Systems and methods for identifying mis-set and mis-calibrated atmosphere control systems

Abstract

Systems and methods are provided to for identifying a mis-set, mis-calibrated, or malfunctioning thermostat. The method can include receiving ambient environmental data from at least one sensor monitoring an asset; identifying aberrative behavior in the ambient environment data; obtaining a complement of the aberrative behavior; determining a segment of normal behavior in the complement; identifying a mis-set subsequence in the segment; generating a report documenting the mis-set subsequence; and transmitting the report to a user device.

1 . A method for identifying a mis-set, mis-calibrated, or malfunctioning thermostat comprising:
 receiving ambient environmental data from at least one sensor monitoring an asset;   identifying aberrative behavior in the ambient environmental data;   obtaining a complement of the aberrative behavior;   determining a segment of normal behavior in the complement;   identifying a mis-set subsequence in the segment;   generating a report documenting the mis-set subsequence; and   transmitting the report to a user device.   
     
     
         2 . The method of  claim 1 , wherein the ambient environmental data comprises at least one of temperature or humidity data. 
     
     
         3 . The method of  claim 1 , wherein identifying aberrative behavior comprises using a machine learning algorithm trained on historical ambient environmental data associated with the asset to identify the aberrative behavior. 
     
     
         4 . The method of  claim 1 , wherein identifying aberrative behavior comprises using a machine learning algorithm trained on historical ambient environmental data associated with other assets similar to the asset to identify the aberrative behavior. 
     
     
         5 . The method of  claim 1 , wherein identifying aberrative behavior comprises identifying periods of time within the ambient environmental data with a value at least three standard deviations greater than a mean. 
     
     
         6 . The method of  claim 1 , wherein determining the segment of normal behavior comprises:
 partitioning the ambient environmental data into windows of a pre-defined size;   normalizing the windows;   applying dynamic time warping and clustering to the normalized windows; and   identifying a majority cluster as the segment of normal behavior.   
     
     
         7 . The method of  claim 1 , wherein determining the segment of normal behavior comprises:
 partitioning the ambient environmental data into windows of a pre-defined size;   applying an auto-correlation function to the windows;   determining a strength of local minima and maxima for each window; and   identifying a window as normal based on the strength of the window.   
     
     
         8 . The method of  claim 7 , wherein identifying the window as normal based on the strength of the window comprises determining that the auto-correlation function is sufficiently periodic. 
     
     
         9 . The method of  claim 1 , wherein determining the segment of normal behavior comprises partitioning the ambient environmental data with a MatrixProfile (MP) and Corrected Arc Curve (CAC) technique. 
     
     
         10 . The method of  claim 9  further comprising using a machine learning classifier to identify the segment of normal behavior. 
     
     
         11 . The method of  claim 9  further comprising:
 applying an auto-correlation function to the partitioned ambient environmental data; 
 determining a strength of local minima and maxima for each partition; and 
 identifying a partition as normal based on the strength. 
 
     
     
         12 . The method of  claim 1 , wherein identifying the mis-set subsequence in the segment comprises:
 partitioning the segment of normal behavior into a plurality of small sections;   calculating a mean and maximum temperature for each of the plurality of small sections;   determining, based on the mean and maximum temperature, that at least one small section of the plurality of small sections is mis-set;   partitioning the segment of normal behavior into a plurality of large sections;   calculating a mis-set percentage for each large section of the plurality of large sections; and   determining, based on the mis-set percentage, that at least one large section is mis-set.   
     
     
         13 . The method of  claim 12 , wherein the plurality of small sections are a size of about two times a periodicity of the ambient environmental data. 
     
     
         14 . The method of  claim 12 , wherein the plurality of large sections are a size of about ten times a periodicity of the ambient environmental data. 
     
     
         15 . The method of  claim 12  further comprising:
 obtaining the aberrative behavior; 
 identifying a preceding and succeeding segment for each instance of aberrative behavior; 
 determining that the preceding and succeeding segment are each mis-set; and 
 identifying the instance of aberrative behavior, the preceding segment, and the succeeding segment as a mis-set subsequence. 
 
     
     
         16 . A system for identifying a mis-set, mis-calibrated, or malfunctioning thermostat comprising:
 a sensor positioned at an asset and configured to measure ambient environmental data associated with the asset; and   a server configured to:
 receive ambient environmental data from the sensor, the ambient environmental data comprising at least one of temperature or humidity data; 
 identify aberrative behavior in the ambient environmental data; 
 obtain a complement of the aberrative behavior; 
 determine a segment of normal behavior in the complement; 
 identify a mis-set subsequence in the segment; 
 generate a report documenting the mis-set subsequence; and 
 transmit the report to a user device. 
   
     
     
         17 . The system of  claim 16 , wherein identifying the mis-set subsequence in the segment comprises:
 partitioning the segment of normal behavior into a plurality of small sections;   calculating a mean and maximum temperature for each of the plurality of small sections;   determining, based on the mean and maximum temperature, that at least one small section of the plurality of small sections is mis-set;   partitioning the segment of normal behavior into a plurality of large sections;   calculating a mis-set percentage for each large section of the plurality of large sections; and   determining, based on the mis-set percentage, that at least one large section is mis-set.   
     
     
         18 . The system of  claim 17 , wherein the server is configured to:
 obtain the aberrative behavior;   identify a preceding and succeeding segment for each instance of aberrative behavior;   determine that the preceding and succeeding segment are each mis-set; and   identify the instance of aberrative behavior, the preceding segment, and the succeeding segment as a mis-set subsequence.   
     
     
         19 . The system of  claim 16 , wherein determining the segment of normal behavior comprises:
 partitioning the ambient environmental data into windows of a pre-defined size;   normalizing the windows;   applying dynamic time warping and clustering to the normalized windows; and   identifying a majority cluster as the segment of normal behavior.   
     
     
         20 . The system of  claim 16 , wherein determining the segment of normal behavior comprises:
 partitioning the ambient environmental data into windows of a pre-defined size;   applying an auto-correlation function to the windows;   determining a strength of local minima and maxima for each window; and   identifying a window as normal based on the strength.