System and method for HVAC (heating, ventilation, and air conditioning) optimization
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Solution Overview
Problem
Conventional HVAC systems struggle to balance thermal comfort and energy efficiency, particularly in environments with varying occupancy, weather, and occupant preferences, leading to suboptimal comfort and excessive energy consumption.
Innovation Solution
Integration of a digital twin with a reinforcement learning agent to optimize HVAC operations in real-time, using data from sensors and IoT devices to adjust parameters such as temperature, airflow, and energy consumption based on environmental conditions, occupancy patterns, and thermal comfort indices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If conventional HVAC systems operate on fixed schedules and settings, then system operation is simple and reliable, but thermal comfort is suboptimal and energy consumption is excessive
Solution Approach 1:
The patent implements dynamic HVAC control by replacing fixed schedules with real-time adjustment of system parameters based on occupancy patterns, environmental conditions, and thermal comfort indices. The digital twin continuously updates system state, and the reinforcement learning agent dynamically optimizes setpoints, airflow rates, and equipment operation to match actual conditions, thereby reducing energy consumption while maintaining comfort.
Solution Approach 2:
The patent employs feedback mechanisms through digital twin technology that continuously monitors system performance, occupancy, and environmental parameters. This feedback loop enables the reinforcement learning agent to learn from past decisions and adjust HVAC operations in real-time, optimizing energy efficiency while maintaining thermal comfort based on actual system response and occupancy patterns.
2Device complexity
If conventional HVAC systems use fixed settings, then system control is simple, but thermal comfort and energy efficiency balance is poor
Solution Approach 1:
The patent introduces a digital twin as an intermediary between the physical HVAC system and the control algorithm. This virtual model simplifies the control complexity by providing a computational environment where the reinforcement learning agent can safely learn and optimize control strategies without directly impacting the physical system, thereby improving reliability of thermal comfort and energy efficiency balance.
Solution Approach 2:
The patent creates a digital copy (digital twin) of the HVAC system that replicates its behavior and parameters. This copy allows the reinforcement learning agent to simulate and learn optimal control strategies in a virtual environment before applying them to the physical system, reducing control complexity while improving the reliability of comfort and efficiency outcomes.
3Reliability
If real-time data collection from sensors and IoT devices is implemented, then thermal comfort and energy efficiency can be optimized, but data processing and analysis becomes overwhelming
Solution Approach 1:
The digital twin serves as an intermediary that processes and integrates data from multiple sensors and IoT devices. Instead of directly analyzing overwhelming raw data, the reinforcement learning agent interacts with the simplified digital twin model, which consolidates sensor inputs into meaningful system states, thereby reducing data processing complexity while maintaining optimization reliability.
4Adaptability or versatility
If HVAC systems adapt to changing occupancy, weather, and preferences in real-time, then thermal comfort improves, but system complexity and data processing requirements increase
Solution Approach 1:
The patent implements dynamic adaptation by continuously adjusting HVAC parameters based on real-time occupancy, weather, and comfort preference data. The digital twin and reinforcement learning agent work together to dynamically optimize system operation, enabling high adaptability to changing conditions while managing complexity through intelligent automation and learning algorithms.
Data Source
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AI summary
A system (100) for HVAC optimization is disclosed. The system (100) may include a digital twin (112) of an HVAC system (114) installed in a premises (108). The digital twin (112) is based on design parameters, operational data, and PMV-based thermal comfort analysis. The system (100) further includes an RL agent (202) that is configured to receive and process at least real-time environmental conditions, occupancy patterns, and PMV value associated with the premises (108) of the HVAC system (114), identifying one or more optimal actions to adjust one or more HVAC parameters. The digital twin (112), driven by the identified optimal actions, may dynamically simulate the HVAC system (114) and the RL agent (202) may optimize operations of the HVAC system (114) based on the identified optimal actions, ensuring seamless and adaptive thermal comfort and energy efficiency management within the premises (108).