In this paper, for the first time, a feature selection (FS) problem for an unattributed-identity multi-target regression (UIMTR) problem is presented. UIMTR is defi?ned as a multi-target regression problem where the set of target and predictor variables are undetermined, i.e., the identity of the variables is unattributed. Two forward selection ?filter-based mutual information sequential-methods are proposed. In particular, the proposed methods are multi-objective adaptations of the classical Mutual Information Maximization (MIM) and Maximum Relevance Minimum Redundancy (mRMR). The concept of “sentinel variable” is also introduced in this paper: any variable selected by the methods that, a posteriori, will be a predictor variable (its real-time data will be used by the models to predict the value of the target variables). To highlight the existence of this type of problems in the industry, and thus the need for this approach, a current problem of low voltage power grids is presented and modelled. In particular, the question of selecting a subset of smart meters (“sentinel smart meters”) that serve as predictors of a certain electrical measurement for the rest of the smart meters in the grid. The empirical approach will be applied to voltage curves of smart meters for six different transformer substations. The results are evaluated from three perspectives: (i) the quality of the predictions, (ii) the stability of the methods and (iii) the execution time. In addition, the results are compared with three other methods, a purely empirical one proposed in the article (based on voltage patterns (VP)) and another two which are well-known in the literature: (a) RReliefF (Relief for regressions) and (b) Fisher Score.

With the advent of new loads and generation on the low voltage grid, voltage fluctuation has increased, especially in active distribution grids with a high penetration of distributed resources and a large deployment of electric vehicles. The coordination of different technologies has emerged as the best way for voltage regulation, among others, smart inverters, open soft points or transformers with on-load regulation capability. This paper proposes a novel way to model the control strategies for the automatic voltage controller of On-Load Tap-Changer transformers. The purpose is to standardize and simplify the way these strategies are represented, in order to facilitate (i) their  election by Distribution System Operators, (ii) their future integration with other systems, and (iii) to increase the ability to anticipate On-Load Tap-Changer behavior. The proposal has been validated using real data, obtaining an accuracy of 99.15% in the tap changer positions. A unified framework is also introduced, which allows the proposed functional representation to be combined with the On-Load Tap-Changer controller behavior estimation. A experimental validation has been carried out where more than 150 000 strategies have been simulated, finally determining the one that best fits the objectives.