Controlling the setback and setback recovery of a power-consuming device
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Solution Overview
Problem
Existing systems for managing the operation of power-consuming devices, such as HVAC systems, lack efficient methods to control setback and setback recovery, leading to suboptimal energy management and increased operational costs.
Innovation Solution
The system modifies setback settings based on current environmental parameters and a calculated drift value, allowing power-consuming devices to enter setback mode at advantageous times and exit more efficiently during setback recovery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If power-consuming devices are setback to reduced levels of operation to conserve energy, then energy costs are reduced, but the devices must operate at full capacity during recovery periods, increasing operational stress
Solution Approach 1:
The system performs preliminary action by pre-cooling or pre-heating spaces before setback periods, and by calculating optimal recovery start times in advance. This allows the HVAC system to enter setback mode earlier without compromising comfort requirements, reducing the duration and intensity of recovery operations.
Solution Approach 2:
The system dynamically adjusts setback duration and recovery timing based on real-time environmental parameters, drift values, and predictive algorithms. This dynamic optimization ensures that setback periods are as long as possible while still meeting comfort requirements, thereby reducing energy consumption without excessive recovery stress.
2Loss of energy
If setback recovery is delayed to maximize energy savings, then energy costs are reduced, but comfort requirements may not be met, reducing system reliability
Solution Approach 1:
The system continuously monitors environmental parameters such as temperature, humidity, and occupancy patterns. This feedback is used to adjust setback and recovery timing in real-time, ensuring that comfort requirements are always met while maximizing energy savings. The system learns from historical data to predict when recovery should begin.
Solution Approach 2:
The system performs preliminary calculations to determine the exact moment when recovery should begin to meet comfort requirements. By using drift values and environmental parameter analysis, the system can predict the precise timing needed, allowing for maximum setback duration without compromising comfort.
3Ease of operation
If conventional setback control is used without environmental parameter adjustment, then system operation is simple, but energy management is suboptimal, increasing energy costs
Solution Approach 1:
The system performs self-service by automatically calculating drift values, analyzing environmental parameters, and optimizing setback/recovery timing without requiring manual intervention. The controller autonomously adjusts settings based on real-time data, making the complex energy optimization transparent to the user while achieving superior energy management.
Solution Approach 2:
The system dynamically changes operational parameters such as setback start time, duration, and intensity based on environmental conditions. By adjusting these parameters in real-time rather than using fixed schedules, the system achieves optimal energy management while maintaining simple operation for the end user.
Data Source
AI summary
Systems and methods are provided for controlling a setback mode of a power-consuming device, and for controlling setback recovery of power-consuming devices, in order to make setback and setback recovery more dynamic based on current environmental parameters and previous observed operating parameters, in order to enable more efficient operation of power-consuming devices resulting in reduced energy costs and increased power efficiency.