Big Data Analytics for Discovering Electricity Consumption Patterns in Smart Cities

Rubén Pérez-Chacón, José M. Luna-Romera, Alicia Troncoso, Francisco Martínez-Álvarez and José C. Riquelme
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Rubén Pérez-Chacón: Division of Computer Science, Universidad Pablo de Olavide, ES-41013 Seville, Spain
José M. Luna-Romera: Division of Computer Science, University of Sevilla, ES-41012 Seville, Spain
Alicia Troncoso: Division of Computer Science, Universidad Pablo de Olavide, ES-41013 Seville, Spain
Francisco Martínez-Álvarez: Division of Computer Science, Universidad Pablo de Olavide, ES-41013 Seville, Spain
José C. Riquelme: Division of Computer Science, University of Sevilla, ES-41012 Seville, Spain

Energies, 2018, vol. 11, issue 3, 1-19

Abstract: New technologies such as sensor networks have been incorporated into the management of buildings for organizations and cities. Sensor networks have led to an exponential increase in the volume of data available in recent years, which can be used to extract consumption patterns for the purposes of energy and monetary savings. For this reason, new approaches and strategies are needed to analyze information in big data environments. This paper proposes a methodology to extract electric energy consumption patterns in big data time series, so that very valuable conclusions can be made for managers and governments. The methodology is based on the study of four clustering validity indices in their parallelized versions along with the application of a clustering technique. In particular, this work uses a voting system to choose an optimal number of clusters from the results of the indices, as well as the application of the distributed version of the k-means algorithm included in Apache Spark’s Machine Learning Library. The results, using electricity consumption for the years 2011–2017 for eight buildings of a public university, are presented and discussed. In addition, the performance of the proposed methodology is evaluated using synthetic big data, which cab represent thousands of buildings in a smart city. Finally, policies derived from the patterns discovered are proposed to optimize energy usage across the university campus.

Keywords: big data; time series clustering; patterns; smart cities (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: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (23)

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