Method and system for generating occupant schedules

• US 10,453,005 B2
• Filed: 08/09/2011
• Issued: 10/22/2019
• Est. Priority Date: 08/09/2011
• Status: Active Grant

• Current Assignee: Autodesk, Inc.
• Original Assignee: Autodesk, Inc.
• Inventors: Goldstein, Rhys, Tessier, Alexander, Khan, Azam
• Primary Examiner(s): Alhija, Saif A
• Application Number:
  US13/206,352
• Publication Number:
  US 20130041632A1
• Time in Patent Office: 2,996 Days
• Field of Search: 703 2
• 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 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling

Abstract

Embodiments of the present invention generally relate to the modeling of building occupant behavior. An embodiment of the present invention relates to a method for generating interdependent schedules in an occupant simulation model. Within schedules generated in accordance with the present invention may be included activities that require the attendance of other occupants. Disclosed are methods for distributing multi-occupant activities among other occupants of a building. In another embodiment of the present invention, personas are used to generate simulated occupant schedules from a limited number of real-world occupant surveys. Characteristics of the real-world schedules are manipulated so as to fit desired characteristics. Multiple persona models are disclosed.

Claims

1. A method for generating schedules for occupants of a building, comprising:

receiving an identification of a first set of activities that can be assigned to a first occupant and a second occupant of a building, wherein one or more attributes are associated with each activity in the first set of activities;

for each of the one or more attributes, receiving one or more parameters for bounding a variability of the one or more attributes for determining an interval range for the one or more attributes, wherein at least one attribute comprises a start time, an arrival time, an end time, a departure time, a number of breaks in a day, or a percentage of time spent performing an activity in a day that is represented by an interval range having an associated probability p, wherein for the at least one attribute, the interval range boundaries b_lower and b_upper of the associated probability p is determined from a mean value μ and

a standard deviation σ

of the at least one attribute using the equation;

2. The method of claim 1, wherein generating the first occupant model includes the steps of

collecting a first predetermined number of survey schedules;

calculating weighting coefficients for the first predetermined number of survey schedules, wherein the weighting coefficients transform statistics of the survey schedules to conform to the one or more parameters for bounding a variability of the one or more attributes.

3. The method of claim 2, wherein the first predetermined number of survey schedules are obtained from a survey of occupants of a real-world building.

4. The method of claim 2, wherein the weighting coefficients are calculated to be positive numbers only.

5. The method of claim 1, wherein the one or more parameters for bounding a variability of the one or more attributes determine a mean and standard deviation for a probability of assigning the first set of activities.

6. The method of claim 1, wherein the one or more parameters for bounding a variability of the one or more attributes determine a mean and standard deviation for a probability of assigning the one or more first values and the one or more second values to the one or more attributes of the first activity.

7. The method of claim 1, wherein generating the first occupant model includes the steps of

generating a first predetermined number of artificial schedules that include the first set of activities and the one or more attributes associated with each activity in the first set of activities;

calculating weighting coefficients for the first predetermined number of artificial schedules, wherein the weighting coefficients transform statistics of the survey schedules to conform to the one or more parameters for bounding a variability of the one or more attributes.

8. The method of claim 6, wherein the weighting coefficients are calculated using a least squares algorithm.

9. The method of claim 6, wherein the weighting coefficients are calculated to be non-negative numbers only.

21. The method of claim 1, further comprising:

assigning a second activity to the first occupant according to the first occupant model, wherein the second activity is chosen from the first set of activities; and

assigning a third activity to the second occupant according to the first occupant model, wherein the third activity is chosen from the first set of activities, the third activity being different than the second activity.

22. The method of claim 21, further comprising:

assigning, for the first occupant, one or more values to the one or more attributes of the second activity according to the first occupant model; and

assigning, for the second occupant, one or more values to the one or more attributes of the third activity according to the first occupant model.

23. The method of claim 1, wherein an interval range for an attribute comprises a lower value and an upper value based on a probability associated with the interval range.

24. The method of claim 1, wherein an interval range for an attribute comprises a lower value and an upper value based on a probability, mean, and standard deviation associated with the interval range.

25. The method of claim 1, wherein for the at least one attribute, the interval range of an associated probability is determined from a mean value and a standard deviation of the at least one attribute.

10. A non-transitory computer-readable medium including instructions that, when executed by a processing unit, cause the processing unit to generate schedules for individuals that are occupants of a building, by performing the steps of:

receiving an identification of a first set of activities that can be assigned to a first occupant and a second occupant of a building, wherein one or more attributes are associated with each activity in the first set of activities;

for each of the one or more attributes, receiving one or more parameters for bounding a variability of the one or more attributes for determining an interval range for the one or more attributes, wherein at least one attribute comprises a start time, an arrival time, an end time, a departure time, a number of breaks in a day, or a percentage of time spent performing an activity in a day that is represented by an interval range having an associated probability p, wherein for the at least one attribute, the interval range boundaries b_lower and b_upper of the associated probability p is determined from a mean value μ and

a standard deviation σ

of the at least one attribute using the equation;

11. The non-transitory computer-readable medium of claim 10, wherein generating the first occupant model includes the steps of

collecting a first predetermined number of survey schedules;

calculating weighting coefficients for the first predetermined number of survey schedules, wherein the weighting coefficients transform statistics of the survey schedules to conform to the one or more parameters for bounding a variability of the one or more attributes.

12. The non-transitory computer-readable medium of claim 11, wherein the first predetermined number of survey schedules are obtained from a survey of occupants of a real-world building.

13. The non-transitory computer-readable medium of claim 10, wherein the weighting coefficients are calculated to be non-negative numbers only.

14. The non-transitory computer-readable medium of claim 10, wherein the one or more parameters for bounding a variability of the one or more attributes determine a mean and standard deviation for a probability of assigning the first set of activities.

15. The non-transitory computer-readable medium of claim 10, wherein the one or more parameters for bounding a variability of the one or more attributes determine a mean and standard deviation for a probability of assigning the one or more first values and the one or more second values to the one or more attributes of the first activity.

16. The non-transitory computer-readable medium of claim 10, wherein generating the first occupant model includes the steps of generating a first predetermined number of artificial schedules that include the first set of activities and the one or more attributes associated with each activity in the first set of activities;

calculating weighting coefficients for the first predetermined number of artificial schedules, wherein the weighting coefficients transform statistics of the survey schedules to conform to the one or more parameters for bounding a variability of the one or more attributes.

17. The non-transitory computer-readable medium of claim 16, wherein the weighting coefficients are calculated using a least squares algorithm.

18. The non-transitory computer-readable medium of claim 16, wherein the weighting coefficients are calculated to be non-negative numbers only.

19. A computing device comprising:

a data bus;

a memory unit coupled to the data bus;

a processing unit coupled to the data bus and configured to;

receive an identification of a first set of activities that can be assigned to a first occupant and a second occupant of a building, wherein one or more attributes are associated with each activity in the first set of activities;

for each of the one or more attributes, receive one or more parameters for bounding a variability of the one or more attributes for determining an interval range for the one or more attributes, wherein at least one attribute comprises a start time, an arrival time, an end time, a departure time, a number of breaks in a day, or a percentage of time spent performing an activity in a day that is represented by an interval range having an associated probability p, wherein for the at least one attribute, the interval range boundaries b_lower and b_upper of the associated probability p is determined from a mean value μ and

a standard deviation σ

of the at least one attribute using the equation;

20. The computing device of claim 19, wherein the memory includes instructions that, when executed by the processing unit, cause the processing unit to perform the receiving, generating, and assigning steps.