Method of intelligent data analysis to detect abnormal use of utilities in buildings

• US 6,816,811 B2
• Filed: 07/20/2001
• Issued: 11/09/2004
• Est. Priority Date: 06/21/2001
• Status: Expired due to Term

• Current Assignee: Johnson Controls Tyco IP Holdings LLP (Johnson Controls International plc)
• Original Assignee: Johnson Controls Technology Company (Johnson Controls International plc)
• Inventors: Seem, John E.
• Primary Examiner(s): WEST, JEFFREY R
• Application Number:
  US09/910,371
• Publication Number:
  US 20030028350A1
• Time in Patent Office: 1,208 Days
• Field of Search: 702/60, 702/61, 702/62, 702/64, 702/65, 702/179, 702/180, 702/181, 702/182, 702/183, 705/412
• US Class Current: 702/179
• CPC Class Codes: G01D 4/14 Fixed-demand indicating or recording apparatus, i.e. where indication is made when a predetermined quantity has been consumed during a time interval greater or less than a predetermined time interval
• CPC Class Codes: G06F 17/18 for evaluating statistical data , e.g. average values, frequency distributions, probability functions, regression analysis forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04

Abstract

Outlier identification is employed to detect abnormally high or low energy use in a building. The utility use is measured periodically throughout each day and the measurements are grouped according to days that have similar average utility consumption levels. The data in each group is statistically analyzed using the Generalized Extreme Studentized Deviate (GESD) method. That method identifies outliers which are data samples that vary significantly from the majority of the data. The degree to which each outlier deviates from the remainder of the data indicates the severity of the abnormal utility consumption denoted by that outlier. The resultant outlier information is readily discernable by the building operators in accessing whether the cause of a particular occurrence of abnormal utility usage requires further investigation.

Claims

1. A method for determining abnormal consumption of a utility by a system:

repeatedly measuring a level of use of the utility thereby producing a plurality of utility measurements;

employing a statistical procedure to identify a set of outliers in the plurality of utility measurements;

removing the set of outliers from the plurality of utility measurements to define a set of non-outliers; and

evaluating performance of the system in response to the set of outliers and the set of non-outliers by determining a severity of abnormal utility usage represented by each outlier in the set of outliers, wherein determining a severity of abnormal utility usage for each outlier comprises calculating how far each outlier is from a robust estimate value for utility usage determined from the set of non-outliers.

2. The method as recited in claim 1 wherein determining a severity of abnormal utility usage comprises calculating how many standard deviations a given outlier is from the average value for utility usage by using the expression:

$z_j=\frac{x_{e,j}-{\stackrel{\_}{x}}_{\mathrm{robust}}}{s_{\mathrm{robust}}}$

where x_ej is the energy consumption for the j^th outlier, {overscore (x)}_robust is a robust estimate of the average energy consumption for days of the same day type as outlier j, and S_robust is a robust estimate of the standard deviation of energy consumption for days of the same day type.

3. The method as recited in claim 1 wherein determining a severity of abnormal utility usage comprises determining an amount that each outlier deviates from a mean of the plurality of utility measurements which are not identified as outliers.

4. The method as recited in claim 1 further comprising separating the plurality of utility measurements into groups wherein each group contains utility measurements acquired during days that under normal conditions have similar utility consumption levels;

and

wherein the statistical procedure is applied separately to each group.

5. The method as recited in claim 1 further comprising separating the plurality of utility measurements into groups wherein each group contains utility measurements acquired during predefined time periods that under normal conditions have similar utility consumption levels;

and

wherein the statistical procedure is applied separately to each group.

6. The method as recited in claim 1 wherein the outliers are identified using a Generalized Extreme Studentized Deviate (GESD) statistical procedure.

7. The method as recited in claim 6 wherein the Generalized Extreme Studentized Deviate (GESD) statistical procedure comprises:

(a) calculating an arithmetic mean ({overscore (x)}) of the plurality of utility measurements;

(b) finding an extreme utility measurement x_e,i which is the utility measurement that has a value which is farther numerically from the arithmetic mean ({overscore (x)}) than the other ones of the plurality of utility measurements;

(c) using the extreme utility measurement x_e,i to calculate an extreme studentized deviate R_i;

(d) calculating a 100α

percent critical value λ

_i for the extreme utility measurement x_e,i;

(e) declaring the extreme utility measurement x_e,i to be an outlier when the extreme studentized deviate R_i is greater than the 100α

percent critical value λ

_i.

