HVAC Cost Target Optimization Using Dynamic Temperature Setpoints
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Solution Overview
Problem
HVAC systems face challenges in minimizing energy consumption without causing occupant discomfort, as maintaining comfortable temperatures often leads to high energy costs, and existing systems struggle to optimize energy usage while ensuring occupant comfort.
Innovation Solution
A building management system that uses a neural network to classify the current state of a building, determines temperature bounds, and adjusts a cost function with penalty terms to optimize temperature setpoints, thereby reducing energy consumption while maintaining comfort by penalizing deviations from set temperature ranges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If HVAC systems maintain comfortable temperatures for occupants, then occupant comfort is improved, but energy consumption increases
Solution Approach 1:
The system performs preliminary action by pre-cooling or pre-heating the building during periods of low energy cost (off-peak hours) so that the indoor temperature remains within comfortable bounds during high-cost periods without requiring continuous HVAC operation. This anticipatory approach reduces peak energy consumption while maintaining comfort.
Solution Approach 2:
The system dynamically adjusts temperature setpoints and HVAC operation based on real-time conditions including outdoor temperature, occupancy patterns, and energy pricing. The controller continuously optimizes the balance between comfort and energy consumption by adapting control parameters rather than maintaining fixed settings.
2Use of energy by moving object
If HVAC systems reduce energy consumption, then energy cost decreases, but occupant comfort deteriorates
Solution Approach 1:
The system incorporates feedback mechanisms that continuously monitor indoor temperature, occupancy, and energy consumption. This feedback enables the controller to adjust HVAC operation in real-time to maintain comfort within acceptable bounds while minimizing energy cost, preventing excessive temperature deviations that would compromise occupant comfort.
Solution Approach 2:
The system changes operational parameters such as temperature setpoints, HVAC cycling patterns, and equipment runtime based on energy pricing signals and environmental conditions. By dynamically adjusting these parameters, the system achieves cost reduction without allowing temperature to deviate beyond comfortable ranges for occupants.
3Productivity
If existing systems optimize energy usage, then energy efficiency improves, but the ability to ensure occupant comfort becomes insufficient
Solution Approach 1:
The system uses thermal energy storage (pre-cooling or pre-heating building structures) during off-peak hours to provide a thermal buffer that maintains comfortable temperatures during peak periods without requiring continuous high-energy HVAC operation, thereby improving efficiency while ensuring comfort reliability.
Solution Approach 2:
The system introduces thermal mass (building structure, water tanks, or phase change materials) as an intermediary between the HVAC system and the occupied space. This intermediary stores and releases thermal energy, decoupling the timing of energy consumption from comfort provision, thereby improving energy efficiency while maintaining reliable comfort assurance.
Data Source
AI summary
A heating, ventilation, or air conditioning (HVAC) system for a building includes one or more processing circuits having one or more processors and one or more non-transitory computer-readable media containing program instructions. When executed by the one or more processors, the instructions cause the one or more processors to perform operations including providing an optimization function for operating HVAC equipment over a future time period including a plurality of time steps and using the optimization function to generate a time series of temperature setpoints for the plurality of time steps in the future time period. The time series of temperature setpoints achieve a target value of the optimization function over the future time period. The operations include operating the HVAC equipment to drive indoor air temperature toward a first temperature setpoint of the time series of temperature setpoints for a first time step of the plurality of time steps.