The indoor thermal environment plays a crucial role in influencing occupants' comfort and productivity. Heating, ventilation, and air conditioning (HVAC) systems are the primary means of controlling indoor thermal conditions in residential buildings. However, HVAC systems consume a significant amount of energy, accounting for 20%–40% of a building's total energy usage, resulting in high operational costs. Therefore, it is essential to design HVAC control strategies that minimize energy consumption while maintaining an optimal thermal environment. This paper proposes an augmented intelligence (AuI)-based framework that integrates human guidance with deep reinforcement learning (DRL) to optimize HVAC control. We formulate an optimization problem aimed at balancing energy consumption with thermal comfort, minimizing the costs associated with both factors. A human behavior model is developed to simulate occupant responses to varying thermal conditions. The proposed AuI-enhanced deep deterministic policy gradient (DDPG) algorithm is employed to learn the optimal HVAC strategy, considering both thermal comfort and energy efficiency. A simulation environment is constructed for strategy training and performance evaluation. The results show that, compared to a baseline without human guidance, the AuI-DDPG algorithm reduces HVAC energy consumption by 38.7% while improving occupant thermal comfort by 31.1%.