Building HVAC Controller with AI-Based Comfort Constraint Optimization
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Solution Overview
Problem
Traditional HVAC systems require occupants to manually adjust environmental conditions, leading to wasted time and energy, and can be costly if not managed efficiently, as they often fail to maintain optimal comfort levels without proper automation.
Innovation Solution
A controller and environmental control system that uses artificial intelligence to generate an occupant comfort model based on building and occupant data, optimizing the operation of HVAC equipment to maintain comfort while minimizing energy consumption by determining time-varying comfort constraints and setpoints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If occupants manually adjust environmental conditions themselves, then they can maintain personal comfort preferences, but it wastes occupants' time and energy
Solution Approach 1:
The system uses AI to automatically monitor building data, generate comfort models, determine time-varying comfort constraints, and adjust environmental conditions without occupant intervention. The system serves itself by learning from historical data and automatically optimizing HVAC operation to maintain comfort while eliminating the need for manual occupant adjustments.
Solution Approach 2:
The system continuously monitors building data and occupant comfort feedback, updates the comfort model in real-time, and adjusts environmental conditions based on this feedback loop. This automatic feedback mechanism replaces manual occupant adjustments while maintaining or improving comfort levels.
2Reliability
If building equipment is operated to maintain occupant comfort, then occupant comfort is maintained, but it can be expensive if not performed correctly
Solution Approach 1:
The system dynamically changes operational parameters of building equipment based on time-varying comfort constraints and real-time building conditions. By optimizing parameters such as temperature setpoints, equipment runtime, and energy consumption levels within comfort boundaries, the system maintains reliable comfort while minimizing energy loss and operational costs.
Solution Approach 2:
The system transitions from static comfort maintenance to dynamic optimization by continuously updating comfort models and adjusting equipment operation in real-time. This dynamic approach allows the system to adapt to changing conditions and optimize energy usage while maintaining comfort reliability.
3Loss of energy
If AI-based automatic comfort management is implemented, then operational costs are reduced and comfort is maintained, but the system complexity increases
Solution Approach 1:
The controller performs multiple functions including data collection, comfort model generation, constraint optimization, and equipment control within a single integrated system. This multi-functionality reduces the need for separate systems while managing complexity through consolidation of AI-based comfort management tasks.
Data Source
AI summary
A controller for maintaining occupant comfort in a space of a building. The controller includes processors and non-transitory computer-readable media storing instructions that, when executed by the processors, cause the processors to perform operations. The operations include obtaining building data and obtaining occupant comfort data. The operations include generating an occupant comfort model relating the building data to a level of occupant comfort within the space based on the building data and the occupant comfort data. The operations include generating time-varying comfort constraint for an environmental condition of the space using the occupant comfort model and include performing a cost optimization of a cost function of operating building equipment over a time duration to determine a setpoint for the building equipment. The operations include operating the building equipment based on the setpoint to affect the variable state or condition of the space.