Air handling unit and rooftop unit with predictive control
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Solution Overview
Problem
HVAC systems, such as air handling units (AHUs) and rooftop units (RTUs), consume significant power, leading to high energy costs due to the operation of fans, compressors, and heating/cooling elements, with existing technologies failing to optimize energy usage effectively.
Innovation Solution
Incorporating a predictive controller that optimizes energy usage by determining the optimal amount of energy to purchase from the grid and store in a battery, considering time-varying energy prices, demand charges, and the cost of heating/cooling, to minimize overall energy costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the AHU operates powered components continuously to maintain building zone temperatures, then temperature control reliability is improved, but energy consumption increases
Solution Approach 1:
The predictive controller performs preliminary actions by forecasting future energy prices and demand charges, then proactively adjusting power setpoints and temperature setpoints before peak pricing periods occur. This allows the system to pre-cool or pre-heat building zones using stored cooling/heating capacity, reducing the need for continuous high-power operation during expensive periods while maintaining temperature reliability.
Solution Approach 2:
The system dynamically adjusts operating parameters based on real-time energy pricing data and forecasted conditions. The predictive controller continuously optimizes power setpoints for fans, compressors, and heating/cooling elements, as well as temperature setpoints for building zones, creating a dynamic control strategy that adapts to changing economic conditions while maintaining reliable temperature control.
2Power
If the AHU purchases electric energy from the energy grid during peak pricing periods, then immediate power availability is improved, but energy cost increases
Solution Approach 1:
The predictive controller implements feedback mechanisms by continuously monitoring actual energy prices, demand charges, and system performance against forecasted conditions. This feedback loop allows the system to refine its predictions and adjust power setpoints in real-time, optimizing the balance between purchasing grid power during low-cost periods versus using stored cooling/heating capacity during high-cost periods.
Solution Approach 2:
The system changes operational parameters by adjusting power setpoints and temperature setpoints based on forecasted energy pricing conditions. When peak pricing is predicted, the controller modifies these parameters to reduce grid power consumption during expensive periods, utilizing stored thermal energy in building zones and thermal energy storage systems to maintain comfort while avoiding high energy costs.
3Reliability
If the AHU increases cooling capacity during hot periods to maintain zone temperatures, then temperature control reliability is improved, but demand charge increases
Solution Approach 1:
The predictive controller performs preliminary cooling actions by pre-cooling building zones before predicted peak demand charge periods occur. By storing cooling capacity in the thermal mass of building structures and thermal energy storage systems during off-peak hours, the system reduces the need for high-capacity cooling operation during expensive periods, thereby lowering demand charges while maintaining temperature reliability.
Solution Approach 2:
The system dynamically adjusts cooling capacity and temperature setpoints based on forecasted demand charge conditions. The predictive controller continuously optimizes the operation of compressors, condensing units, and air handlers, creating a dynamic control strategy that smooths peak demand while maintaining reliable temperature control during hot periods.
Data Source
AI summary
An air handling unit (AHU) or rooftop unit (RTU) or other building device in a building includes one or more powered components and is used with a battery, and a predictive controller The battery is configured to store electric energy and discharge the stored electric energy for use in powering the powered components. The predictive controller is configured to optimize a predictive cost function to determine an optimal amount of electric energy to purchase from an energy grid and an optimal amount of electric energy to store in the battery or discharge from the battery for use in powering the powered components at each time step of an optimization period.