Weather data quality control and ranking method

• US 7,228,234 B2
• Filed: 04/28/2005
• Issued: 06/05/2007
• Est. Priority Date: 01/26/2005
• Status: Active Grant

• Current Assignee: Siemens Industry Incorporated (Siemens AG)
• Original Assignee: Siemens Building Technologies Incorporated (Siemens AG)
• Inventors: McNally, James T., Allen, Robert C., Price, Jeffrey U., Vaiser, Boris
• Primary Examiner(s): MCELHENY JR, DONALD E
• Application Number:
  US11/119,459
• Publication Number:
  US 20060184326A1
• Time in Patent Office: 768 Days
• Field of Search: 702/3, 702/4, 703/2, 703/6
• US Class Current: 702/3
• CPC Class Codes: G01W 1/00 Meteorology
• CPC Class Codes: G01W 1/10 Devices for predicting weather conditions computers per se G06; display devices G09

Abstract

A method and system for performing quality control checks upon weather data are provided. The weather data includes information pertaining to a plurality of weather parameters. The quality control checks may identify inaccurate weather parameters or be used to rank weather data sets. The quality control checks include hourly, daily, and forecast quality control checks performed upon hourly, daily, and forecast weather parameter data. The method and system may exclude the inaccurate weather parameters identified from a reasonable weather database. The method and system may determine reasonable or approximate weather parameters that approximate the actual weather parameters. The system may rank a plurality of weather data sets based upon the accuracy determination. The reasonable weather data or the weather data ranked as the most accurate may be used to calculate energy or cost savings for building energy conservation measures.

Claims

1. A method of quality control for weather data, the method comprising:

receiving weather data from at least one weather data provider, the weather data including information pertaining to a plurality of weather parameters;

automatically identifying an inaccurate weather parameters from among the plurality of weather parameters using a process; and

estimating and presents to a user an energy usage, an energy cost, or a cost savings associated with a building or a piece of equipment using the weather data exclusive of the inaccurate weather parameter.

2. The method of claim 1, comprising performing hourly data quality control checks on hourly weather parameter data.

3. The method of claim 2, wherein the hourly data quality control checks include identifying temperature only patterns in the hourly weather parameter data.

4. The method of claim 2, wherein the hourly data quality control checks include identifying erroneous hourly weather parameter data based upon time entry information.

5. The method of claim 2, wherein the hourly data quality control checks include comparison of a hourly dew point temperature with a corresponding hourly dry bulb temperature.

6. The method of claim 2, wherein the hourly data quality control checks include comparison of a hourly temperature with an average temperature.

7. The method of claim 2, wherein the hourly data quality control checks include comparison of a temperature change per unit time with a temperature slope limit.

8. The method of claim 2, wherein the hourly data quality control checks include comparison of a provisional daily high temperature and a provisional daily low temperature with historical temperature data.

9. The method of claim 1, comprising performing quality control checks on daily weather parameter data.

10. The method of claim 9, wherein the quality control checks include comparison of a daily high temperature with a daily low temperature.

11. The method of claim 9, wherein the quality control checks include comparison of a daily high temperature and a daily low temperature with a temperature determined to be inaccurate based upon a temperature change per unit time calculation.

12. The method of claim 9, wherein the quality control checks include comparison of a provisional high temperature with the boundaries of a high temperature range and comparison of a provisional low temperature with the boundaries of a low temperature range.

13. The method of claim 9, wherein the quality control checks include comparison of a dew point temperature with a dry bulb temperature.

14. The method of claim 1, comprising performing quality control checks on forecast weather parameter data.

15. The method of claim 14, wherein the quality control checks include comparison of a provisional high temperature with the boundaries of a high temperature range and comparison of a provisional low temperature with the boundaries of a low temperature range.

16. The method of claim 15, wherein the high and low temperature ranges correspond to an average high and low temperature range, a record high and low temperature range, a thirty-year average monthly high and low temperature range, or an all-time high and low temperature range, respectively.

17. The method of claim 1, comprising:

receiving weather data from a plurality of weather data providers;

ranking the accuracy of the weather data from each weather data provider; and

calculating energy or cost savings for a building energy conservation measure based upon the most accurate weather data.

18. A method of quality control for weather data, the method comprising:

receiving a plurality of weather data sets, each weather data set including information pertaining to a plurality of weather values;

identifying inaccurate weather values;

ranking the weather data sets based upon the accuracy of the respective weather; and

estimating and outputting to a user an energy usage, an energy cost, or a cost savings for associated with a building or a piece of equipment using a first weather data set ranked to be more accurate than a second weather data set.

19. The method of claim 18, comprising identifying inaccurate hourly weather parameter data.

20. The method of claim 19, comprising identifying “

temperature only”

patterns interspersed amongst “

full data”

patterns.

21. The method of claim 19, comprising comparing a hourly dew point temperature with a corresponding hourly dry bulb temperature.

22. The method of claim 19, comprising comparing a temperature change per unit time with a predetermined temperature slope.

23. The method of claim 18, comprising identifying inaccurate daily weather parameters.

24. The method of claim 23, comprising comparing a daily high temperature and a daily low temperature with a temperature determined to be inaccurate based upon a temperature change per unit time calculation.

25. The method of claim 23, comprising comparing a dew point temperature with a dry bulb temperature.

26. The method of claim 18, comprising identifying inaccurate forecast weather parameters.

27. The method of claim 26, comprising comparing a provisional high temperature with the boundaries of a high temperature range and a provisional low temperature with the boundaries of a low temperature range.

28. The method of claim 18, wherein the weather data from the most accurate weather data set is used to forecast energy and cost savings for a building energy conservation measure.

29. A system for identifying inaccurate weather parameters, the system comprising:

a processor operable to receive weather data and perform quality control checks on the weather data, the weather data including information pertaining to a plurality of weather parameters, wherein the quality control checks identify an erroneous weather parameters and the processor calculates and presents to a user an energy usage, an energy cost, or a cost savings associated with a building or a piece of equipment using the weather data exclusive of the erroneous weather parameter.

30. The system of claim 29, wherein the processor determines approximate weather parameter data that approximates actual weather parameters to replace the erroneous weather parameter data.

31. The system of claim 30, wherein the processor calculates energy and cost savings for a building energy conservation measure using the approximate weather parameter data.

32. The system of claim 31, wherein the processor receives weather data from a plurality of sources and ranks the sources based upon the accuracy of the weather data provided by each source.

33. The system of claim 29, wherein the quality control checks include hourly, daily, and forecast quality checks performed upon hourly, daily, and forecast weather parameters, respectively.

34. A computer-readable medium having instructions executable on a computer stored thereon, the instructions comprising:

receiving a plurality of weather data sets, each weather data set including information pertaining to a plurality of weather values;

ranking the weather data sets based upon the accuracy of the respective weather values; and

calculating and outputting to a user an energy load, an energy cost, or a cost savings for a building using a weather data set ranked to be the most accurate.

35. The computer-readable medium of claim 34, comprising calculating the energy load and cost savings for a building energy conservation measure using the weather data set ranked to be the most accurate.

36. The computer-readable medium of claim 34, wherein the ranking of the weather data sets includes performing quality control checks on hourly weather values.

37. The computer-readable medium of claim 34, wherein the ranking of the weather data sets includes performing quality control checks on daily weather values.

38. The computer-readable medium of claim 34, wherein the ranking of the weather data sets includes performing quality control checks on forecast weather values.

39. The computer-readable medium of claim 34, comprising:

storing the weather data sets in a memory; and

automatically ranking the stored weather data sets if an up-to-date weather data set is not unavailable.