Jean SUMAILI AKILIMALI

Jean SUMAILI AKILIMALI

Instituto de Engenharia de Sistemas

e Computadores do Porto, INESC Porto

Campus da FEUP, Rua Dr Roberto Frias 378,

4200 – 465, Porto, Portugal.

Email: jsa@inescport.pt; jbsumaili@yahoo.fr

BIOGRAPHIE SOMMAIRE

Jean Sumaili Akilimali est né le 21 juillet 1972 à Goma en République Démocratique du Congo. Après de brillantes études secondaires chez les Salésiens à l’Institut Technique Industriel de Goma (ITIG Don Bosco), il décroche son diplôme d’état en Electricité Industrielle. En 1990, il s’inscrit à la Faculté Polytechnique de l’Université de Kinshasa dont il est Gradué en Sciences Appliqués – Option : Electricité (1998).

Il est Docteur en Génie Electrique (2008) du “Politecnico di Torino” où il a reçu son diplôme d’Ingénieur “Laurea in Ingegneria Elettrica” en 2004 et son habilitation à l’exercice de la profession d’Ingénieur (2005). Il a également travaillé comme come chargé des travaux pratiques pour les cours de “Systèmes Electriques Industriels”, “Transmission de l’Energie Electrique”, “Installations Electriques” (2006 – 2009) et comme “Research Assistant” (janvier 2008 – mars 2009).

Dr Sumaili travaille actuellement comme “Senior Researcher” à l’Instituto de Engenharia de Sistemas e Computadores do Porto, INESC Porto – Porto (Portugal). Son domaine de recherche comprend l’analyse des réseaux de distribution, les applications de la génération distribuée, les installations solaires photovoltaïques et la classification des consommateurs.

 PUBLICATIONS

Journal papers

G. Chicco and J. Sumaili Akilimali,

“Renyi entropy-based classification of daily electrical load patterns"

IET Proceedings : Generation, Transmission and Distribution, in Press.

Abstract : This paper illustrates and discusses an original approach to classify the electricity consumers according to their daily load patterns. This approach exploits the notion of entropy introduced by Renyi for setting up specific clustering procedures. The proposed procedures differ with respect to typical methods adopted for electricity consumer classification, based on the Euclidean distance notion. The algorithms tested include firstly a classical method based on the between-cluster entropy and its slight variation. Then, a novel procedure is presented, based on the calculation of the similarity between centroids, with successive refinement to allow effective identification of the outliers. The outcomes of the classification carried out by using the proposed procedure are compared to the results of other available techniques, using a set of clustering validity indicators for ranking the clustering methods. On the basis of these results, it emerges that the novel procedure exhibits better clustering performance with respect to both the literature approaches and the classical entropy-based method, for different numbers of clusters. The results obtained are of key relevance for assisting the electricity suppliers in identifying a reduced number of load pattern-dependent classes, to be associated with distinct consumer groups for load aggregation or tariff purposes.

Keywords: clustering, clustering validity indicator, classification, electricity consumer, load pattern, load profile, outlier, Renyi entropy, similarity.