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- Data mining techniques for electricity customer characterizationPublication . Ramos, Sérgio Filipe Carvalho; Soares, João; Cembranel, Samuel S.; Tavares, Inês; Foroozandeh, Z.; Vale, Zita; Fernandes, RubipiaraThe liberalization of electricity markets has been resulted in the emergence of new players, increasing the competitiveness in the markets, standing those can provide better services for 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 curves 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 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 used to classify new customers according to their consumption data. The classification model is used to select the correct class for each customer. To make the model simple, each load curve is represented by three indices which represent 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 customers. The energy consumption data can be constantly updated to improve the model precision, finding estimates that can better represent consumers and their consumption habits.
- A Short Review on Data Mining Techniques for Electricity Customers CharacterizationPublication . Cembranel, Samuel S.; Lezama, Fernando; Soares, João; Filipe Ramos, Sérgio; Gomes, Antonio; Vale, ZitaAn important tool to manage electrical systems is the knowledge of customers' consumption patterns. Data Mining (DM) emerges as an important tool for extracting information about energy consumption in databases and identifying consumption patterns. This paper presents a short review on DM, with a focus on the characterization of electricity customers supported on knowledge discovery in database (KDD) process. The study includes several steps: first, few concepts of the KDD process are presented; following, a short review of clustering algorithms is presented including partitional, hierarchical, fuzzy, evolutionary methods, and Self-Organizing Maps; finally, the main concepts and methods for load classification, based on load shape indices are presented. The main objective of this work is to present a short review of DM techniques applied to identify typical load profiles in electrical systems and new customers' classification.