Thermostat with estimation of run-time savings
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Solution Overview
Problem
Current HVAC systems lack an efficient method to determine and adjust setpoints or schedules to maximize energy savings, leading to suboptimal energy usage and increased costs.
Innovation Solution
A method involving processing circuits to estimate the steady-state gain of HVAC systems, calculate actual runtime, predict runtime reduction for alternative setpoints or schedules, and adjust the original setpoints or schedules based on these calculations, displayed to users and potentially adjusted automatically for energy savings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional HVAC control methods are used, then system operation is simple, but energy savings are suboptimal
Solution Approach 1:
The system performs preliminary characterization of the HVAC system by estimating steady-state gain and time constant parameters before optimization. This preliminary action creates a mathematical model of the system that enables future predictive calculations of runtime reductions, allowing the system to prepare optimization strategies in advance rather than reacting to energy consumption in real-time
Solution Approach 2:
The system implements feedback by continuously monitoring actual HVAC equipment runtime and comparing it against predicted runtime values. The feedback loop uses actual runtime data to refine steady-state gain estimates and improve future predictions, creating a self-correcting system that progressively enhances energy savings while maintaining accurate computational models
2Use of energy by moving object
If setpoints are adjusted to reduce runtime, then energy consumption decreases, but temperature control precision may be compromised
Solution Approach 1:
The system changes parameters by adjusting setpoints based on dynamically calculated optimal values that account for steady-state gain and predicted runtime reductions. Rather than using fixed or manually set setpoints, the system continuously modifies temperature setpoints to achieve the optimal balance between energy consumption and temperature control precision, using mathematical models to ensure precision is maintained
Solution Approach 2:
The system applies dynamics by making setpoints adaptive rather than static. The optimal setpoints are continuously adjusted based on current conditions, predicted runtime reductions, and actual performance feedback. This dynamic approach allows the system to respond to changing environmental conditions and system states, maintaining temperature control precision while maximizing energy savings
3Measurement precision
If steady-state gain is estimated using multiple parameters, then prediction accuracy improves, but computational complexity increases
Solution Approach 1:
The system applies partial action by using a focused set of critical parameters (steady-state gain and time constant) rather than attempting to model all possible HVAC system variables. This selective approach captures the essential dynamics needed for accurate runtime predictions while avoiding the computational burden of comprehensive system modeling, achieving sufficient precision with manageable complexity
Data Source
AI summary
A space controller and method for determining energy savings of the space controller are provided. The method includes estimating, by one or more processing circuits, a steady state gain of a controlled variable of a heating, ventilation, and air conditioning (HVAC) system; determining, by the one or more processing circuits, an actual runtime that HVAC equipment of the HVAC system is in an on state based on a first setpoint or schedule; predicting, by the one or more processing circuits, a reduction of runtime that the HVAC equipment is in the on state for a second setpoint or schedule based on the actual runtime and the steady state gain; and adjusting, by the one or more processing circuits, the first setpoint or schedule to the second setpoint or schedule based on the reduction of runtime.