter read data, wherein one or more daily and interval meter read data values are labeled to indicate that it was estimated based on interval meter read information.

14. The process of claim 13, further including using a graphical user interface for enabling user access to the version history.

15. The process of claim 9, further including using a processor for determining missing time-of-use (TOU) meter read data based on estimates of missing interval data.

16. The process of claim 9, further including using a processor for determining missing demand meter read data based on estimates of missing interval data.

17. A non-transitory computer readable medium comprising instructions that when executed by a processor executes a process comprising:

receiving and storing daily and interval meter read data, with the received daily or interval meter read data having missing daily or interval read data;

processing the received daily and interval meter read data; and

reducing occurrences of sum check exceptions in the processed daily meter read data;

wherein the sum check comprises a match between usage values calculated using the daily meter read data and usage values calculated using the interval meter read data; and

wherein reducing the occurrences of sum check exceptions includes instructions for estimating missing interval read data by reallocating unaccounted-for usage that would result in a sum check exception across the missing interval read data.

18. The non-transitory computer readable medium of claim 17, wherein the unaccounted-for usage is reallocated equally across the missing intervals.

19. The non-transitory computer readable medium of claim 17, wherein the unaccounted-for usage is reallocated proportionately across the missing intervals, based on load profile data.

20. The non-transitory computer readable medium of claim 17,

wherein the processing the received daily and interval meter read data includes instructions for estimating missing daily meter values based on average daily usage;

and

wherein the reducing sum check exceptions includes instructions for re-estimating at least one estimated missing daily meter value, based on interval meter read data.

21. The non-transitory computer readable medium of claim 17, further including instructions for maintaining a version history of the daily and interval meter read data, wherein one or more daily and interval meter read data values are labeled to indicate that it was estimated based on interval meter read information.

22. The non-transitory computer readable medium of claim 21, further including instructions for using a graphical user interface for enabling user access to the version history.

23. The non-transitory computer readable medium of claim 17, further including instructions for determining missing time-of-use (TOU) meter read data based on estimates of missing interval data.

24. The non-transitory computer readable medium of claim 17, further including instructions for determining missing demand meter read data based on estimates of missing interval data.