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Open AccessArticle

Influence of Data Sampling Frequency on Household Consumption Load Profile Features: A Case Study in Spain

1
Center for Advanced Studies in Earth Sciences, Energy and Environment, University of Jaén, 23071 Jaén, Spain
2
Department of Electrical Engineering, University of Jaen, 23071 Jaen, Spain
3
Department of Electrical Engineering, University of Talca, Camino los Niches Km. 1, Curicó 3344158, Chile
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(21), 6034; https://doi.org/10.3390/s20216034
Received: 13 September 2020 / Revised: 19 October 2020 / Accepted: 20 October 2020 / Published: 23 October 2020
(This article belongs to the Special Issue Smart IoT System for Renewable Energy Resource)
Smart meter (SM) deployment in the residential context provides a vast amount of data of high granularity at the individual household level. In this context, the choice of temporal resolution for describing household load profile features has a crucial impact on the results of any action or assessment. This study presents a methodology that makes two new contributions. Firstly, it proposes periodograms along with autocorrelation and partial autocorrelation analyses and an empirical distribution-based statistical analysis, which are able to describe household consumption profile features with greater accuracy. Secondly, it proposes a framework for data collection in households at a high sampling frequency. This methodology is able to analyze the influence of data granularity on the description of household consumption profile features. Its effectiveness was confirmed in a case study of four households in Spain. The results indicate that high-resolution data should be used to consider the full range of consumption load fluctuations. Nonetheless, the accuracy of these features was found to largely depend on the load profile analyzed. Indeed, in some households, accurate descriptions were obtained with coarse-grained data. In any case, an intermediate data-resolution of 5 s showed feature characterization closer to those of 0.5 s. View Full-Text
Keywords: smart meter; temporal data granularity; electric load profile; time slices; time series; advanced metering infrastructure smart meter; temporal data granularity; electric load profile; time slices; time series; advanced metering infrastructure
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MDPI and ACS Style

Hernandez, J.C.; Sanchez-Sutil, F.; Cano-Ortega, A.; Baier, C.R. Influence of Data Sampling Frequency on Household Consumption Load Profile Features: A Case Study in Spain. Sensors 2020, 20, 6034. https://doi.org/10.3390/s20216034

AMA Style

Hernandez JC, Sanchez-Sutil F, Cano-Ortega A, Baier CR. Influence of Data Sampling Frequency on Household Consumption Load Profile Features: A Case Study in Spain. Sensors. 2020; 20(21):6034. https://doi.org/10.3390/s20216034

Chicago/Turabian Style

Hernandez, J. C.; Sanchez-Sutil, F.; Cano-Ortega, A.; Baier, C. R. 2020. "Influence of Data Sampling Frequency on Household Consumption Load Profile Features: A Case Study in Spain" Sensors 20, no. 21: 6034. https://doi.org/10.3390/s20216034

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