Abstract The time-varying nature of the heating loads of public buildings creates scope for exploring strategies to improve the energy system efficiency and to reduce the energy consumption and system operating costs. A well-researched and refined energy system operation strategy based on time-varying heating load demands is proposed in this paper. The proposed strategy is more effective and efficient than the existing experience-based operation strategies used to run energy systems. With full consideration of the factors affecting building heating loads under various scenarios, a multi-objective particle swarm optimisation algorithm combined with a scenario analysis is presented in this paper. The system efficiency and operation cost are set as two basic objectives to generate a Pareto frontier, and the occupant thermal comfort level is the dominant consideration while selecting an optimal state point for the final operation strategy. Using this simplified decision-making process, this approach can simultaneously calculate both the starting sequence and parameter settings for an optimised operation of the heat supply units. An energy plant on a university campus in Tianjin was selected to implement and evaluate this optimisation strategy. The case study results show that, without compromising the requirements of the thermal comfort of the building occupants, the energy system operating cost can be reduced by 38.9%, with an increase by a factor of 2.24 in the system coefficient of performance when compared with the current experience-based operation strategies.