Fast building performance simulation method for evaluating renovation strategies of terraced houses in the Netherlands

A fast building performance simulation method is proposed for evaluating renovation strategies of Dutch terraced houses. It utilizes limited EnergyPlus simulation data generated by data sampling to train surrogate models. Two data sampling approaches (simple random sampling (SRS) and Latin hypercube sampling (LHS)) and three data-driven models (multiple linear regression, extreme gradient boosting, and artificial neural network (ANN)) are investigated. A Dutch terraced house is applied for performance verification. The results show that surrogate models trained with simulation samples from LHS achieve accuracy similar to those trained with samples from SRS. Furthermore, it is found that limited simulation data (400 samples) are sufficient to train an accurate surrogate model. ANN is the most accurate model in simulating indoor air temperatures (R2 = 0.99, MAE = 0.12 ºC) and thermal loads (R2 = 0.95, MAE = 700 W). The computational time of ANN accounts for only 0.03% of that of EnergyPlus.