A hybrid neural network model and encoding technique for enhanced classification of energy consumption data

Total losses in transmission and distribution (T&D) of electrical energy including nontechnical losses (NTL) are huge and are affecting the good interest of utility company and its customers. In this context, importance of customer load profile evaluation for detection of illegal consumers is explained in this paper. Classification of the customers based on load profile evaluation using SVMLIB requires us to choose training function and related parameters. Selecting these parameters would consume a lot of time and is not suggestible evaluation of real time electricity consumption patterns, as, the suspicious profiles are to be predicted instantly. In light of this issue, this paper implements a neural network (NN) model and suggests a hierarchical model for enhanced estimation of the classification efficiency, if that data was classified using support vector machines (SVM). In addition, this paper proposes an encoding technique that can identify illegal consumers with better efficiency and faster classification of data.