Analysis of the Behavior Pattern of Energy Consumption through Online Clustering Techniques

Juan Viera, Jose Aguilar (), Maria Rodríguez-Moreno and Carlos Quintero-Gull
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Juan Viera: Escuela Politécnica Superior, ISG—Intelligent Systems Group, Universidad de Alcalá, 28805 Alcalá de Henares, Spain
Jose Aguilar: Escuela Politécnica Superior, ISG—Intelligent Systems Group, Universidad de Alcalá, 28805 Alcalá de Henares, Spain
Maria Rodríguez-Moreno: Escuela Politécnica Superior, ISG—Intelligent Systems Group, Universidad de Alcalá, 28805 Alcalá de Henares, Spain
Carlos Quintero-Gull: Departamento de Ciencias Aplicadas y Humanísticas, Universidad de Los Andes, Mérida 5101, Venezuela

Energies, 2023, vol. 16, issue 4, 1-17

Abstract: Analyzing energy consumption is currently of great interest to define efficient energy management strategies. In particular, studying the evolution of the behavior of the consumption pattern can allow energy policies to be defined according to the time of the year. In this sense, this work proposes to study the evolution of energy behavior patterns using online clustering techniques. In particular, the centroids of the groups constructed by the techniques will represent their consumption patterns. Specifically, two unsupervised online machine learning techniques ideal for the stated objective will be analyzed, X-Means and LAMDA, since they are capable of varying and adapting the number of clusters at runtime. These techniques are applied to energy consumption data in commercial buildings, making groupings on previous groups, in our case, monthly and quarterly. We compared their performance by analyzing the evolution of the patterns over time. The results are very promising since the quality of the consumption patterns obtained is very good according to the performance metrics. Thus, the three main contributions of this article are to propose an approach to determine energy consumption patterns using online non-supervised learning approaches, a methodology to analyze and explain the evolution of energy consumption using centroids of clusters, and a comparison strategy of online learning techniques. The online clustering techniques have qualities of the order of 0.59 and 0.41 for Silhouette and Davies-Boulding, respectively, for X-Means and of the order of 0.71 and 0.24 for Silhouette and Davies-Boulding, respectively, for LAMDA in different datasets of energy. The results are motivating since very good results are obtained in terms of the quality of the clusters, particularly with LAMDA; therefore, analyzing its centroids as the patterns of user behaviors makes a lot of sense.

Keywords: online clustering techniques; energy consumption; LAMDA; X-means; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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