Building HVAC system with multi-objective optimization control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing building HVAC systems struggle to maintain occupant comfort and disinfection while minimizing costs, often leading to inefficient energy consumption and excessive expenses.
Innovation Solution
A controller for HVAC equipment that uses predictive models to optimize environmental conditions by balancing carbon emissions, infection risk, and operational costs through multi-objective optimization, allowing for real-time and off-line decision-making to achieve desired comfort and disinfection levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If building equipment is operated to change environmental conditions for occupant comfort and disinfection, then occupant comfort and disinfection are improved, but operating expenses increase
Solution Approach 1:
The system changes operational parameters of building equipment (HVAC settings, lighting schedules, equipment runtime) to achieve optimal balance between environmental conditions and energy consumption. The optimization controller adjusts these parameters dynamically based on predictive models and multiple objectives including comfort, disinfection, and carbon emissions.
Solution Approach 2:
The system uses predictive models to forecast future environmental conditions and equipment performance before making control decisions. This allows the optimization controller to pre-determine optimal operating strategies that balance comfort and disinfection requirements with energy consumption, rather than reacting to current conditions alone.
2Ease of operation
If building equipment is operated to maintain environmental conditions, then occupant comfort is improved, but energy consumption increases
Solution Approach 1:
The system transitions from static HVAC control to dynamic optimization that continuously adjusts equipment operation based on changing conditions. The multi-objective optimization controller modifies setpoints and operational parameters in real-time to maintain comfort while minimizing energy use, rather than maintaining fixed environmental conditions.
Solution Approach 2:
The optimization controller dynamically changes operational parameters such as temperature setpoints, airflow rates, and equipment scheduling to achieve the best compromise between comfort maintenance and energy consumption, particularly during transition periods or when predictive models indicate favorable external conditions.
3Productivity
If multiple environmental conditions are optimized simultaneously, then overall building performance is improved, but control system complexity increases
Solution Approach 1:
The optimization controller is designed as a multi-functional system that simultaneously handles multiple objectives including occupant comfort, disinfection, energy consumption, and carbon emissions. This universal controller integrates various control functions and predictive models into a single coordinated system, managing multiple environmental conditions through unified optimization rather than separate control systems.
Solution Approach 2:
The system introduces an optimization controller as an intermediary layer between building equipment and environmental conditions. This mediator coordinates multiple objectives and translates complex multi-parameter optimization into actionable equipment commands, simplifying the control architecture while achieving comprehensive building performance optimization.
Data Source
AI summary
A controller for heating, ventilation, or air conditioning (HVAC) equipment operable to affect an environmental condition of a building is configured to obtain predictive models that predict values of an energy control objective and an air quality control objective as a function of control decision variables for the HVAC equipment. The controller executes a multi-objective optimization process using the predictive models to produce multiple sets of optimization results corresponding to different values of the control decision variables, the energy control objective, and the air quality control objective. The controller selects one or more of the sets of optimization results based on the values of the energy control objective and the air quality 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.