Climate control adaptive temperature setpoint adjustment systems and methods
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Solution Overview
Problem
HVAC systems face inefficiencies due to overly stringent temperature setpoints, especially when buildings are unoccupied, leading to increased energy consumption and operational costs.
Innovation Solution
A climate control system that dynamically adjusts temperature setpoints based on historical occupancy data and performance metrics to optimize energy usage by determining candidate schedules that meet target temperatures while minimizing energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the temperature setpoint is kept stringent (close to occupied conditions) during unoccupied periods, then comfort upon occupancy is maintained, but energy consumption increases
Solution Approach 1:
The system performs preliminary conditioning of the building environment before the expected occupancy time. By using historical occupancy data to predict when occupants will return, the system pre-heats or pre-cools the space during the unoccupied period, so that comfortable conditions are already established when occupants arrive, eliminating the need for high energy consumption at occupancy transition
Solution Approach 2:
The temperature setpoint is made dynamic rather than static. The system adjusts the setpoint based on the building's thermal mass, outdoor temperature conditions, and predicted occupancy timing. This dynamic adjustment allows the system to optimize energy consumption by raising or lowering setpoints during unoccupied periods while ensuring comfort is restored by the expected occupancy time
2Use of energy by moving object
If the temperature setpoint is relaxed during unoccupied periods, then energy consumption decreases, but comfort upon occupancy may be compromised
Solution Approach 1:
The system performs preliminary conditioning of the building environment before the expected occupancy time. By using historical occupancy data to predict when occupants will return, the system pre-heats or pre-cools the space during the unoccupied period, so that comfortable conditions are already established when occupants arrive, eliminating the need for high energy consumption at occupancy transition
Solution Approach 2:
The system uses historical occupancy data and performance metrics as feedback to continuously refine temperature schedule predictions. By analyzing past occupancy patterns and how the building responds to temperature adjustments, the system learns to optimize relaxed setpoints that maintain comfort while minimizing energy consumption during unoccupied periods
3Use of energy by moving object
If multiple candidate schedules are evaluated, then optimal energy efficiency is achieved, but system complexity increases
Solution Approach 1:
The system generates and evaluates multiple candidate temperature schedules automatically using historical occupancy data and building performance metrics. Rather than requiring manual configuration or complex real-time optimization, the system self-adjusts by selecting from pre-computed candidate schedules that balance energy efficiency with comfort requirements, reducing the burden of system complexity
Data Source
AI summary
The present disclosure presents techniques for improving operational efficiency of climate control systems. A climate control system may include climate control equipment, a sensor that measures temperature in a building, and a control system that controls operation of the equipment using a first temperature schedule, which associates each time step with a temperature setpoint, when the building is occupied. When not occupied, the control system determines an expected return time based on historical occupancy data associated with the building, determines the temperature setpoint associated with the expected return time, determines candidate schedules each expected to result in the inside air temperature meeting the temperature setpoint, determines efficiency metrics each associated with one of the candidates based on historical performance data resulting from previous operation of the climate control system, and controls operation of the equipment based on a second temperature schedule selected from the candidates based on associated efficiency metrics.