Enhancing energy efficiency and coordinated scheduling in university building clusters driven by student energy behavior

The optimization of energy scheduling in university building clusters is of great significance for achieving energy conservation, reducing consumption, and improving energy utilization efficiency. With the intensifying global energy crisis and the challenges posed by climate change, how to achieve a low-carbon transformation on campuses has become an urgent issue to address. Existing research has mainly focused on energy consumption optimization for individual buildings or at the macro level, with limited in-depth exploration of individual student energy behavior differences and their interactions in multi-building environments. This study proposes an energy scheduling optimization method driven by student behavior by integrating students' energy usage patterns with the energy efficiency scheduling of building clusters. The campus buildings are classified into three categories—daytime energy-intensive, nighttime peak-intensive, and balanced consumption buildings—and a dynamic surplus-deficit threshold is introduced to develop time-based scheduling control strategies for different building types. Using Sichuan University as a case study, simulation results show that the optimization strategy reduces energy consumption in daytime energy-intensive buildings by approximately 16.35%, lowers peak load in nighttime energy-intensive buildings by 16.02%, and improves overall energy efficiency by nearly 20%. Furthermore, while maintaining comfort, the method significantly reduces energy costs and peak load fluctuations, demonstrating the feasibility and effectiveness of behavior-driven scheduling optimization in enhancing building cluster energy efficiency management. This research provides scientific support and practical guidance for advancing green, low-carbon campus development and intelligent energy management.