Self-adaptive smart setback control system
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Solution Overview
Problem
Conventional HVAC systems operate inefficiently due to deviations in user settings and external environmental conditions, leading to excessive energy expenditure and increased costs, as they rely solely on pre-set schedules without adapting to varying indoor and outdoor conditions.
Innovation Solution
A self-adaptive smart setback HVAC control system that learns indoor temperature recovery rates from historical data to determine optimal start-up times for the HVAC unit, adjusting operations based on current conditions and scheduled occupancy, using a temperature setback unit to change internal air temperature to a comfort setpoint at a calculated recovery rate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If pre-set schedules are used to control HVAC operations, then ease of operation is improved, but adaptability to varying indoor and outdoor conditions deteriorates
Solution Approach 1:
The system continuously monitors actual indoor temperature, outdoor temperature, and occupancy conditions, then uses this feedback to dynamically adjust the setback schedule. The controller compares predicted temperature recovery with actual recovery rates and modifies future schedules accordingly, creating a closed-loop adaptive system that maintains ease of operation while improving adaptability.
Solution Approach 2:
The HVAC control system transitions from a static pre-set schedule to a dynamic adaptive schedule that automatically adjusts setback temperatures and recovery times based on real-time conditions. The system dynamically modifies operational parameters including temperature setpoints, timing schedules, and recovery rates according to measured environmental factors and occupancy patterns.
2Use of energy by moving object
If dynamic adjustment based on learned recovery rates is implemented, then energy efficiency is improved, but device complexity increases
Solution Approach 1:
The system performs self-learning by automatically monitoring temperature recovery patterns over multiple cycles and deriving optimal recovery rates without user intervention. The controller autonomously analyzes historical data, identifies trends in thermal mass performance, and adjusts schedules based on learned characteristics, eliminating the need for manual programming or complex user configuration while improving energy efficiency.
Solution Approach 2:
The system performs preliminary learning during initial operation cycles to establish baseline recovery rates before full adaptive control is activated. By pre-characterizing the building's thermal response during commissioning and early operation, the system reduces the complexity of real-time decision-making while maintaining energy optimization capabilities.
3Use of energy by moving object
If setback mode is used to reduce energy consumption, then energy efficiency is improved, but temperature comfort may deteriorate if recovery timing is inaccurate
Solution Approach 1:
The system replaces traditional mechanical timing mechanisms with computational prediction based on learned thermal characteristics. Instead of using fixed timers or simple temperature sensors, the controller uses software-based models that predict temperature recovery curves, allowing precise scheduling of HVAC restart times to ensure comfort requirements are met while maximizing energy savings during setback periods.
Data Source
AI summary
A self-adaptive smart setback heating, ventilation and air conditioning (HVAC) control system includes an HVAC unit configured to deliver at least one of heated air and cooled air to a target area. A temperature sensor determines a current internal air temperature (IAT) of the target area. An HVAC controller is in signal communication with the HVAC unit and the temperature sensor. The HVAC controller selectively operates in an active mode and a setback mode. The active mode controls the HVAC unit based on a first temperature setpoint value, and the setback mode controls the HVAC unit based on a second temperature setpoint value different from the first temperature setpoint value. The HVAC controller includes an electronic temperature setback unit that is configured change the IAT during the setback mode based on an actively determined upcoming temperature recovery rate.