Continuous variable quantum reinforcement learning for HVAC control and power management in residential building
Year: 2025
Type: article
Abstract: 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.
Source: Energy and AI
Authors: Sarvar Hussain Nengroo, Dongsoo Har, Hoon Jeong, Taewook Heo, Sangkeum Lee
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,630
Funders: Ministry of Trade, Industry and Energy, Electronics and Telecommunications Research Institute, Ministry of Science and ICT, South Korea, Korea Evaluation Institute of Industrial Technology, Korea Institute of Energy Technology Evaluation and Planning, Institute for Information and Communications Technology Promotion
Awards: G2381227441, G5080751969, G6749679801
OpenAlex ID: W4411326388