HVAC Start/Stop Control Using Weather Forecasting and Thermal Modeling
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Solution Overview
Problem
Existing HVAC system control strategies fail to account for zone and outdoor air temperatures forecasting and HVAC equipment efficiency, leading to comfort violations and increased energy usage.
Innovation Solution
A control system with a processor-based controller that determines setpoints and start/stop times for HVAC components based on predicted weather conditions and actual room air conditions, optimizing the operation of HVAC equipment to maintain comfort and reduce energy consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If fixed start/stop schedules are used for HVAC systems, then operation simplicity is maintained, but energy usage increases and comfort violations occur
Solution Approach 1:
The system performs preliminary actions by predicting future weather conditions and pre-calculating optimal HVAC start/stop times before the actual occupancy period begins. The controller uses forecasted weather data to determine when to start the HVAC system in advance, ensuring comfort requirements are met while avoiding unnecessary early startup that would waste energy.
Solution Approach 2:
The system transitions from static fixed schedules to dynamic adaptive scheduling. The HVAC control strategy dynamically adjusts start/stop times based on real-time weather forecasts, occupancy patterns, and thermal model predictions. This dynamic approach allows the system to adapt to changing environmental conditions and optimize energy consumption while maintaining comfort.
2Device complexity
If gradient method with single linear approximation is used, then calculation complexity is reduced, but prediction accuracy deteriorates leading to comfort violations
Solution Approach 1:
The system changes the parameters used for temperature prediction from simple linear approximations to multi-factor thermal models that incorporate weather forecasts, building envelope properties, internal heat gains, and HVAC system characteristics. By changing the predictive parameters to include these additional factors, the system achieves higher accuracy without excessive complexity.
Solution Approach 2:
The system implements feedback mechanisms where actual temperature measurements are compared with predicted temperatures, and the thermal model parameters are adjusted accordingly. This feedback loop continuously refines the prediction accuracy by learning from past performance and adapting to actual building behavior patterns.
3Loss of energy
If unoccupied setpoint adjustment method is used, then energy savings during unoccupied periods are achieved, but comfort requirements at occupancy start/end are not met
Solution Approach 1:
The system performs preliminary cooling or heating actions during unoccupied periods based on predicted weather conditions and occupancy schedules. By pre-conditioning the building space before occupants arrive, the system can use higher setpoints during unoccupied periods (saving energy) while still achieving comfortable temperatures at the start of occupancy. The preliminary action accounts for thermal mass and heat transfer dynamics.
Solution Approach 2:
The thermal model acts as an intermediary that bridges the gap between unoccupied period energy savings and occupied period comfort requirements. It calculates the optimal setpoint trajectories during unoccupied periods that balance energy savings with the ability to reach comfort setpoints by occupancy start time, considering building thermal dynamics and weather forecasts.
4Loss of energy
If weather forecasting and thermal modeling are implemented, then energy optimization is improved, but system complexity increases
Solution Approach 1:
The controller is designed as a multi-functional device that integrates weather data acquisition, thermal modeling, prediction algorithms, and HVAC control functions in a single system. By making the controller universal and capable of performing multiple functions, the system achieves energy optimization through weather forecasting and thermal modeling without proportionally increasing overall system complexity.
Solution Approach 2:
The system performs self-service by automatically acquiring weather forecasts, calculating optimal schedules, and controlling HVAC operations without requiring external intervention or complex manual programming. The thermal model and control algorithms are self-contained within the controller, which autonomously makes decisions based on current conditions and predictions, reducing the operational complexity burden.
Data Source
AI summary
A control system for an HVAC system having at least one HVAC component, the control system comprising: a controller having a processor and a memory, the controller in signal communication with the at least one HVAC component, the controller configured to: determine a startup/shut-down setpoint and the time associated with a beginning or an end of a building occupancy period; determine a predicted weather condition for outside air at a location of the HVAC system; predict a set of indoor air conditions over the period from the current time until the building being occupied/unoccupied based on the determined setpoint and time and the predicted weather condition; and start/stop the at least one HVAC component when an actual room air condition approaches the predicted indoor air condition.