Building energy simulation is widely employed to support sustainable design, retrofit evaluation, and operational decision-making. However, the reliability of such applications depends critically on accurate model calibration. This study proposes a novel calibration methodology for building cooling load models that leverages high-resolution data to enhance accuracy and robustness. The approach extends the ASHRAE Residential Cooling Load Factor method by introducing the Tuning Night and Day Cycle (TNDC) technique, which differentiates between nighttime and daytime thermal dynamics. Specifically, nighttime cooling loads are exploited to identify infiltration rates, while daytime loads are utilized to estimate the solar heat gain coefficient. An integrated simulation framework was developed using EnergyPlus and the Python-based package OpyPlus to implement the proposed method. The approach was validated through a case study of an office building, demonstrating substantial improvements in calibration accuracy: the normalized mean bias error (NMBE) decreased from 44% to 1%, and the coefficient of variation of the root mean square error (CV(RMSE)) was reduced from 53% to 24%. These results highlight the potential of TNDC as a rigorous and scalable strategy for improving building energy model calibration using high-resolution data.