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Article

Outlier Detection in Buildings’ Power Consumption Data Using Forecast Error

1
Univ. Grenoble Alpes, CNRS, Grenoble INP, G2Elab, F-38000 Grenoble, France
2
Department of Electrical and Electronic Engineering, Universidade Federal de Santa Catarina, Florianópolis 88040-900, Brazil
*
Author to whom correspondence should be addressed.
Academic Editor: Grigore Stamatescu
Energies 2021, 14(24), 8325; https://doi.org/10.3390/en14248325
Received: 28 November 2021 / Revised: 7 December 2021 / Accepted: 8 December 2021 / Published: 10 December 2021
(This article belongs to the Special Issue Energy Management for Smart Buildings)
Buildings play a central role in energy transition, as they were responsible for 67.8% of the total consumption of electricity in France in 2017. Because of that, detecting anomalies (outliers) is crucial in order to identify both potential opportunities to reduce energy consumption and malfunctioning of the metering system. This work aims to compare the performance of several outlier detection methods, such as classical statistical methods (as boxplots) applied to the actual measurements and to the difference between the measurements and their predictions, in the task of detecting outliers in the power consumption data of a tertiary building located in France. The results show that the combination of a regression method, such as random forest, and the adjusted boxplot outlier detection method have promising potential in detecting this type of data quality problem in electricity consumption. View Full-Text
Keywords: data quality; forecast error; outlier detection; power consumption; tertiary buildings data quality; forecast error; outlier detection; power consumption; tertiary buildings
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MDPI and ACS Style

Martin Nascimento, G.F.; Wurtz, F.; Kuo-Peng, P.; Delinchant, B.; Jhoe Batistela, N. Outlier Detection in Buildings’ Power Consumption Data Using Forecast Error. Energies 2021, 14, 8325. https://doi.org/10.3390/en14248325

AMA Style

Martin Nascimento GF, Wurtz F, Kuo-Peng P, Delinchant B, Jhoe Batistela N. Outlier Detection in Buildings’ Power Consumption Data Using Forecast Error. Energies. 2021; 14(24):8325. https://doi.org/10.3390/en14248325

Chicago/Turabian Style

Martin Nascimento, Gustavo Felipe, Frédéric Wurtz, Patrick Kuo-Peng, Benoit Delinchant, and Nelson Jhoe Batistela. 2021. "Outlier Detection in Buildings’ Power Consumption Data Using Forecast Error" Energies 14, no. 24: 8325. https://doi.org/10.3390/en14248325

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