Smart Thermostat Predictive Setpoint Control for HVAC Energy Cost
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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, zone heat transfer characteristics, and other factors that affect heating and cooling costs.
Innovation Solution
A smart thermostat with a model predictive controller that determines optimal temperature setpoints by generating a cost function accounting for the cost of operating HVAC equipment over time, using predictive models to forecast temperature and optimize setpoints based on constraints such as time-varying utility rates and heat transfer characteristics.
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 prices, thermal models, and weather forecasts. The model predictive controller continuously optimizes setpoints over a time horizon, transitioning from static fixed schedules to dynamic adaptive control that responds to changing conditions while maintaining comfort constraints.
Solution Approach 2:
The system performs self-optimization by automatically determining optimal temperature setpoints without requiring user intervention. The model predictive controller uses built-in thermal models, energy price data, and weather forecasts to autonomously calculate and adjust setpoints, eliminating the need for users to manually program complex schedules while achieving cost reduction.
2Use of energy by moving object
If model predictive control with optimization is implemented, then energy cost decreases, but the device complexity increases
Solution Approach 1:
The system performs preliminary calculations by pre-computing optimal temperature setpoints over a forecast horizon using model predictive control. The controller proactively determines the entire sequence of optimal setpoints based on predicted energy prices and thermal models before execution, rather than reacting to current conditions alone, which simplifies real-time control while achieving optimization.
Solution Approach 2:
The patent introduces a thermal model as an intermediary between the controller and the building thermal mass. This model predicts future temperature behavior and energy requirements, allowing the controller to optimize setpoints based on predicted rather than actual conditions. The thermal model acts as a mediator that translates complex thermal dynamics into actionable control decisions.
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.