Custom Outlier Detection for Electrical Energy Consumption Data Applied in Case of Demand Response in Block of Buildings

The aim of this paper is to provide an extended analysis of the outlier detection, using probabilistic and AI techniques, applied in a demo pilot demand response in blocks of buildings project, based on real experiments and energy data collection with detected anomalies. A numerical algorithm was cr...
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Autor*in:	Dacian I. Jurj [verfasserIn] Levente Czumbil [verfasserIn] Bogdan Bârgăuan [verfasserIn] Andrei Ceclan [verfasserIn] Alexis Polycarpou [verfasserIn] Dan D. Micu [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	data cleaning demand response baseline electricity consumption outliers local outlier factor (LOF) interquartile range (IQR)

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 21(2021), 9, p 2946
Übergeordnetes Werk:	volume:21 ; year:2021 ; number:9, p 2946

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s21092946

Katalog-ID:	DOAJ085569291

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callnumber-first	T - Technology
author	Dacian I. Jurj
spellingShingle	Dacian I. Jurj misc TP1-1185 misc data cleaning misc demand response misc baseline electricity consumption misc outliers misc local outlier factor (LOF) misc interquartile range (IQR) misc Chemical technology Custom Outlier Detection for Electrical Energy Consumption Data Applied in Case of Demand Response in Block of Buildings
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title	Custom Outlier Detection for Electrical Energy Consumption Data Applied in Case of Demand Response in Block of Buildings
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title_sort	custom outlier detection for electrical energy consumption data applied in case of demand response in block of buildings
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title_auth	Custom Outlier Detection for Electrical Energy Consumption Data Applied in Case of Demand Response in Block of Buildings
abstract	The aim of this paper is to provide an extended analysis of the outlier detection, using probabilistic and AI techniques, applied in a demo pilot demand response in blocks of buildings project, based on real experiments and energy data collection with detected anomalies. A numerical algorithm was created to differentiate between natural energy peaks and outliers, so as to first apply a data cleaning. Then, a calculation of the impact in the energy baseline for the demand response computation was implemented, with improved precision, as related to other referenced methods and to the original data processing. For the demo pilot project implemented in the Technical University of Cluj-Napoca block of buildings, without the energy baseline data cleaning, in some cases it was impossible to compute the established key performance indicators (peak power reduction, energy savings, cost savings, CO<sub<2</sub< emissions reduction) or the resulted values were far much higher (<50%) and not realistic. Therefore, in real case business models, it is crucial to use outlier’s removal. In the past years, both companies and academic communities pulled their efforts in generating input that consist in new abstractions, interfaces, approaches for scalability, and crowdsourcing techniques. Quantitative and qualitative methods were created with the scope of error reduction and were covered in multiple surveys and overviews to cope with outlier detection.
abstractGer	The aim of this paper is to provide an extended analysis of the outlier detection, using probabilistic and AI techniques, applied in a demo pilot demand response in blocks of buildings project, based on real experiments and energy data collection with detected anomalies. A numerical algorithm was created to differentiate between natural energy peaks and outliers, so as to first apply a data cleaning. Then, a calculation of the impact in the energy baseline for the demand response computation was implemented, with improved precision, as related to other referenced methods and to the original data processing. For the demo pilot project implemented in the Technical University of Cluj-Napoca block of buildings, without the energy baseline data cleaning, in some cases it was impossible to compute the established key performance indicators (peak power reduction, energy savings, cost savings, CO<sub<2</sub< emissions reduction) or the resulted values were far much higher (<50%) and not realistic. Therefore, in real case business models, it is crucial to use outlier’s removal. In the past years, both companies and academic communities pulled their efforts in generating input that consist in new abstractions, interfaces, approaches for scalability, and crowdsourcing techniques. Quantitative and qualitative methods were created with the scope of error reduction and were covered in multiple surveys and overviews to cope with outlier detection.
abstract_unstemmed	The aim of this paper is to provide an extended analysis of the outlier detection, using probabilistic and AI techniques, applied in a demo pilot demand response in blocks of buildings project, based on real experiments and energy data collection with detected anomalies. A numerical algorithm was created to differentiate between natural energy peaks and outliers, so as to first apply a data cleaning. Then, a calculation of the impact in the energy baseline for the demand response computation was implemented, with improved precision, as related to other referenced methods and to the original data processing. For the demo pilot project implemented in the Technical University of Cluj-Napoca block of buildings, without the energy baseline data cleaning, in some cases it was impossible to compute the established key performance indicators (peak power reduction, energy savings, cost savings, CO<sub<2</sub< emissions reduction) or the resulted values were far much higher (<50%) and not realistic. Therefore, in real case business models, it is crucial to use outlier’s removal. In the past years, both companies and academic communities pulled their efforts in generating input that consist in new abstractions, interfaces, approaches for scalability, and crowdsourcing techniques. Quantitative and qualitative methods were created with the scope of error reduction and were covered in multiple surveys and overviews to cope with outlier detection.
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title_short	Custom Outlier Detection for Electrical Energy Consumption Data Applied in Case of Demand Response in Block of Buildings
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author2	Levente Czumbil Bogdan Bârgăuan Andrei Ceclan Alexis Polycarpou Dan D. Micu
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