Analysis of the Behavior Pattern of Energy Consumption through Online Clustering Techniques
Author:
Viera Juan1, Aguilar Jose1234ORCID, Rodríguez-Moreno Maria15ORCID, Quintero-Gull Carlos6
Affiliation:
1. Escuela Politécnica Superior, ISG—Intelligent Systems Group, Universidad de Alcalá, 28805 Alcalá de Henares, Spain 2. CEMISID—Centro de Estudios en Microprocesadores y Sistemas Digitales, Universidad de Los Andes, Mérida 5101, Venezuela 3. GIDITIC—Grupo de Investigación, Desarrollo e Innovación en Tecnologías de la Información y las Comunicaciones, Universidad EAFIT, Medellín 50022, Colombia 4. IMDEA Networks Institute, Leganés, 28918 Madrid, Spain 5. TNO, Intelligent Autonomous Systems Group (IAS), 2597 AK The Hague, The Netherlands 6. Departamento de Ciencias Aplicadas y Humanísticas, Universidad de Los Andes, Mérida 5101, Venezuela
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.
Funder
European Union’s Horizon 2020 research and innovation programme JCLM Spanish Ministry of Science and Innovation project European Regional Development Fund
Subject
Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction
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