Energy flexibility of nighttime cooling of internal thermal mass: development of EnergyPlus Python Plugin

Building peak energy demand continues to rise due to widespread electrification. Nighttime cooling of internal thermal mass is an effective measure to enhance building energy flexibility. Evaluating such flexibility requires whole-building energy simulations, but EnergyPlus lacks an accurate internal thermal mass model. This study aims to integrate an internal thermal mass analytical model into EnergyPlus via a Python Plugin. The model is validated against on-site time series measurements from literature. The validation yields a Cv(RMSE) of 7.1 % and an NMBE of −4.2 %. Comparative performance analyses with and without the Python Plugin demonstrate the accuracy of the model. The maximum peak air temperature deviation reaches 2.7 °C, and nighttime air change rate per hour deviation is −0.16 ± 0.18 h−1. Applying the model to assess energy efficiency shows that nighttime cooling of internal mass is effective for up to 14 days in west-facing rooms during June. In September, the average peak power hour is delayed by 0.8 h, and 63.3 % of days experience peak cooling power reductions of up to 0.53 W m−2. Even in July, 3.2 % of days exceed 80 % cooling efficiency, and nighttime cooling reduces air-handling unit operation, lowering total building energy consumption by 5.5 %. Overall, the Python Plugin model enables a more accurate evaluation of building energy flexibility by improving the prediction precision of indoor air temperatures and air change rates per hour.