Applicability of the multifactor dimensionality reduction methodology to the analysis of variables in sanitary buildings
DOI:
https://doi.org/10.20868/ade.2024.5307Keywords:
Multifactor Dimensionality Reduction; Engineering; Healthcare Building; Machine Learning; Data MiningAbstract
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.
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