AUTOMATED FACILITIES MANAGEMENT SYSTEM

• US 20120150788A1
• Filed: 09/02/2011
• Published: 06/14/2012
• Est. Priority Date: 09/02/2010
• Status: Active Grant

• Current Assignee: PepperDash Technology Corporation
• Original Assignee: PepperDash Technology Corporation
• Inventors: Huselid, David M., Nunes, Howard A., Rayancha, Sumanth, Berg-Snne, Anker
• Granted Patent: US 8,751,432 B2
• Time in Patent Office: Days
• US Class Current: 706/48
• CPC Class Codes: G05B 15/02 electric
• CPC Class Codes: H02J 2310/54 according to a pre-established time schedule
• CPC Class Codes: H02J 3/003 Load forecast, e.g. methods or systems for forecasting future load demand
• CPC Class Codes: Y02B 70/3225 Demand response systems, e.g. load shedding, peak shaving
• CPC Class Codes: Y02B 90/20 Smart grids as enabling technology in buildings sector smart grids supporting the management or operation of end-user stationary applications in general, or like technologies with no associated climate change mitigation effect Y04S20/00
• CPC Class Codes: Y04S 10/50 Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
• CPC Class Codes: Y04S 20/00 Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
• CPC Class Codes: Y04S 20/222 Demand response systems, e.g. load shedding, peak shaving

Abstract

An automated facilities management system has the ability to predict occupant behavior by identifying recurring patterns in the way that people use buildings and comparing them with environmental characteristics. This technology is not limited to human behavior patterns, but extends to any mechanical systems or data points that tend to vary in recurring patterns. The data processing is carried out by rules engines triggered by relational database modifications.

Claims

1. A method for predicting future behavior, comprising:

collecting historical data related to a behavior;

processing the historical data into intervals related to the behavior;

processing the intervals of historical data into a plurality of patterns;

comparing current data related to the behavior with the patterns to determine a best fit pattern that is most similar to the current data;

applying the data in the best fit pattern to predict future data related to the behavior.

2. The method of claim 1, wherein the behavior is a group behavior.

3. The method of claim 1, wherein the intervals are divided into an equal number of slices.

4. The method of claim 3, wherein the slices are consecutive time slices.

5. The method of claim 1, wherein processing the intervals of historical data includes performing a cluster analysis to identify the patterns.

6. The method of claim 1, wherein comparing current data includes performing a cluster analysis to identify the best fit pattern from the plurality of patterns.

7. The method of claim 6, wherein the cluster analysis weighs recent current data more than distant current data.

9. The method of claim 1 further comprising making adjustments in anticipation of the predicted data.

10. The method of claim 1 further comprising soliciting feedback data from users.

11. The method of claim 10 further comprising making adjustments in response to the feedback data.

12. A system for predicting future behavior, comprising:

a collection of historical data related to a behavior, the historical data organized into intervals related to the behavior;

a plurality of patterns representing the intervals of historical data;

a best fit pattern to current data related to the behavior, the best fit pattern being the most similar to the current data; and

a prediction of future data related to the behavior derived from the best fit pattern.