Patent Document ID: 9535917
Application ID: 13631298
Patent Status: 1

Claim One:
1. A system of anomalous utility usage detection, comprising: a memory or other storage device configured to store a subject set of utility usage data; and a processor coupled to the memory or other storage device and configured to: analyze a set of historical utility usage data associated with one or more readings over a particular window of time and derive a plurality of components from the set of historical utility usage data, wherein each of the plurality of components represents a characteristic of the set of historical utility usage data; select a subset of the plurality of components to use as a set of significant components, wherein the plurality of components is associated with a corresponding plurality of significance values, wherein the subset of the plurality of components is selected based at least in part on the corresponding plurality of significance values, wherein the set of significant components is selected to represent normal utility usage data; determine with respect to the subject set of utility usage data a portion that is not associated with the set of significant components; determine that the portion that is not associated with the set of significant components exceeds a prescribed threshold, wherein the portion that is not associated with the set of significant components corresponds to a difference between the subject set of utility usage data and a projection of the subject set of utility usage data onto a subspace spanned by the set of significant components; and conclude, based at least in part on the determination that the portion that is not associated with the set of significant components exceeds the prescribed threshold, that the subject set of utility usage data is anomalous; and wherein, the plurality of components is decomposed from the set of historical utility usage data using a principal component analysis that comprises: determining one or more eigenvectors associated with a training matrix generated using the set of historical usage data; determining one or more eigenvalues respectively corresponding to the one or more eigenvectors; and selecting the set of significant components based at least in part on the one or more eigenvalues respectively corresponding to the one or more eigenvectors.