Smart thermostat with model predictive control
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Solution Overview
Problem
Conventional thermostats operate using fixed temperature setpoint schedules, leading to suboptimal control of HVAC equipment and increased energy costs, as they do not account for time-varying energy prices and zone heat transfer characteristics.
Innovation Solution
A smart thermostat with a model predictive controller that determines optimal temperature setpoints by generating a cost function using predictive models to account for time-varying utility rates and heat transfer dynamics, optimizing energy usage while maintaining desired temperature ranges.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a fixed temperature setpoint schedule is used, then the thermostat operation is simple, but the energy cost increases due to suboptimal HVAC control
Solution Approach 1:
The patent implements dynamic temperature setpoints that automatically adjust based on predicted energy costs, weather forecasts, and building thermal characteristics. The system transitions from static fixed schedules to dynamic optimization, allowing the thermostat to adapt setpoints in real-time to minimize energy consumption while maintaining comfort constraints.
Solution Approach 2:
The system performs self-optimization by automatically determining optimal temperature setpoints without requiring manual user input. The microprocessor-based controller autonomously processes energy cost data, weather forecasts, and building models to generate optimized schedules, eliminating the need for users to manually program complex schedules.
2Use of energy by moving object
If automatic optimization is implemented, then energy cost decreases, but the device complexity increases
Solution Approach 1:
The patent replaces complex manual programming interfaces with automated electronic optimization. The microprocessor-based controller uses algorithms to automatically process multiple input parameters (energy costs, weather forecasts, building thermal models) and generate optimized schedules, substituting electronic computation for manual mechanical programming.
Solution Approach 2:
The controller integrates multiple functions into a single device: it processes energy cost data, forecasts weather conditions, maintains building thermal models, optimizes temperature schedules, and interfaces with HVAC equipment. This multi-functionality consolidates what would otherwise require separate systems into one unified controller.
3Device complexity
If fixed temperature schedules are used, then the control system is simple, but energy efficiency deteriorates
Solution Approach 1:
The system performs preliminary cooling or heating of the building thermal mass during periods of low energy costs (such as nighttime or off-peak hours). By pre-conditioning the building envelope and thermal mass in advance, the system reduces the need for intensive HVAC operation during high-cost periods, thereby improving overall energy efficiency.
Solution Approach 2:
The patent dynamically changes temperature setpoint parameters based on varying energy costs, weather conditions, and building occupancy patterns. The system adjusts multiple parameters including temperature setpoints, HVAC equipment schedules, and thermal mass utilization strategies to optimize energy efficiency under different operating conditions.
Data Source
AI summary
A thermostat for a building zone includes at least one of a model predictive controller and an equipment controller. The model predictive controller is configured to obtain a cost function that accounts for a cost of operating HVAC equipment during each of a plurality of time steps, use a predictive model to predict a temperature of the building zone during each of the plurality of time steps, and generate temperature setpoints for the building zone for each of the plurality of time steps by optimizing the cost function subject to a constraint on the predicted temperature. The equipment controller is configured to receive the temperature setpoints generated by the model predictive controller and drive the temperature of the building zone toward the temperature setpoints during each of the plurality of time steps by operating the HVAC equipment to provide heating or cooling to the building zone.