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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8122780/ https://www.ncbi.nlm.nih.gov/pubmed/33922298 http://dx.doi.org/10.3390/s21092946 |
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author | Jurj, Dacian I. Czumbil, Levente Bârgăuan, Bogdan Ceclan, Andrei Polycarpou, Alexis Micu, Dan D. |
author_facet | Jurj, Dacian I. Czumbil, Levente Bârgăuan, Bogdan Ceclan, Andrei Polycarpou, Alexis Micu, Dan D. |
author_sort | Jurj, Dacian I. |
collection | PubMed |
description | 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(2) 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. |
format | Online Article Text |
id | pubmed-8122780 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-81227802021-05-16 Custom Outlier Detection for Electrical Energy Consumption Data Applied in Case of Demand Response in Block of Buildings Jurj, Dacian I. Czumbil, Levente Bârgăuan, Bogdan Ceclan, Andrei Polycarpou, Alexis Micu, Dan D. Sensors (Basel) Article 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(2) 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. MDPI 2021-04-22 /pmc/articles/PMC8122780/ /pubmed/33922298 http://dx.doi.org/10.3390/s21092946 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Jurj, Dacian I. Czumbil, Levente Bârgăuan, Bogdan Ceclan, Andrei Polycarpou, Alexis Micu, Dan D. Custom Outlier Detection for Electrical Energy Consumption Data Applied in Case of Demand Response in Block of Buildings |
title | Custom Outlier Detection for Electrical Energy Consumption Data Applied in Case of Demand Response in Block of Buildings |
title_full | Custom Outlier Detection for Electrical Energy Consumption Data Applied in Case of Demand Response in Block of Buildings |
title_fullStr | Custom Outlier Detection for Electrical Energy Consumption Data Applied in Case of Demand Response in Block of Buildings |
title_full_unstemmed | Custom Outlier Detection for Electrical Energy Consumption Data Applied in Case of Demand Response in Block of Buildings |
title_short | Custom Outlier Detection for Electrical Energy Consumption Data Applied in Case of Demand Response in Block of Buildings |
title_sort | custom outlier detection for electrical energy consumption data applied in case of demand response in block of buildings |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8122780/ https://www.ncbi.nlm.nih.gov/pubmed/33922298 http://dx.doi.org/10.3390/s21092946 |
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