Optimisation of thermal comfort and indoor air quality estimations applied to in-use buildings combining NSGA-III and XGBoost
Bibliographic Data
| ID | 21230171 |
|---|---|
| Authors | Miguel Martínez-Comesaña (0000-0002-4568-1968, Universidade de Vigo, corresponding author), Pablo Eguía-Oller (0000-0001-8616-9509, Universidade de Vigo), Javier Martínez Martínez (0000-0001-6359-895X, Universidade de Vigo), Lara Febrero-Garrido (0000-0002-8374-8967), Enrique Granada-Álvarez, Enrique Granada (0000-0002-1689-9992, Universidade de Vigo) |
| Year | 2022 |
| Volume | 80 |
| Pages | 103723 |
| Publication date | 2022-05-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Sustainable Cities and Society (JOURNAL) |
| Journal identifiers | ISSN: 2210-6707 • E-ISSN: 2210-6715 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.scs.2022.103723 |
| OpenAlex | W4210849796 |
| Language | EN |
| Citations received | 6 |
| References cited | 59 |
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 CO2 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
Air conditioning · Air quality index · Cooling load · Genetic algorithm · Geography · Indoor air quality · Machine learning · Meteorology · Relative humidity · Thermal comfort · Building Energy and Comfort Optimization · Computer Science · Engineering · Environmental Science · Indoor Air Quality and Microbial Exposure · Urban Heat Island Mitigation · Artificial Intelligence · Environmental Engineering
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| Citations per year | 6 |
|---|---|
| Citation span | 2025 - 2026 (2) |
| Highly cited | No |