Data mining techniques for electricity customer characterization

The liberalization of electricity markets has been resulted in the emergence of new players, increasing the competitiveness in the electricity sector, aiming to provide better services and better prices. The knowledge of energy consumers’ profile has been an important tool to help players to make decisions in the electrical sectors. In this paper, a characterization model of typical load profiles for Low Voltage (LV) customers is proposed and evaluated. The identification of consumption patterns is based on clustering analysis. The clustering methodology is based on seven clustering algorithms (partitional and hierarchical). Also, five clustering validity indices are used to identify the best data partition. With the knowledge obtained in clustering analysis, a classification model is implemented in order to classify new customers according to their consumption data. The classification model is used to select the correct class for each customer. To simplify the classification model, each load curve is represented by three indices which represent the load curves shape. The methodology used in this work demonstrates to be an effective tool and can be used in most diverse sectors, highlighting the use of knowledge in the optimization of the energy contracting for low voltage consumers. The energy consumption data can be constantly updated to improve the model precision, as well to better represent consumers and their consumption habits.This work has received funding from FEDER Funds through COMPETE program and from184 National Funds through FCT under the project BENEFICE–PTDC/EEI-EEE/29070/2017 and185 UIDB/00760/2020 under CEECIND/02814/2017 grant