Continuous variable quantum reinforcement learning for HVAC control and power management in residential building

• Deep learning techniques is used for occupancy detection and quantum reinforcement learning (QRL) for optimal HVAC control. • Multi-zone cooling is used to control the temperature of individual zones in the building. • Clustering is employed to group similar data points and adjust cooling, resulting in overall energy savings. The use of occupancy information for heating, ventilation, and air conditioning (HVAC) control in smart buildings has become increasingly important for enhancing energy efficiency and occupant comfort. However, residential HVAC control presents significant challenges due to the complex dynamic nature of buildings and the uncertainties associated with heat loads and weather conditions. This study addresses this gap in adaptive and energy efficient HVAC control by introducing a quantum reinforcement learning (QRL) based approach. Unlike conventional reinforcement learning techniques, QRL leverages quantum computing principles to efficiently handle high dimensional state and action spaces, enabling more precise HVAC control in multi-zone residential buildings. The proposed framework integrates real-time occupancy detection using deep learning with operational data, including power consumption patterns, air conditioner control data, and external temperature variations. To evaluate the effectiveness of the proposed approach, simulations were conducted using real world data from 26 residential households over a three month period. The results demonstrate that the QRL based HVAC control significantly reduces energy consumption and electricity costs while maintaining thermal comfort. Compared to the deep deterministic policy gradient method, the QRL approach achieved a 63% reduction in power consumption and a 64.4% decrease in electricity costs. Similarly, it outperformed the proximal policy optimization algorithm, leading to an average reduction of 62.5% in electricity costs and 62.4% in power consumption. RL integrated with a quantum layer. Each iteration involves the following loop. At each time step, the agent receives the state and produces the action parameter based on the current policy. Subsequently, the agent generates the next state. Using the information from both, a reward is calculated and provided as feedback to the agent. Following this, the policy is updated based on the state and reward. The RX gates in the dashed box symbolize rotations along the x-axis for single-qubit operations. Utilizing CNOT gates facilitates the entanglement of quantum states across individual qubits.