Heating, ventilation, and air-conditioning systems provide a comfortable indoor thermal environment, but high energy consumption is often necessary to achieve an adequate level of indoor thermal comfort. However, it is challenging to design an energy-efficient thermal comfort control strategy, mainly because the internal thermal environment is influenced by complicated factors and difficult to model accurately. To solve this problem, a control strategy incorporating the parallel temporal convolutional neural network (PTCN) and the improved chimp optimization algorithm (ICHOA) is proposed for thermal comfort control of buildings. Thermal comfort control is transformed into a cost-minimization problem by establishing an objective function for both the future thermal comfort of the occupants and energy consumption and optimizing multiple air-conditioning temperature set points for the coming day. First, to ensure the prediction performance, a PTCN model was developed to predict the energy consumption and thermal comfort under different factors. An opposition-learning-based adaptive chimp algorithm was then used to solve the objective function to output the optimal set temperature. Finally, the superiority of the PTCN-ICHOA optimization strategy was verified using an office building in Jinan as an example. In winter and summer experiments, the proposed PTCN model achieved the lowest prediction errors among the models compared in terms of energy and temperature prediction. Furthermore, the PTCN-ICHOA optimization model exhibited faster convergence than the other models for both experiments. Through the proposed optimization strategy, energy consumption savings of approximately 6.3%–8.1% can be achieved while maintaining indoor thermal comfort.

供暖、通风和空调系统提供了舒适的室内热环境，但通常需要高能耗才能达到足够的室内热舒适度。然而，设计一种节能的热舒适控制策略具有挑战性，主要是因为内部热环境受复杂因素影响，难以准确建模。为了解决这个问题，提出了一种结合并行时间卷积神经网络（PTCN）和改进的黑猩猩优化算法（ICHOA）的控制策略，用于建筑物的热舒适控制。通过为居住者的未来热舒适度和能源消耗建立目标函数，并优化未来一天的多个空调温度设定点，热舒适度控制转化为成本最小化问题。首先，为了保证预测性能，建立了一个PTCN模型来预测不同因素下的能耗和热舒适度。然后使用基于对立学习的自适应黑猩猩算法求解目标函数以输出最佳设定温度。最后以济南某写字楼为例，验证了PTCN-ICHOA优化策略的优越性。在冬季和夏季实验中，所提出的PTCN模型在能量和温度预测方面实现了模型中最低的预测误差。此外，PTCN-ICHOA 优化模型在两个实验中都表现出比其他模型更快的收敛速度。通过提出的优化策略，在保持室内热舒适度的同时，可以实现约 6.3%–8.1% 的能耗节省。