Predictive control of a heat pump
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Solution Overview
Problem
Controlling heat pump systems is challenging due to coupled input and output variables and nonlinear behavior, leading to inefficient operation, unstable states, and frequent freezing of heat exchangers, with existing solutions failing to integrate limiting factors accurately.
Innovation Solution
A model-based predictive control method using discrete transfer functions and CARIMA structure predicts output variables, incorporating all dynamic properties and limiting factors, allowing simultaneous control of multiple actuators to optimize Coefficient of Performance (COP) and adhere to operational limits.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If conventional control methods (on/off or modulating based on thermostat) are used, then device complexity is low, but energy efficiency deteriorates due to inability to account for heat loss/gain dynamics
Solution Approach 1:
The control system performs preliminary calculations of predicted heat loss or gain for each building zone based on historical data, weather forecasts, and building characteristics before the heating/cooling season begins. This allows the system to proactively adjust setpoints and scheduling without requiring complex real-time sensing or actuation infrastructure.
Solution Approach 2:
The patent replaces complex mechanical control systems with software-based predictive algorithms that run on standard computing hardware. The control logic uses mathematical models and optimization algorithms to determine optimal operation schedules, substituting physical control complexity with computational processing.
2Loss of energy
If real-time adaptive control is implemented, then energy efficiency improves through dynamic adjustment, but device complexity increases due to additional sensors and control algorithms
Solution Approach 1:
The system pre-calculates predicted heat loss or gain values for each zone using historical operational data, building characteristics, and weather forecasts. These preliminary calculations form the basis for determining optimal setpoints and scheduling without requiring complex real-time computation or additional sensing infrastructure.
Solution Approach 2:
The control system automatically determines optimal operation schedules and setpoints without requiring manual intervention or complex user input. The system self-adjusts based on predicted conditions, eliminating the need for sophisticated real-time user interfaces or manual calibration procedures.
3Productivity
If predictive algorithms using historical data and weather forecasts are employed, then productivity improves through optimized operation scheduling, but loss of information increases due to data processing requirements
Solution Approach 1:
The system extracts only the essential predictive information needed for control decisions from available data sources, such as weather forecasts and historical operational patterns. By focusing on key parameters rather than processing all available data, the system maintains scheduling optimization while minimizing data processing requirements.
Solution Approach 2:
The control algorithm transforms raw weather forecast data and historical operational data into simplified predictive parameters representing expected heat loss or gain. This parameter transformation reduces data complexity while preserving the essential information needed for optimal scheduling decisions.
Data Source
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AI summary
Method for regulating or controlling linear or nonlinear dynamic systems of a heat pump system with a left-handed thermodynamic Clausius-Rankine cycle, the heat pump system comprising at least one controlled, electrically driven refrigeration compressor (2) for working fluid, at least one controlled expansion valve (4) for working fluid, at least two heat exchangers (3, 5) for working fluid through which heat transfer fluids flow, at least two electrically driven and controlled conveying devices for heat transfer fluids, measuring points for state variables for working fluid and heat transfer fluids, an electronic control device, wherein all operating limits for pressure, temperature, speed and valve openings are included as limiting variables in the control, and the control is based on a model of all essential dynamic properties of the input and output variables.with which an estimate of the output variables after each control intervention is carried out, these estimated output variables of the system are included in the control and a prediction horizon (17) is determined, from which the manipulated variables of all actuators are calculated, the control of all manipulated variables takes place simultaneously, the setpoint temperature of the heat transfer fluid for the flow of the heating circuit of the heat pump system is selected as the first control variable for the other manipulated variables, the electrical energy consumption of all electrical machines as well as the total electrical energy consumption of the entire heat pump is measured and recorded, the COP value is selected as the second control variable, whereby a predetermined setpoint temperature of the heat transfer fluid for the flow of the heating circuit of the heat pump system is varied,and the COP value is calculated from the quotient of the heat output to the heat transfer fluid of the heating circuit and the sum of all electrical consumers of the heat pump system,