HVAC Controller Optimization for Carbon Emissions and Occupant Comfort
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Solution Overview
Problem
Existing building HVAC systems face challenges in maintaining occupant comfort and disinfection while minimizing costs, as they often require manual adjustments that can inadvertently affect other environmental conditions, leading to inefficient energy consumption and excessive expenses.
Innovation Solution
A controller for HVAC equipment that uses predictive models and optimization processes to balance carbon emissions, infection risk, operating costs, and capital costs, allowing for real-time and offline optimization to determine optimal control decisions for environmental conditions, incorporating features like UV lights and filtration systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If building equipment is operated to change specific environmental conditions, then occupant comfort is improved, but other environmental conditions are adversely affected and energy consumption increases
Solution Approach 1:
The system changes operational parameters of building equipment based on predictive models and optimization algorithms. The controller adjusts control decision variables (such as temperature setpoints, ventilation rates, equipment scheduling) to optimize multiple objectives simultaneously, including energy consumption and occupant comfort, rather than manually adjusting single parameters that adversely affect other conditions.
Solution Approach 2:
The system implements feedback through predictive models that evaluate the impact of control decisions on multiple environmental conditions. The optimization process uses feedback from simulated outcomes to iteratively improve control strategies, selecting decisions that balance energy consumption with maintaining occupant comfort across all environmental parameters.
2Reliability
If building equipment is operated to provide disinfection, then infection risk is reduced, but operating costs increase
Solution Approach 1:
The system performs preliminary action by using predictive models to forecast infection risk and evaluate disinfection strategies before implementation. The optimization process pre-determines cost-effective control decisions for disinfection equipment operation, allowing facility managers to implement disinfection measures only when and where they are most needed, rather than continuous operation that incurs excessive costs.
Solution Approach 2:
The system adjusts operational parameters of disinfection equipment based on predicted infection risk levels and cost constraints. The optimization algorithm modifies control decision variables such as UV light intensity, filtration system operation schedules, and ventilation rates to achieve sufficient disinfection at minimized operating costs.
3Ease of operation
If manual adjustments are made to environmental conditions, then specific comfort requirements are met, but other environmental conditions deteriorate
Solution Approach 1:
The system implements multi-functionality through a unified optimization framework that simultaneously manages multiple environmental conditions (temperature, humidity, ventilation, disinfection) rather than separate manual controls for each parameter. The predictive model evaluates the interrelationships between all environmental factors, ensuring that adjustments to meet specific comfort requirements do not destabilize other environmental conditions.
Solution Approach 2:
The system coordinates changes across multiple environmental parameters simultaneously through optimized control decisions. Rather than manually adjusting single parameters that may adversely affect others, the system uses predictive modeling to determine coordinated parameter changes that maintain overall environmental balance while meeting occupant comfort requirements.
Data Source
AI summary
A controller for heating, ventilation, or air conditioning (HVAC) equipment that is operable to affect an environmental condition of a building is configured to obtain predictive models that predict values of a carbon emissions control objective and another control objective as a function of control decision variables for the HVAC equipment. The controller executes an optimization process using the predictive models to produce sets of optimization results corresponding to different values of the control decision variables, the carbon emissions control objective, and the other control objective. The controller selects from the sets of optimization results based on the values of the carbon emissions control objective and the other control objective. The controller operates the HVAC equipment to affect the environmental condition of the building in accordance with the values of the control decision variables corresponding to a selected set of the optimization results.