Machine Learning (ML) as a surrogate model for early-stage energy optimization

Paper in proceeding, 2024

Early-stage optimization is effective in reducing a building’s energy consumption. However, today’s optimization process is based on simulation and is often very time-consuming and inefficient. To address this, we developed a machine learning (ML) surrogate model to replace the heating demand simulation process. The model was trained using a parametrically generated synthetic dataset based on rectangular buildings. We investigated which ML algorithm performs best concerning the size of the training dataset. We found that Linear Regression performs best when the dataset is smaller than 1000 while Random Forest performs best as the training dataset increases above 1000. When the dataset size reaches 9200, Random Forest can reach a mean absolute error of 1.68 kWh/m2 and a root mean square error of 2.69 kWh/m2. The best-performing surrogate model can predict the heating demand within 0.00005 seconds with high accuracy. Thus, our results show that an ML surrogate model can substitute a building’s heating demand simulation and significantly improve the efficiency of early-stage optimization processes.