Carbon emission and thermal comfort prediction model for an office building considering the contribution rate of design parameters

In recent years, increasing climate change has triggered the development of various techniques and strategies for predicting building energy consumption and thermal comfort. However, many features could affect prediction efficiency, and the coupling of appropriate learning algorithms with different sampling methods and input parameters requires further research. In this study, we evaluated 25 features of an office building model. Then, the comprehensive effects of five different sampling methods and cumulative contribution rates (CCR) of input parameters on the prediction performance of building carbon emission (BCE) and indoor discomfort hour (IDH) were explored in 10 machine learning algorithms. The results indicated that the Sobol sampling method could achieve the best prediction effect in the combinations of different contribution rates of features and machine learning algorithms. Meanwhile, an artificial neural network (ANN) was the best learning algorithm when the CCR was 100%. However, the optimal machine learning method corresponding to each CCR stage differed. When the CCR was reduced to 50%, only three influence factors were considered most important, and K-nearest neighbors (KNN) was the best prediction algorithm in this scenario, indicating that the input parameters were reduced by 88%, and the R-2 value was only reduced by 6.1%. Therefore, we proposed a new strategy idea for the research of building performance prediction by determining the most important building impact factors and suitable machine learning algorithms, which could simplify the prediction process and improve prediction efficiency while reducing building parameters. (C) 2022 The Author(s). Published by Elsevier Ltd.

Automated summary

In recent years, the development of techniques and strategies for predicting building energy consumption and thermal comfort has been driven by increasing climate change. This study evaluated 25 features of an office building model and explored the effects of different sampling methods and input parameters on the prediction performance of building carbon emission and indoor discomfort hour using 10 machine learning algorithms. The results indicated that the Sobol sampling method achieved the best prediction effect in different combinations of features and machine learning algorithms. Additionally, the artificial neural network was the best learning algorithm when the contribution rate was 100%, but the optimal algorithm varied at different contribution rate stages.