Automated Detection of Electric Energy Consumption Load Profile Patterns

Ignacio Benítez and José-Luis Díez
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Ignacio Benítez: Sustainability and Energy Efficiency Area, Fundación Valenciaport, Building III, Avda. Muelle del Túria s/n, 46023 Valencia, Spain
José-Luis Díez: Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, Camino de Vera s/n, 46023 Valencia, Spain

Energies, 2022, vol. 15, issue 6, 1-26

Abstract: Load profiles of energy consumption from smart meters are becoming more and more available, and the amount of data to analyse is huge. In order to automate this analysis, the application of state-of-the-art data mining techniques for time series analysis is reviewed. In particular, the use of dynamic clustering techniques to obtain and visualise temporal patterns characterising the users of electrical energy is deeply studied. The performed review can be used as a guide for those interested in the automatic analysis and groups of behaviour detection within load profile databases. Additionally, a selection of dynamic clustering algorithms have been implemented and the performances compared using an available electric energy consumption load profile database. The results allow experts to easily evaluate how users consume energy, to assess trends and to predict future scenarios.

Keywords: time series analysis; dynamic clustering; user load profiles (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: 2022
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