8. The method as recited in claim 7 wherein the Generalized Extreme Studentized Deviate (GESD) statistical procedure further comprises:

removing the extreme utility measurement x_e,i from the plurality of utility measurements to form a new plurality of utility measurements; and

repeating steps (a) through (e) for the new plurality of utility measurements.

9. The method as recited in claim 7 wherein the extreme studentized deviate R_i is calculated according to the expression:

$R_i=\frac{<br><br>x_{e,i}-\stackrel{\_}{x}<br><br>}{s}$

where s is a standard deviation of the plurality of utility measurements.

10. The method as recited in claim 7 wherein the 100α

percent critical value λ

_i then is calculated using the equation;

${\mathrm{\lambda <br><br>}}_i=\frac{\left(n-i\right)<br><br>t_{n-i-1,p}}{\sqrt{\left(n-i+1\right)<br><br>\left(n-i-1+t_{n-i-1,p}^2\right)}}$

where n is the number of utility measurements, i is a number identifying a particular outlier being evaluated, t<sub>n−

i−

1,p</sub> is a student'"'"'s t-distribution with (n−

i−

1) degrees of freedom, and p is a value based on the user defined probability α

of incorrectly declaring one or more outliers when no outliers exist.

11. The method as recited in claim 10 wherein the percentile p is determined from:

$p=1-\left(\frac{\mathrm{\alpha <br><br>}}{2<br><br>\left(n-i+1\right)}\right).$

12. A method for determining abnormal consumption of a utility by a system, comprising:

(a) repeatedly measuring the level of use of the utility, thereby producing a plurality of utility measurements;

(b) forming a group of those of the plurality of utility measurements taken during predefined periods of time;

(c) calculating an arithmetic mean ({overscore (x)}) of the group;

(d) finding an extreme utility measurement x_e,i which is the utility measurement having a value that is farthest numerically from the arithmetic mean ({overscore (x)});

(e) using the extreme utility measurement x_e,i to calculate an extreme studentized deviate R_i;

(f) calculating a 100α

percent critical value λ

_i for the extreme utility measurement x_e,i;

(g) declaring the extreme utility measurement x_e,i to be an outlier indicative of abnormal utility use and adding the outlier to a set of outliers when the extreme studentized deviate is greater than the 100α

percent critical value;

(h) removing the extreme utility measurement x_e,i from the group of utility measurements;

(i) repeating steps (c) through (h) a defined number of times;

(j) removing the set of outliers from the group of utility measurements to define a set of non-outliers; and

(k) evaluating performance of the system in response to the set of outliers and the set of non-outliers by determining a severity of abnormal utility usage represented by each outlier, wherein determining a severity of abnormal utility usage for each outlier comprises calculating how far each outlier is from a robust estimate value for utility usage determined from the set of non-outliers.

13. The method as recited in claim 12 wherein the extreme studentized deviate R_i is calculated according to the expression:

$R_i=\frac{<br><br>x_{e,i}-\stackrel{\_}{x}<br><br>}{s}$

where s is a standard deviation of the utility measurements in the group.

14. The method as recited in claim 12 wherein the 100α

percent critical value λ

_i is calculated using the equation;

${\mathrm{\lambda <br><br>}}_i=\frac{\left(n-i\right)<br><br>t_{n-i-1,p}}{\sqrt{\left(n-i+1\right)<br><br>\left(n-i-1+t_{n-i-1,p}^2\right)}}$

where n is the number of utility measurements, i is a number identifying a particular outlier being evaluated, t<sub>n−

i−

1,p</sub> is the student'"'"'s t-distribution with (n−

i−

1) degrees of freedom, and p is a value based on the user defined probability α

of incorrectly declaring one or more outliers when no outliers exist.

15. The method as recited in claim 14 wherein the percentile p is determined from:

$p=1-\frac{\mathrm{\alpha <br><br>}}{2<br><br>\left(n-i+1\right)}.$

16. The method as recited in claim 12 further comprising defining periods of time during a plurality of days in which under normal conditions similar utility consumption levels occur during each one of those periods of time.

17. The method as recited in claim 12 further comprising performing maintenance on the system in response to examination of one or more of the outliers.