Market-adaptive detection of plug-in electric vehicle charging using whole house energy metering data
First Claim
Patent Images
1. A computer-implemented method for detecting plug-in electrical vehicle (PEV) charging at a whole house electrical meter location, comprising the steps of:
- providing a plurality of whole house electrical meters, wherein each whole house electrical meter is located at a particular whole house electrical meter location, and provides input data regarding that particular whole house electrical meter location;
retrieving the input data from a tangible computer memory, said input data including (i) whole house meter interval data for a single whole house meter location to be tested and (ii) whole house meter interval data for a superset of whole house meter locations;
calculating time-referenced summary statistics comprising one or both of the means and standard deviations for the whole house meter interval data for the superset;
selecting a time period for testing a location for PEV charging;
generating a SINGLE HOME MATRIX which is a matrix of whole house meter interval data to be tested;
generating one or both of;
a MEAN MATRIX which is a matrix of corresponding interval means from the superset, anda STANDARD DEVIATION MATRIX which is a matrix of corresponding interval standard deviations from the superset;
normalizing the SINGLE HOME MATRIX using one or both of the MEAN MATRIX and the STANDARD DEVIATION MATRIX to generate a NORMALIZED MATRIX;
applying a binary hypothesis test to the NORMALIZED MATRIX to obtain a determination of whether PEV charging is present at the single whole house meter location;
andstoring a record of the determination in tangible computer memory.
1 Assignment
0 Petitions
Accused Products
Abstract
The invention provides improved methods for detecting the presence of a plug-in electric vehicle (PEV) at a location based on interval measurements from whole house electrical meters. This methodology is applicable to multiple detection algorithms and has been validated using actual PEV customer data.
-
Citations
31 Claims
-
1. A computer-implemented method for detecting plug-in electrical vehicle (PEV) charging at a whole house electrical meter location, comprising the steps of:
-
providing a plurality of whole house electrical meters, wherein each whole house electrical meter is located at a particular whole house electrical meter location, and provides input data regarding that particular whole house electrical meter location; retrieving the input data from a tangible computer memory, said input data including (i) whole house meter interval data for a single whole house meter location to be tested and (ii) whole house meter interval data for a superset of whole house meter locations; calculating time-referenced summary statistics comprising one or both of the means and standard deviations for the whole house meter interval data for the superset; selecting a time period for testing a location for PEV charging; generating a SINGLE HOME MATRIX which is a matrix of whole house meter interval data to be tested; generating one or both of; a MEAN MATRIX which is a matrix of corresponding interval means from the superset, and a STANDARD DEVIATION MATRIX which is a matrix of corresponding interval standard deviations from the superset; normalizing the SINGLE HOME MATRIX using one or both of the MEAN MATRIX and the STANDARD DEVIATION MATRIX to generate a NORMALIZED MATRIX; applying a binary hypothesis test to the NORMALIZED MATRIX to obtain a determination of whether PEV charging is present at the single whole house meter location; and storing a record of the determination in tangible computer memory. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
-
-
12. A computer-implemented method for detecting plugin electrical vehicle (PEV) charging at a whole house electrical meter location using a Support Vector Machine (SVM) algorithm, comprising the steps of:
-
providing a plurality of whole house electrical meters, wherein each whole house electrical meter is located at a particular whole house electrical meter location, and provides input data regarding that particular whole house electrical meter location; retrieving the input data from a tangible computer memory, said input data including (i) whole house meter interval data for a single whole house meter location to be tested and (ii) whole house meter interval data for a superset of whole house meter locations; calculating time-referenced summary statistics comprising the means for the whole house meter interval data for the superset; selecting a time period for testing a location for PEV charging;
generating a SINGLE HOME MATRIX which is a matrix of whole house meter interval data to be tested (x);generating a TEMPORAL SEGMENTATION MATRIX which is a matrix of corresponding interval means from the superset (n); selecting a NOMINAL DAY MODEL MATRIX of whole house meter interval data from a set of parameterized matrices with no PEV charging based on what matrix has the smallest norm (i.e. Euclidean/L2 norm) of difference with respect to the TEMPORAL SEGMENTATION MATRIX (so NOMINAL DAY MODEL MATRIX* H=TEMPORAL SEGMENTATION MATRIX, but for an infinite set of matrices, NOMINAL DAY MODEL MATRIX=\i=TEMPORAL SEGMENTATION MATRIX); generating a PEV DAY MODEL MATRIX of whole house meter interval data that is the element-wise sum of the NOMINAL DAY MODEL MATRIX plus a predetermined sequence of PEV charging load that is typical for the time period selected (n+Xi);
calculating the norm for the difference of SINGLE HOME MATRIX minus NOMINAL DAY MODELMATRIX (i.e. Euclidian/L2 norm) and then taking the reciprocal, assigned to NULL (=1/∥
x−
\i∥
);calculating the norm for the difference of SINGLE HOME MATRIX minus PEV DAY MODELMATRIX (i.e. Euclidian/L2 norm) and then taking the reciprocal, assigned to ALTERNATE (=1/1|x−
(n+Xi)∥
=1/∥
x−
n−
Xi∥
);
dividing ALTERNATE by NULL to obtain a RATIO value;
making the determination that if the RATIO value is greater than a preselected calibration parameter, then a PEV is detected, else no PEV is detected for the selected single whole house meter location during the time period being tested; and
storing a record of the determination in tangible computer memory.- View Dependent Claims (13, 14, 15, 16, 17, 18, 19, 20)
-
-
21. A computer-implemented method for detecting plugin electrical vehicle (PEV) charging at a whole house electrical meter location using a Likelihood Ration Test (LRT) algorithm, comprising the steps of:
-
providing a plurality of whole house electrical meters, wherein each whole house electrical meter is located at a particular whole house electrical meter location, and provides input data regarding that particular whole house electrical meter location; retrieving the input data from a tangible computer memory, said input data including (i) whole house meter interval data for a single whole house meter location to be tested and (ii) whole house meter interval data for a superset of whole house meter locations; calculating time-referenced summary statistics comprising the means and standard deviations for the whole house meter interval data for the superset;
selecting a time period for testing a location for PEV charging;
generating a SINGLE HOME MATRIX which is a matrix of whole house meter interval data to be tested (x);generating one or both of; a MEAN MATRIX which is a matrix of corresponding interval means from the superset (\i), and a STANDARD DEVIATION MATRIX which is a matrix of corresponding interval standard deviations from the superset (a); generating a TEMPORAL SEGMENTATION MATRIX which is a concatenated matrix of MEAN MATRIX and STANDARD DEVIATION MATRIX [\i, a]; selecting a joint probability density/mass function from a set of parameterized functions for no PEV charging (null function) based on which function'"'"'s corresponding matrix of concatenated means and standard deviations has the smallest norm (i.e. Euclidean/L2 norm) of difference with respect TEMPORAL SEGMENTATION MATRIX; generating a joint probability density/mass function for a PEV charging (alternate function) by adding a mean and standard deviation adjustment to the null function which could be as simple as a constant mean offset by charge rate R or a time-weighted offset for mean and standard deviation; calculating the alternate function value for the SINGLE HOME MATRIX and assign to ALTERNATE HYPOTHESIS; dividing the ALTERNATE HYPOTHESIS by the NULL HYPOTHESIS to obtain a RATIO value; and making the determination that if the RATIO value is greater than a preselected calibration parameter, then a PEV is detected, else no PEV is detected for the selected whole house meter location during the time period being tested; and storing a record of the determination in tangible computer memory. - View Dependent Claims (22, 23, 24, 25, 26, 27, 28, 29, 30, 31)
-
Specification