HVAC System Predictive Temperature Control for Setpoint Scheduling
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Solution Overview
Problem
Conventional HVAC systems often fail to efficiently reach a set comfort temperature by a desired time, leading to user discomfort and unnecessary energy consumption, as they lack the ability to dynamically predict indoor air temperature and adjust operations accordingly.
Innovation Solution
An HVAC system with a processor that dynamically predicts indoor air temperature based on the least-correlated variables, determining and implementing an operation schedule to reach a setpoint by a specified time, while also considering occupancy status to adjust the HVAC operation schedule.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional HVAC systems operate based on fixed schedules or simple thermostat control, then the system operation is simple and reliable, but the system cannot efficiently reach the set comfort temperature by the desired time, leading to user discomfort and unnecessary energy consumption
Solution Approach 1:
The system performs preliminary actions by predicting future indoor air temperature and determining an operation schedule in advance. The controller uses historical data and environmental factors to forecast temperature trends, then proactively adjusts HVAC operation to ensure the setpoint is reached by the desired time without unnecessary energy consumption.
Solution Approach 2:
The system transitions from static, fixed schedules to dynamic prediction-based control. The controller continuously updates temperature predictions based on changing environmental conditions, occupancy patterns, and historical data, allowing the HVAC system to adapt its operation in real-time to optimize both comfort and energy efficiency.
2Measurement precision
If the HVAC system uses multiple lag values for prediction, then the prediction accuracy improves, but the complexity of data processing increases
Solution Approach 1:
The system applies partial action by selectively using only the most relevant lag values for prediction. Rather than processing all available historical data equally, the controller identifies and utilizes key lag values that have the greatest impact on prediction accuracy, reducing computational complexity while maintaining predictive performance.
3Loss of energy
If the HVAC system dynamically adjusts operation based on predicted temperature, then energy efficiency improves, but the system complexity increases
Solution Approach 1:
The HVAC system performs self-service through automated temperature prediction and schedule determination. The controller independently analyzes historical data, predicts future temperature conditions, and adjusts operation without requiring manual intervention or complex external control systems, thereby improving energy efficiency while keeping system complexity manageable.
Data Source
AI summary
A method includes receiving a setpoint and a time of interest indicating a time in the future when the setpoint is to be reached and obtaining a first data set comprising a plurality of lag values, the plurality of lag values associated with one or more variables related to the HVAC system. The method further includes selecting a second data set comprising a subset of the lag values from the first data set and determining a predicted condition at the time of interest based at least in part on the lag values in the second data set. The method further includes determining a schedule for operating heating or cooling components of the HVAC system such that the setpoint is reached by the time of interest and communicating one or more signals instructing the HVAC system to operate according to the schedule.