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

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 assessme...

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Bibliographic Details
Published inSensors (Basel, Switzerland) Vol. 20; no. 21; p. 6034
Main Authors Hernandez, J. C., Sanchez-Sutil, F., Cano-Ortega, A., Baier, C. R.
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 23.10.2020
MDPI
Subjects
Accuracy
advanced metering infrastructure
Algorithms
Autocorrelation
Bias
Data compression
Data sampling
Datasets
electric load profile
Electricity
Energy
Households
Load fluctuation
smart meter
Statistical analysis
temporal data granularity
Temporal resolution
time series
time slices
Spain
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Summary: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.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s20216034