US 6,816,811 B2 · Method of intelligent data analysis to detect abnormal use of utilities in buildings

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.

Claims17

Dependent claims

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 more elements

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 xe,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 xe,i to calculate an extreme studentized deviate Ri;
(f) calculating a 100α percent critical value λi for the extreme utility measurement xe,i;
(g) declaring the extreme utility measurement xe,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 xe,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.

9 more elements

Dependencies are inferred from the claim text.

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

Life of this patent

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

Life of this patent

Abstract

Claims17