Lightweight construction gained popularity due to many advantages, such as faster construction, higher design flexibility, and cost-efficiency. However, lightweight façade systems raise concerns over indoor thermal comfort and increased space energy use due to the lack of thermal storage properties. Phase Change Materials (PCMs) can enable better control of thermal performance by storing excessive heat during the melting phase and releasing this heat during the freezing phase. Nevertheless, PCMs are emerg-ing technology at a high cost. Therefore, an efficient and cost-effective application of PCMs typically requires customized solutions based on detailed numerical analysis. Building energy modeling (BPS) is a robust technique that provides a pathway to test, analyze, and optimize various energy efficiency measures and technologies. The Stanley Pauley Engineering (SPEB) building used as a case study was still under the construction when this research study was conducted. Therefore, to have more confidence in the predictions of the models developed in EnergyPlus its predictions were compared against the models developed in IES by the company Stantec. Approximately 2.1% and 1.7% discrepancy between the predictions of the total energy consumption and energy use intensity, respectively demonstrate good agreement between the two models. There is a limited number of BPS tools capable of simulating the phase change phenomena and in particular those able to model the hysteresis effect. As of version 8.9, EnergyPlus can model the hysteresis effect. The results show a discrepancy between the hysteresis and enthalpy method that may vary considerably with the intensity and duration of the solar radiation received by the location with PCM. Additionally, The PCM improved energy and thermal performance of the investigated spaces by achieving heat-ing and cooling energy savings ranging from 1.9% to 11.4% and 5.5% to 37.4%, respectively as well as reducing the discomfort hours from 10% to 29% depending on the zone and material. The results of the local sensitivity analysis indicate that uncertainty in critical PCM’s properties such as peak melting temperature, peak freezing temperature, and latent heat during the entire phase change process can increase the inaccuracy of the modeling predictions.