Forecasting system

• US 20080255760A1
• Filed: 04/16/2007
• Published: 10/16/2008
• Est. Priority Date: 04/16/2007
• Status: Abandoned Application

• Current Assignee: Honeywell International Inc.
• Original Assignee: Honeywell International Inc.
• Inventors: Rojicek, Jiri, Marik, Karel
• Application Number:
  US11/787,324
• Publication Number:
  US 20080255760A1
• Time in Patent Office: Days
• US Class Current: 702/3
• 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

A process includes providing a plurality of forecasts from a plurality of forecasting models. The plurality of forecasts each includes a mean and a variance. A model weight is calculated for each forecasting model. The model weight is proportional to the ability of that model to successfully forecast a queried situation. The plurality of forecasts are combined using an aggregate mean and an aggregate variance of the plurality of forecasts.

Claims

1. A utility forecasting system comprising:

a module that provides one or more utility forecasts from one or more utility forecasting models, the one or more utility forecasts each comprising a statistical measure;

a module that calculates a model weight for each utility forecasting model, the model weight being proportional to the ability of that model to successfully forecast a queried situation; and

a module that combines the one or more utility forecasts using an aggregate statistical measure of the one or more utility forecasts;

wherein inputs to the utility forecasts include one or more of a meteorological forecast, a production plan, or operator-defined values.

2. The system of claim 1, wherein the statistical measure comprises a mean and a variance, and the aggregate statistical measure comprises an aggregate mean and an aggregate variance.

3. The system of claim 2, wherein the aggregate mean of the plurality of forecasts comprises

$M=\sum _{}^{}$

i = 0 k 

w i ·

m i

wherein m_i, represents a mean for each utility forecasting model; and

wherein w_i, represents the model weight of each utility forecasting mode; and

.

wherein the aggregate variance of the one or more utility forecasts comprises;

$V=\underset{i=0}{\overset{k}{\sum <br><br>}}<br><br>w_i·<br><br>\left(v_i+m_i^2\right)-M^2$

wherein w_i represents the model weight of each utility forecasting model; and

wherein v_i represents a variance of each utility forecasting model.

4. The system of claim 1, wherein the module that calculates the model weight comprises:

a module that searches a database to locate process histories of models for one or more situations that are similar to the queried situation;

a module that locates past utility forecasts for all the models found in the search; and

a module that weights the models found in the search as a function of each model'"'"'s accuracy in a situation similar to the queried situation.

5. The system of claim 4, wherein the module that searches a database comprises:

a module that determines a Euclidean distance between the queried situation and a model process history;

a module that transforms the Euclidean distance into a weight by applying a kernel weighting; and

a module that normalizes the weights.

6. The system of claim 5, wherein the kernel weighting comprises at least one of a Guassian kernel and an Epanechnikov kernel.

7. The system of claim 4, further comprising a module that tunes the utility forecasting system by:

evaluating the model weights for all the process histories located in the search of the database;

tuning each model so that a best performance is achieved for queries with a high model weight.

8. A process of utility forecasting comprising:

providing one or more utility forecasts from one or more utility forecasting models, the one or more utility forecasts each comprising a statistical measure;

calculating a model weight for each utility forecasting model, the model weight being proportional to the ability of that model to successfully forecast a queried situation; and

combining the one or more utility forecasts using an aggregate statistical measure of the one or more forecasts;

wherein inputs to the utility forecasts include one or more of a meteorological forecast, a production plan, or operator-defined values.

9. The process of claim 8, wherein the statistical measure comprises a mean and a variance, and the aggregate statistical measure comprises an aggregate mean and an aggregate variance.

10. The process of claim 9, wherein the aggregate mean of the plurality of forecasts comprises

$M=\sum _{}^{}$

i = 0 k 

w i ·

m i

wherein m_i represents a mean for each utility forecasting model; and

wherein w_i, represents the model weight of each utility forecasting model; and

wherein the aggregate variance of the one or more utility forecasts comprises;

$V=\underset{i=0}{\overset{k}{\sum <br><br>}}<br><br>w_i·<br><br>\left(v_i+m_i^2\right)-M^2$

wherein w_i, represents the model weight of each utility forecasting model; and

wherein v_i, represents a variance of each utility forecasting model.

11. The process of claim 8, wherein the calculation of the model weight comprises:

searching a database to locate process histories of models for one or more situations that are similar to the queried situation;

locating past utility forecasts for all the models found in the search; and

weighting the models found in the search as a function of each model'"'"'s accuracy in a situation similar to the queried situation.

12. The process of claim 11, wherein the searching a database comprises:

determining a Euclidean distance between the queried situation and a model process history;

transforming the Euclidean distance into a weight by applying a kernel weighting; and

normalizing the weights.

13. The process of claim 12, wherein the kernel weighting comprises at least one of a Guassian kernel and an Epanechnikov kernel.

14. The process of claim 11, further comprising tuning the utility forecasting system by:

evaluating the model weights for all the process histories located in the search of the database;

tuning each model so that a best performance is achieved for queries with a high model weight.

15. The process of claim 8, wherein the calculating a model weight comprises:

evaluating the similarity of past situations for a model to the queried situation; and

aggregating forecast accuracy of a model in all past situations that are similar to the queried situation.

16. A machine readable medium including instructions for executing a process comprising:

providing one or more utility forecasts from one or more utility forecasting models, the one or more utility forecasts each comprising a statistical measure;

calculating a model weight for each utility forecasting model, the model weight being proportional to the ability of that model to successfully forecast a queried situation; and

combining the one or more utility forecasts using an aggregate statistical measure of the one or more utility forecasts;

wherein inputs to the utility forecasts include one or more of a meteorological forecast, a production plan, or operator-defined values.

17. The machine readable medium of claim 16, wherein the statistical measure comprises a mean and a variance, and the aggregate statistical measure comprises an aggregate mean and an aggregate variance.

18. The machine readable medium of claim 17, wherein the aggregate mean of the plurality of forecasts comprises

$M=\sum _{}^{}$

i = 0 k 

w i ·

m i

wherein m_i, represents a mean for each utility forecasting model; and

wherein w_i, represents the model weight of each utility forecasting model; and

wherein the aggregate variance of the one or more utility forecasts comprises;

$V=\underset{i=0}{\overset{k}{\sum <br><br>}}<br><br>w_i·<br><br>\left(v_i+m_i^2\right)-M^2$

wherein w_i represents the model weight of each utility forecasting model; and

wherein v_i represents a variance of each utility forecasting model.

19. The machine readable medium of claim 16, further comprising instructions for:

searching a database to locate process histories of models for one or more situations that are similar to the queried situation;

locating past utility forecasts for all the models found in the search; and

weighting the models found in the search as a function of each model'"'"'s accuracy in a situation similar to the queried situation.

20. The machine readable medium of claim 19, wherein the instructions for searching a database comprises:

determining a Euclidean distance between the queried situation and a model process history;

transforming the Euclidean distance into a weight by applying a kernel weighting; and

normalizing the weights.