Applicability of the multifactor dimensionality reduction methodology to the analysis of variables in sanitary buildings
In order to optimise engineering activities, it is necessary to analyse a large amount of data and variables. The objective is to implement MDR to better address the study and propose improvements to reduce energy consumption in Extremadura's health centres. These numerous variables do not have...
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| Format: | Article |
| Language: | English |
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Universidad Politécnica de Madrid
2024-03-01
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| Series: | Anales de Edificación |
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| Online Access: | https://polired.upm.es/index.php/anales_de_edificacion/article/view/5307 |
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| author | Alejandro Prieto-Fernández Álvaro Carmona-Baltasar Jaime Gónzalez-Domínguez Manuel Botejara-Domínguez Gonzalo Sánchez-Barroso Justo García-Sanz-Calcedo |
| author_facet | Alejandro Prieto-Fernández Álvaro Carmona-Baltasar Jaime Gónzalez-Domínguez Manuel Botejara-Domínguez Gonzalo Sánchez-Barroso Justo García-Sanz-Calcedo |
| author_sort | Alejandro Prieto-Fernández |
| collection | DOAJ |
| description | In order to optimise engineering activities, it is necessary to analyse a large amount of data and variables. The objective is to implement MDR to better address the study and propose improvements to reduce energy consumption in Extremadura's health centres. These numerous variables do not have direct and quantifiable interactions on energy consumption. To overcome this drawback, it is possible to apply the multifactor dimensionality reduction (MDR) method. MDR uses Machine Learning to search for the best combinations of variables. This makes it possible to create a model that simplifies the analysis of the data studied. From the whole set, the combination of variables that best describes the study is selected, grouping them into high or low risk. It has been proven that in this way it is possible to better understand and optimise engineering activities. MDR can be used in numerous engineering analyses: energy consumption, equipment maintenance, waste generation, etc. |
| format | Article |
| id | doaj-art-01c2b11772824be38458b7a4448fe5e5 |
| institution | Kabale University |
| issn | 2444-1309 |
| language | English |
| publishDate | 2024-03-01 |
| publisher | Universidad Politécnica de Madrid |
| record_format | Article |
| series | Anales de Edificación |
| spelling | doaj-art-01c2b11772824be38458b7a4448fe5e52025-02-05T10:55:14ZengUniversidad Politécnica de MadridAnales de Edificación2444-13092024-03-0191333710.20868/ade.2024.53078224Applicability of the multifactor dimensionality reduction methodology to the analysis of variables in sanitary buildingsAlejandro Prieto-Fernández0https://orcid.org/0000-0001-6716-1085Álvaro Carmona-Baltasar1Jaime Gónzalez-Domínguez2https://orcid.org/0000-0003-4042-1195Manuel Botejara-Domínguez3https://orcid.org/0000-0003-2570-1658Gonzalo Sánchez-Barroso4https://orcid.org/0000-0002-7006-1197Justo García-Sanz-Calcedo5https://orcid.org/0000-0003-4449-2636Escuela de Ingenierías Industriales, Universidad de ExtremaduraEscuela de Ingenierías Industriales, Universidad de ExtremaduraEscuela de Ingenierías Industriales, Universidad de ExtremaduraEscuela de Ingenierías Industriales, Universidad de ExtremaduraEscuela de Ingenierías Industriales, Universidad de ExtremaduraEscuela de Ingenierías Industriales, Universidad de ExtremaduraIn order to optimise engineering activities, it is necessary to analyse a large amount of data and variables. The objective is to implement MDR to better address the study and propose improvements to reduce energy consumption in Extremadura's health centres. These numerous variables do not have direct and quantifiable interactions on energy consumption. To overcome this drawback, it is possible to apply the multifactor dimensionality reduction (MDR) method. MDR uses Machine Learning to search for the best combinations of variables. This makes it possible to create a model that simplifies the analysis of the data studied. From the whole set, the combination of variables that best describes the study is selected, grouping them into high or low risk. It has been proven that in this way it is possible to better understand and optimise engineering activities. MDR can be used in numerous engineering analyses: energy consumption, equipment maintenance, waste generation, etc.https://polired.upm.es/index.php/anales_de_edificacion/article/view/5307multifactor dimensionality reduction; engineering; healthcare building; machine learning; data mining |
| spellingShingle | Alejandro Prieto-Fernández Álvaro Carmona-Baltasar Jaime Gónzalez-Domínguez Manuel Botejara-Domínguez Gonzalo Sánchez-Barroso Justo García-Sanz-Calcedo Applicability of the multifactor dimensionality reduction methodology to the analysis of variables in sanitary buildings Anales de Edificación multifactor dimensionality reduction; engineering; healthcare building; machine learning; data mining |
| title | Applicability of the multifactor dimensionality reduction methodology to the analysis of variables in sanitary buildings |
| title_full | Applicability of the multifactor dimensionality reduction methodology to the analysis of variables in sanitary buildings |
| title_fullStr | Applicability of the multifactor dimensionality reduction methodology to the analysis of variables in sanitary buildings |
| title_full_unstemmed | Applicability of the multifactor dimensionality reduction methodology to the analysis of variables in sanitary buildings |
| title_short | Applicability of the multifactor dimensionality reduction methodology to the analysis of variables in sanitary buildings |
| title_sort | applicability of the multifactor dimensionality reduction methodology to the analysis of variables in sanitary buildings |
| topic | multifactor dimensionality reduction; engineering; healthcare building; machine learning; data mining |
| url | https://polired.upm.es/index.php/anales_de_edificacion/article/view/5307 |
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