RT Journal Article
T1 Optimisation of thermal comfort and indoor air quality estimations applied to in-use buildings combining NSGA-III and XGBoost
A1 Martínez Comesaña, Miguel
A1 Eguía Oller, Pablo
A1 Martínez Torres, Javier
A1 Febrero Garrido, Lara
A1 Granada Álvarez, Enrique
K1 1299 Otras Especialidades Matemáticas
AB Indoor environmental quality (IEQ) monitoring of in-use buildings has become essential in recent years due to the COVID-19 pandemic, as it significantly affects the well-being, health and productivity of building users. Nevertheless, knowing in real time the environmental conditions in large multi-zone areas is a difficult issue. Thus, the use of machine learning techniques to estimate indoor conditions has increased considerably. The aim of this paper is to present an interpolation model, based on an optimised extreme gradient boosting algorithm, to estimate every minute the indoor temperature, relative humidity and CO concentration inside buildings. These estimations are obtained without requiring permanent monitoring in the occupied zone. The optimisation, focused on finding the minimum number of monitoring devices needed to provide accurate interpolations, is performed using the multi-objective genetic algorithm NSGA-III. This methodology was applied in a research centre in the north-western Spain. The results show that the optimised or reduced model is capable of estimating indoor temperatures and relative humidity with relative errors below 6% and CO2 levels below 10%.
PB Sustainable Cities and Society
SN 22106707
YR 2022
FD 2022-05
LK http://hdl.handle.net/11093/3094
UL http://hdl.handle.net/11093/3094
LA eng
NO Sustainable Cities and Society, 80: 103723 (2022)
NO Financiado para publicación en acceso aberto: Universidade de Vigo/CISUG
NO Agencia Estatal de Investigación | Ref. RTI2018-096296-B-C2
DS Investigo
RD 04-dic-2024