Conference proceeding
Using Artificial Intelligence Models for Real-time Forecasting of Indoor Microclimate in Historic Buildings
EMERGING CHALLENGES technological
2025
Abstract
Accurate monitoring of the indoor microclimate, including air temperature, relative humidity, and dew point, is crucial for preserving historic buildings and ensuring sustainable conservation. Similarly, forecasting indoor environmental conditions is essential for both improving building performance and making informed decisions about the conservation of historic structures. This study addresses the research gap in predicting the indoor microclimate of structures by developing and evaluating the performance of three Machine Learning (ML) methods for forecasting indoor microclimate in the Kelso House, a low-thermal mass historic building in San Antonio, Texas, USA. From April 2022 to January 2023, indoor and outdoor conditions (air temperature, relative humidity, and dew point) were recorded every 15 minutes using data loggers. The collected datasets were used to train and test the different ML algorithms, namely Multi-Layer Perceptron (MLP), Support Vector Regression (SVR), and Extreme Gradient Boosting (XGBoost). Various predictive models were developed to forecast the 15-minute-ahead values of indoor air temperature, relative humidity, and dew point within the case study building. The accuracy and computational efficiency of the models were evaluated using metrics such as mean absolute error and convergence time. Results showed that MLP and SVR achieved the highest accuracy and effectively detected abrupt fluctuations in temperature and relative humidity, outperforming XGBoost. However, XGBoost demonstrated exceptional computational efficiency in terms of convergence time, making it suitable for forecasting applications as well. This investigation highlights the potential of the developed ML-driven models for accurately forecasting indoor microclimate. Additionally, the proposed methodology is adaptable and can be applied to a wide range of construction across different climate zones globally. By enabling the prediction of indoor environmental conditions critical to historic preservation, this study provides valuable insights to assist experts in making informed decisions about the conservation of historic structures.
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Details
- Title
- Using Artificial Intelligence Models for Real-time Forecasting of Indoor Microclimate in Historic Buildings
- Creators
- Carlos Faubel Alama - Drexel University, Civil, Architectural, and Environmental EngineeringLayla Iskandar - The University of Texas at San AntonioAntonio Martinez-Molina - Drexel University, Architecture, Design, and Urbanism
- Publication Details
- EMERGING CHALLENGES technological
- Conference
- 2025 Architectural Research Centers Consortium (ARCC) International Conference
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Civil, Architectural, and Environmental Engineering
- Other Identifier
- 991022199650504721