Algorithms that transfer between different energy metering methods for simplification of energy trading and unified billing.

The paper settles a discrepancy between two smart-metering methods. The issue bears on a billion installed smart meters and $36B electric-energy trading market. The contemporary problem of the energy-metering registration gap between the methods, relates to energy-trading unified-billing, and it will aggravate in the future.

TNT Loss: A Technical and Nontechnical Generative Cooperative Energy Loss Detection System.

This paper describes an electricity technical/nontechnical loss detection method capable of loss type identification, classification, and location. Several technologies are implemented to obtain that goal: (i) an architecture of three generative cooperative AI modules and two additional non-cooperative AI modules for data knowledge sharing is proposed, (ii) new expert consumption-based knowledge of feature collaboration of the entire consumption data are embedded as features in an AI classification algorithm, and (iii) an anomaly pooling mechanism that enables one-to-one mapping of signatures to loss types is proposed. A major objective of the paper is an explanation of how an exact loss type to signature mapping is obtained simply and rapidly, (iv) the role of the reactive energy load profile for enhancing signatures for loss types is exemplified, (v) a mathematical demonstration of the quantitative relationship between the features space to algorithm performance is obtained generically for any algorithm, and (vi) a theory of "generative cooperative modules" for technical/nontechnical loss detection is located and mapped to the presented system.