Weather-driven multi-category infrastructure impact forecasting

• US 10,387,802 B2
• Filed: 10/07/2016
• Issued: 08/20/2019
• Est. Priority Date: 04/14/2015
• Status: Active Grant

• Current Assignee: Utopus Insights, Inc. (Vestas Wind Systems A/S)
• Original Assignee: Utopus Insights, Inc. (Vestas Wind Systems A/S)
• Inventors: Heng, Fook-Luen, Li, Zhiguo, Siegel, Stuart A., Singhee, Amith, Wang, Haijing
• Primary Examiner(s): Chaki, Kakali
• Assistant Examiner(s): Zidanic, Michael
• Application Number:
  US15/287,846
• Publication Number:
  US 20170017904A1
• Time in Patent Office: 1,047 Days
• Field of Search: None
• CPC Class Codes: G01W 1/10 Devices for predicting weather conditions computers per se G06; display devices G09
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: G06Q 10/06315 Needs-based resource requirements planning or analysis

Abstract

A method, system, and computer program product for resource management are described. The method includes selecting trouble regions within the service area, generating clustered regions, and training a trouble forecast model for the trouble regions for each type of damage, the training for each trouble region using training data from every trouble region within the clustered region associated with the trouble region. The method also includes applying the trouble forecast model for each trouble region within the service area for each type of damage, determining a trouble forecast for the service area for each type of damage based on the trouble forecast for each of the trouble regions within the service area, and determining a job forecast for the service area based on the trouble forecast for the service area, wherein the managing resources is based on the job forecast for the service area.

Claims

1. A non-transitory computer program product for managing resources based on weather-related damage in a service area, the non-transitory computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to perform a method comprising:

selecting trouble regions within the service area;

generating clustered regions, each of the clustered regions including at least one of the trouble regions within the service area and each of the trouble regions within the service area being associated with one of the clustered regions;

training a trouble forecast model for each of the trouble regions for each type of the weather-related damage, the training for each of the trouble regions using training data from every one of the trouble regions within the clustered region associated with the trouble region, wherein the training data from each of the trouble regions within the clustered region is multiplied by a different scaling factor;

applying the trouble forecast model for each of the trouble regions within the service area for each type of the weather-related damage to obtain a trouble forecast for each of the trouble regions within the service area for each type of the weather-related damage;

determining a trouble forecast for the service area for each type of the weather-related damage based on the trouble forecast for each of the trouble regions within the service area;

determining a job forecast for the service area based on the trouble forecast for the service area according to a trouble-to-job mapping, wherein the managing resources is based on the job forecast for the service area, the applying the trouble forecast model for each of the trouble regions includes spatially interpolating weather forecast information within the respective trouble region to a centroid of the respective trouble region to obtain interpolated data and computing scoring input features from the interpolated data that are used to determine the trouble forecast for the respective trouble region; and

outputting the job forecast to a display device in order to facilitate management of resources by relocating equipment and personnel within the service area according to the job forecast in order to minimize an impact of the trouble forecast.

2. The computer program product of claim 1, wherein the impact comprises an amount of time required to resolve weather-related damage to the resources.

3. The computer program product of claim 1, wherein the impact comprises an amount of time required to resolve a weather-related interruption to service in the service area.

4. The computer program product of claim 1, wherein the training data is based on trouble history records and historical weather data.

5. The computer program product of claim 1, wherein the trouble forecast model further comprises defining a relationship between the training data and a trouble forecast for each of the trouble regions within the service area for each type of weather-related damage.

6. The computer program product of claim 5 further comprising collecting historical data from one of a National Oceanic and Atmospheric Administration (NOAA) Metar station or a NOAA Mesonet station.

7. The computer program product of claim 1, wherein generating clustered regions further comprises generating cluster regions based on similarity of the trouble regions in at least one of job type or weather-related damage.

8. The computer program product of claim 1, wherein determining the job forecast for the service area based on the service area comprises determining a forecasting horizon for each trouble region and each type of weather-related damage.

9. The computer program product of claim 8 further comprises, dividing the forecasting horizon into a plurality of prediction periods.

10. The computer program of claim 9, wherein determining the job forecast for the service area based on the service area further comprises computing, for each trouble region, trouble type and the plurality of prediction periods, a trouble count forecast based on a probability distribution.

11. A system for managing resources based on weather-related damage in a service area, the system comprising:

at least one processor; and

a computer readable storage medium having computer readable program code embodied therewith and executable by the at least one processor, the computer readable program code performs the steps of;

select trouble regions within the service area;

generate clustered regions, each of the clustered regions includes at least one of the trouble regions within the service area and each of the trouble regions within the service area being associated with one of the clustered regions;

train a trouble forecast model for each of the trouble regions for each type of the weather-related damage, the training for each of the trouble regions using training data from every one of the trouble regions within the clustered region associated with the trouble region, wherein the training data from each of the trouble regions within the clustered region is multiplied by a different scaling factor;

apply the trouble forecast model for each of the trouble regions within the service area for each type of the weather-related damage to obtain a trouble forecast for each of the trouble regions within the service area for each type of the weather-related damage;

determine a trouble forecast for the service area for each type of the weather-related damage based on the trouble forecast for each of the trouble regions within the service area;

determine a job forecast for the service area based on the trouble forecast for the service area according to a trouble-to-job mapping, wherein the managing resources is based on the job forecast for the service area, the applying the trouble forecast model for each of the trouble regions includes spatially interpolating weather forecast information within the respective trouble region to a centroid of the respective trouble region to obtain interpolated data and computing scoring input features from the interpolated data that are used to determine the trouble forecast for the respective trouble region; and

outputting the job forecast to a display device in order to facilitate management of resources by relocating equipment and personnel within the service area according to the job forecast in order to minimize an impact of the trouble forecast.

12. The system of claim 11, wherein the impact comprises an amount of time required to resolve weather-related damage to the resources.

13. The system of claim 11, wherein the impact comprises an amount of time required to resolve a weather-related interruption to service in the service area.

14. The system of claim 11, wherein the training data is based on trouble history records and historical weather data.

15. The system of claim 14, wherein the trouble forecast model further comprises defining a relationship between the training data and a trouble forecast for each of the trouble regions within the service area for each type of weather-related damage.

16. The computer program product of claim 14 further comprising collecting historical data from one of a National Oceanic and Atmospheric Administration (NOAA) Metar station or an NOAA Mesonet station.

17. The computer program product of claim 14, wherein generating clustered regions comprises generating cluster regions based on similarity of the trouble regions in at least one of job type or weather-related damage.