Optimizing energy efficiency and indoor comfort through predictive models of building envelope heat transfer

The integration of predictive models for building envelope heat transfer in contemporary architecture is increasingly important for enhancing energy efficiency and comfort while reducing environmental impact. Heat loss and gain through envelope heat transfer are key factors influencing a building’s energy consumption. Predictive models optimize the use and placement of materials like thermal insulation and windows to improve energy efficiency and indoor comfort.

Previous research has underscored the significance of predictive models in managing indoor conditions such as temperature and humidity, contributing to improved occupant comfort (Tan et al., 2024). Furthermore, buildings contribute to environmental issues, including carbon dioxide emissions and resource depletion, through their energy consumption. Optimizing energy efficiency with predictive models can mitigate these impacts, promoting sustainable architectural practices (Mi, 2024). Thus, these models are vital in building design and energy management, aiding compliance with regulatory standards and certification processes.

This study aims to develop and validate predictive models for building envelope heat transfer using real-life data from outdoor experiments. The data collected includes outside temperature, solar radiation, and surface temperatures. Two predictive models were developed: a Linear Regression (LR) model and a Neural Network (NN) model, both designed to forecast indoor surface temperatures based on external environmental factors.

The results indicate that the LR model outperforms the NN model in terms of predictive accuracy. The LR model consistently showed a lower mean squared error (MSE) across various scenarios, demonstrating its superior reliability in predicting indoor surface temperatures in this context. These findings highlight the potential of LR models for practical applications in building energy management.

However, this study also identifies challenges in improving the accuracy and applicability of predictive models, including accounting for climate variations and complex building structures. While the simplicity of the LR model is advantageous, it may limit effectiveness in more complex scenarios. Concerns about evaluation bias underscore the need for objective methods in model development.

In conclusion, the development of LR and NN models for predicting building envelope heat transfer offers valuable insights into optimizing energy efficiency and indoor comfort. Future research should focus on refining these models to address the identified challenges, with the goal of creating more robust and accurate predictive tools. Advancing these models is crucial for promoting sustainable architectural practices and achieving greater energy efficiency in buildings.