This study explores the optimization of the seasonal performance factor of air-source heat pumps using the outdoor temperature. Conducted within the framework of the research project ‘optLWP’, a single-family house in South Germany served as the case study. The heat pump operation was monitored under a rule-based controller. Measurement data was utilized to calculate the seasonal performance factor. A theoretical analysis was conducted subsequently in which the seasonal performance factor was recalculated by assuming an operation shift of the HP towards periods of high ambient air temperatures. The study employed a clustering approach to identify representative periods from the dataset, allowing for the reduction of analytical effort while ensuring accuracy. The theoretical operation shift demonstrated optimization potential of the seasonal performance factor up to 13.5%. However, when considering the influence of increased modulation, the benefits were partially or fully negated. This resulted in reductions of up to 4.8% of the seasonal performance factor in the worst case. The findings underscore the trade-offs between higher ambient temperature operation and the impact of higher modulation on the efficiency of heat pumps. While predictive control strategies offer a promising pathway to enhance the performance of heat pump systems, the impact of a higher modulation is often not considered in optimization algorithms to reduce computational effort. This research highlights the importance of integrating modulation effects into such algorithms. Future work will focus on refining predictive control approaches to balance ambient temperature benefits, modulation rate impacts, and storage losses, ensuring thermal comfort while maximizing efficiency.