Metrics monitoring and financial validation system (M2FVS) for tracking performance of capital, operations, and maintenance investments to an infrastructure

• US 8,725,665 B2
• Filed: 08/20/2012
• Issued: 05/13/2014
• Est. Priority Date: 02/24/2010
• Status: Active Grant

• Current Assignee: Trustees Of Columbia University In The City Of New York (Columbia University)
• Original Assignee: Trustees Of Columbia University In The City Of New York (Columbia University)
• Inventors: Anderson, Roger N., Boulanger, Albert, Wu, Leon, Lee, Serena
• Primary Examiner(s): Chaki, Kakali
• Assistant Examiner(s): Sitiriche, Luis
• Application Number:
  US13/589,737
• Publication Number:
  US 20130073488A1
• Time in Patent Office: 631 Days
• Field of Search: None
• US Class Current: 706/12
• 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

Abstract

Techniques for evaluating the accuracy of a predicted effectiveness of an improvement to an infrastructure include collecting data, representative of at least one pre-defined metric, from the infrastructure during first and second time periods corresponding to before and after a change has been implemented, respectively. A machine learning system can receive compiled data representative of the first time period and generate corresponding machine learning data. A machine learning results evaluator can empirically analyze the generated machine learning data. An implementer can implement the change to the infrastructure based at least in part on the data from a machine learning data outputer. A system performance improvement evaluator can compare the compiled data representative of the first time period to that of the second time period to determine a difference, if any, and compare the difference, if any, to a prediction based on the generated machine learning data.

Claims

1. A system for evaluating the accuracy of a predicted effectiveness of an improvement to an infrastructure based on data collected from the infrastructure during a first time period before a change to an infrastructure has been implemented and a second time period after the change to the infrastructure has been implemented, the collected data including information representative of at least one pre-defined metric of the infrastructure, comprising:

(a) a data collector for collecting the data from the infrastructure during the first time period and the second time period, wherein the data meets at least one predetermined threshold requirement;

(b) a compiler, adapted to receive and compile, via one or more processors, the collected data to generate compiled data representative of the first time period and compiled data representative of the second time period;

(c) an input data evaluator, adapted to evaluate, via one or more processors, the compiled data and provide the compiled data to a machine learning system if the compiled data meets the at least one predetermine threshold requirement;

(d) a machine learning system, coupled to the compiler and adapted to receive the complied data representative of the first time period therefrom and generate, via the one or more processors, corresponding machine learning data;

(e) a machine learning results evaluator, coupled to the machine learning system, to empirically analyze, via the one or more processors, the generated machine learning data;

(f) an implementer to implement the change to the infrastructure, wherein the change to the infrastructure is based at least in part on the machine learning data, and;

(g) a system performance improvement evaluator, coupled to the compiler and adapted for receiving the compiled data representative of the first time period and the compiled data representative of the second time period therefrom, and coupled to the machine learning system and adapted for receiving the generated machine learning data therefrom, for;

(i) comparing the compiled data representative of the first time period to the compiled data representative of the second time period to determine a difference, if any, and

(ii) comparing the difference, if any, determined in (i) to a prediction based on the generated machine learning data.

2. The system of claim 1, further comprising a unified user interface in communication with at least one of the machine learning results evaluator and the system performance evaluator.

3. The system of claim 2, further comprising an input data evaluator, in communication with the data compiler, to determine if the compiled collected data meets at least one predetermined threshold requirement representative of data quality.

4. The system of claim 3, wherein the input data evaluator is in communication with the unified user interface.

5. The system of claim 3, wherein the machine learning system receives only data that meets the at least one predetermined threshold requirement representative of data quality.

6. The system of claim 1, wherein the infrastructure is an electrical grid.

7. The system of claim 4, wherein the input data evaluator sends to the unified user interface a sparkline graph.

8. The system of claim 2, wherein the machine learning results evaluator sends to the unified user interface at least one of a ROC Curve, or an Area under a ROC curve.

9. The system of claim 6, wherein the machine learning data results evaluator outputs a list of electrical feeders ranked according to their susceptibility to failure.

10. A method for evaluating the accuracy of a predicted effectiveness of an improvement to an infrastructure based on data collected from the infrastructure during a first time period before a change to an infrastructure has been implemented and a second time period after the change to the infrastructure has been implemented, the collected data including information representative of at least one pre-defined metric of the infrastructure, comprising:

(a) collecting data from the infrastructure during the first time period and the second time period, wherein the data meets at least one predetermined threshold requirement;

(b) compiling the collected data to generate compiled data representative of the first time period and compiled data representative of the second time period;

(c) providing the compiled data to a machine learning system if the compiled data meets the at least one predetermine threshold requirement;

(d) performing machine learning on the compiled data representative of the first time period and generating corresponding machine learning data;

(e) storing and empirically evaluating the generated machine learning data;

(f) implementing the change to the infrastructure, wherein the change to the infrastructure is based at least in part on the generated machine learning data, and

(g) receiving the compiled data representative of the first time period and the compiled data representative of the second time period therefrom, for;

(i) comparing the compiled data representative of the first time period to the compiled data representative of the second time period to determine a difference, if any, and

(ii) comparing the difference, if any, determined in (i) to a prediction based on the generated machine learning data.

11. The method of claim 10, further comprising communicating output from at least one of the machine learning results evaluator and the system performance evaluator to a unified user interface.

12. The method of claim 11, further comprising evaluating input data to determine if the compiled collected data meets at least one predetermined threshold requirement representative of data quality.

13. The method of claim 12, further comprising communicating the evaluated input data to the unified user interface.

14. The method of claim 13, wherein the machine learning receives only data that meets the at least one predetermined threshold requirement representative of data quality.

15. The method of claim 10, wherein the infrastructure is an electrical grid.

16. The method of claim 15, wherein the generated machine learning includes a list of electrical feeders ranked according to their susceptibility to failure.

17. A method of evaluating the accuracy of a predicted effectiveness of an improvement to an infrastructure, comprising:

(a) collecting data from the infrastructure during a first time period and a second time period, wherein the data meets at least one predetermined threshold requirement;

(b) compiling the collected data to generate compiled data representative of the first time period and compiled data representative of the second time period;

(c) providing the compiled data to a machine learning system if the compiled data meets the at least one predetermine threshold requirement;

(d) performing machine learning on the compiled data representative of the first time period and generating corresponding machine learning data;

(e) storing and empirically evaluating the generated machine learning data;

(f) implementing the change to the infrastructure, wherein the change to the infrastructure is based at least in part on the generated machine learning data, and

(g) receiving the compiled data representative of the first time period and the compiled data representative of the second time period therefrom, for;

(i) comparing the compiled data representative of the first time period to the compiled data representative of the second time period to determine a difference, if any, and

(ii) comparing the difference, if any, determined in (i) to a prediction based on the generated machine learning data.

18. The method of claim 17, wherein the infrastructure is an electrical grid.

19. The method of claim 18, wherein the predicted effectiveness of the improvement to the infrastructure is obtained based at least in part from machine learning.

20. The method of claim 19, wherein the machine learning receives only data that meets the at least one predetermined threshold requirement representative of data quality.