Data mining techniques to support the classification of MV electricity customers

This paper describes a methodology that was developed for the classification of Medium Voltage (MV) electricity customers. Starting from a sample of data bases, resulting from a monitoring campaign, Data Mining (DM) techniques are used in order to discover a set of a MV consumer typical load profile and, therefore, to extract knowledge regarding to the electric energy consumption patterns. In first stage, it was applied several hierarchical clustering algorithms and compared the clustering performance among them using adequacy measures. In second stage, a classification model was developed in order to allow classifying new consumers in one of the obtained clusters that had resulted from the previously process. Finally, the interpretation of the discovered knowledge are presented and discussed.

Electrochemical study of butylate: application to the analysis of water

The electroactivity of butylate (BTL) is studied by cyclic voltammetry (CV) and square wave voltammetry (SWV) at a glassy carbon electrode (GCE) and a hanging mercury drop electrode (HMDE). Britton–Robinson buffer solutions of pH 1.9–11.5 are used as supporting electrolyte. CV voltammograms using GCE show a single anodic peak regarding the oxidation of BTL at +1.7V versus AgCl/ Ag, an irreversible process controlled by diffusion. Using a HMDE, a single cathodic peak is observed, at 1.0V versus AgCl/Ag. The reduction of BTL is irreversible and controlled by adsorption. Mechanism proposals are presented for these redox transformations. Optimisation is carried out univaryingly. Linearity ranges were 0.10–0.50 mmol L-1 and 2.0–9.0 µmolL-1 for anodic and cathodic peaks, respectively. The proposed method is applied to the determination of BTL in waters. Analytical results compare well with those obtained by an HPLC method.