# US 8,751,432 B2 · Automated facilities management system > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 8,751,432 B2 - Document ID: US8751432B2 - Status: Granted - Kind: B2 - Application number: 13/225,398 - Filed: 2011-09-02 - Published: 2012-06-14 - Granted: 2014-06-10 - Earliest claimed: 2010-09-02 - Applicants: Anker Berg-Sonne; David M. Huselid; Howard A. Nunes; Sumanth Rayancha - Inventors: Anker Berg-Sonne; David M. Huselid; Howard A. Nunes; Sumanth Rayancha - Web page: https://patentgrep.com/patent/US8751432B2 ## CPC - G05B 15/02: electric - H02J 3/003: {Load forecast, e.g. methods or systems for forecasting future load demand} - H02J 2105/57: {according to a pre-established time schedule} - Y02B 70/3225: Demand response systems, e.g. load shedding, peak shaving - 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) - 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 - Y04S 20/00: Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof - Y04S 20/222: Demand response systems, e.g. load shedding, peak shaving - G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G) - H02J: ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY - Y02B: CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS - Y04S: SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS ## 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 ### Claim 1 A method for predicting future behavior, comprising: collecting historical sensor data related to a behavior; processing the historical sensor data into a plurality of intervals related to the behavior, each interval comprising a plurality of time slices; generating a plurality of distinct interval pairs, each interval pair comprising two intervals included in the plurality of intervals; for each respective interval pair included in the plurality of interval pairs, comparing time slices between intervals in the respective interval pair to yield a similarity measurement; generating one or more clusters of intervals based on the plurality of interval pairs and their respective similarity measurements; identifying a plurality of patterns based on the one or more clusters of intervals; comparing current data related to the behavior with the plurality of patterns to determine a best fit pattern that is most similar to the current data; and applying data in the best fit pattern to predict future data related to the behavior. ### Claim 2 (depends on 1) The method of claim 1, wherein the behavior is a group behavior. ### Claim 3 (depends on 1) The method of claim 1, wherein generating the one or more clusters of intervals based on the plurality of interval pairs and their respective similarity measurements comprises: weighing recent current data more than distant in time current data. ### Claim 4 (depends on 1) The method of claim 1 further comprising making adjustments in anticipation of the predicted future data. ### Claim 5 (depends on 1) The method of claim 1 further comprising soliciting feedback data from users. ### Claim 6 (depends on 5) The method of claim 5 further comprising making adjustments in response to the feedback data. ### Claim 7 A system for predicting future behavior, comprising: a database configured to store a collection of historical data related to a behavior, the historical data organized into intervals related to the behavior with each interval comprising a plurality of time slices; a processor configured to: generate a plurality of distinct interval pairs, each interval pair comprising two intervals included in the plurality of intervals, for each respective interval pair included in the plurality of interval pairs, compare time slices between intervals in the respective interval pair to yield a similarity measurement, generate one or more clusters of intervals based on the plurality of interval pairs and their respective similarity measurements; identify a plurality of patterns based on the one or more clusters of intervals, determine 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 local energy optimizer component configured to: predict future data related to the behavior derived from the best fit pattern. ### Claim 8 (depends on 7) The system of claim 7, wherein the processor and the database are located in a server connected to the local energy optimizer component over a computer network. ### Claim 9 (depends on 8) The system of claim 8, wherein the local energy optimizer component is further configured to: gather the collection of historical data from one or more sensors; and transfer the collection of historical data to the server according to a predetermined schedule. ### Claim 10 A computer-implemented method for predicting future behavior, comprising: receiving, by a computer system, sensor data related to a behavior from one or more sensors; dividing, by the computer system, the sensor data into a plurality of time intervals; creating, by the computer system, a list of pairings of the plurality time intervals; determining, by the computer system, a similarity value for each of pairing included in the list; ranking, by the computer system, the pairings in the list in increasing order according to their respective similarity values; performing, by the computer system, an iterative clustering process to assign each time interval to one of a plurality of clusters, the iterative clustering process comprising: determining, by the computer system, a pattern fitting current data related to the behavior based on the plurality of clusters; and predicting, by the computer system, future data related to the behavior based on the pattern. ### Claim 11 (depends on 10) The method of claim 10, wherein the iterative clustering process is repeated until each time interval in the list of pairings is assigned to one of the plurality of clusters or each time interval in the list of pairings has a corresponding similarity value greater than a predetermined maximum cluster size. ### Claim 12 (depends on 10) The method claim 10, further comprising: segmenting each of the plurality of time intervals into a plurality of time slices, wherein the similarity value of each pairing in the list is determined across each time slice in the respective pairing. ### Claim 13 (depends on 10) The method of claim 10, wherein determining a best fit pattern to the current data related to the behavior based on the plurality of clusters comprises: generating a plurality of patterns, each pattern associated with one of the plurality of clusters; and comparing the current data to each of the plurality of patterns to determine the best fit pattern. ### Claim 14 (depends on 13) The method of claim 13, wherein the pattern associated with a respective cluster is determined by averaging sensor data across time intervals included in the respective cluster. ### Claim 15 (depends on 14) The method of claim 14, wherein the sensor data is averaged across each time slice of each time interval included in the respective cluster. ### Claim 16 (depends on 13) The method of claim 13, further comprising: for each of the plurality of patterns, measuring similarity across each time interval included in a respective pattern to yield a density value for the respective pattern. ### Claim 17 (depends on 10) The method of claim 10, wherein the iterative clustering process comprises: setting a current maximum cluster size based on a top similarity value associated with a highest ranked pairing included in the list, merging existing clusters in the plurality of clusters if the existing clusters can be combined into a single cluster without exceeding the current maximum cluster size, for each pairing in the list, creating a new cluster in the plurality of clusters for a respective pairing if the respective pairing is associated with a corresponding similarity value less than the current maximum cluster size, identifying one or more removable pairings in the list, wherein each removable pairing includes at least one time interval that has been assigned to one of the plurality of clusters, and removing the one or more removable pairings from the list.