Various embodiments of the present disclosure can include systems, methods, and non-transitory computer readable media configured to select a set of signals relating to a plurality of energy usage conditions. Signal values for the set of signals can be determined. Machine learning can be applied to the signal values to identify energy usage conditions associated with non-technical loss.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer-implemented method comprising: selecting, by a computing system, a set of signals relating to a plurality of energy usage conditions; determining, by the computing system, signal values for the set of signals; and applying, by the computing system, machine learning to the signal values to identify energy usage conditions associated with non-technical loss. 2. The computer-implemented method of claim 1, further comprising: generating a plurality of N-dimensional representations for the plurality of energy usage conditions, the plurality of N-dimensional representations generated based on the signal values; wherein the applying machine learning includes applying at least one machine learning algorithm to the plurality of N-dimensional representations to produce a classifier model for identifying non-technical loss. 3. The computer-implemented method of claim 2, wherein at least a first portion of the plurality of N-dimensional representations has been previously recognized as corresponding to non-technical loss, and wherein at least a second portion of the plurality of N-dimensional representations has been previously recognized as corresponding to normal energy usage. 4. The computer-implemented method of claim 3, wherein the at least one machine learning algorithm includes a supervised process that classifies at least a third portion of the plurality of N-dimensional representations within an allowable N-dimensional proximity from the first portion as corresponding to non-technical loss and that classifies at least a fourth portion of the plurality of N-dimensional representations within the allowable N-dimensional proximity from the second portion as corresponding to normal energy usage. 5. The computer-implemented method of claim 2, further comprising: receiving new signals values for the set of signals, the new signal values being associated with a particular energy usage condition; generating a new N-dimensional representation for the particular energy usage condition based on the new signal values; and classifying the new N-dimensional representation based on the classifier model. 6. The computer-implemented method of claim 5, further comprising: applying the at least one machine learning algorithm to the new N-dimensional representation to modify the classifier model. 7. The computer-implemented method of claim 6, further comprising: identifying the new N-dimensional representation as corresponding to non-technical loss; reporting the non-technical loss to an energy provider associated with the particular energy usage condition. 8. The computer-implemented method of claim 7, further comprising: acquiring, from the energy provider, at least one of a confirmation or a non-confirmation that the particular energy usage condition is associated with the non-technical loss. 9. The computer-implemented method of claim 8, further comprising: modifying the classifier model based on the at least one of the confirmation or the non-confirmation. 10. The computer-implemented method of claim 2, wherein the at least one machine learning algorithm is associated with at least one of a support vector machine, a boosted decision tree, a classification tree, a regression tree, a bagging tree, a random forest, a neural network, or a rotational forest. 11. The computer-implemented method of claim 2, further comprising: identifying a plurality of meters that have likelihoods of being associated with the non-technical loss; and ranking the plurality of meters based on the likelihoods of being associated with the non-technical loss. 12. The computer-implemented method of claim 11, further comprising: determining that at least some of the plurality of meters meet specified ranking threshold criteria; and identifying the at least some of the plurality of utility meters as candidates for investigation. 13. The computer-implemented method of claim 2, wherein one or more signals in the set of signals are associated with at least one of an account attribute signal category, an anomalous load signal category, a calculated status signal category, a current analysis signal category, a missing data signal category, a disconnected signal category, a meter event signal category, a monthly meter anomalous load signal category, a monthly meter consumption on inactive signal category, an outage signal category, a stolen meter signal category, an unusual production signal category, a work order signal category, or a zero reads signal category. 14. The computer-implemented method of claim 2, further comprising: acquiring a set of formulas for the set of signals, each formula in the set of formulas corresponding to a respective signal in the set of signals; and calculating the signal values for the set of signals based on the set of formulas. 15. The computer-implemented method of claim 2, wherein at least some signal values are derived from data acquired from a plurality of meters associated with the plurality of energy usage conditions. 16. The computer-implemented method of claim 2, wherein a first signal in the set of signals is generated based on a modification to a second signal in the set of signals. 17. The computer-implemented method of claim 1, further comprising receiving from an energy provider at least one signal, not included in the set of signals, relating to energy usage conditions to identify non-technical loss. 18. The computer-implemented method of claim 2, wherein the at least one machine learning algorithm includes an unsupervised process, and wherein the unsupervised process utilizes unclassified data for identifying non-technical loss. 19. A system comprising: at least one processor; and a memory storing instructions that, when executed by the at least one processor, cause the system to perform: selecting a set of signals relating to a plurality of energy usage conditions; determining signal values for the set of signals; and applying machine learning to the signal values to identify energy usage conditions associated with non-technical loss. 20. A non-transitory computer-readable storage medium including instructions that, when executed by at least one processor of a computing system, cause the computing system to perform: selecting a set of signals relating to a plurality of energy usage conditions; determining signal values for the set of signals; and applying machine learning to the signal values to identify energy usage conditions associated with non-technical loss.