KR-20260062682-A - Method for Constructing an RC Model Based on Set-point Temperature for Predictive Control of an Air Handling Unit

Abstract

The present invention provides a method for constructing an RC model for a target building, comprising: a step of constructing a reference model, which is an RC model of the target building; and a step of constructing a proposed model, which is an RC model of the target building; wherein the reference model simulates the indoor temperature of the target building based on the air conditioning heat quantity, the proposed model uses the same parameters found through optimization in the reference model, and the proposed model adds an air conditioning thermal resistance between the indoor temperature and the set temperature of the target building to simulate the air conditioning heat quantity.

Inventors

• 김의종
• 김선인

Assignees

• 인하대학교 산학협력단

Dates

Publication Date

20260507

Application Date

20241029

Claims (4)

1. In the method of constructing an RC model of a target building, A step of constructing a reference model, which is an RC model of the target building; It includes the step of constructing a proposed model, which is an RC model of the target building; and The above reference model simulates the indoor temperature of the target building based on the air conditioning heat quantity, and The proposed model above uses the same parameters found through optimization in the reference model above, and The proposed model above is a method for constructing a set temperature-based RC model for predictive control of an air conditioner, wherein an air conditioning thermal resistance is added between the indoor temperature and the set temperature of the target building to simulate the air conditioning heat quantity.
2. In paragraph 1, A method for constructing a set temperature-based RC model for predictive control of an air conditioner, wherein the variable optimization of the above reference model uses a non-linear optimization algorithm with constraints, and the objective function of the above algorithm is set to minimize the error between the indoor temperature output value and the measured value of the above target building.
3. In paragraph 1, The above proposed model is a method for constructing a set temperature-based RC model for predictive control of an air conditioner, which optimizes the above-mentioned air conditioning thermal resistance.
4. In paragraph 3, Optimization of the above air conditioning thermal resistance is a method for constructing a set temperature-based RC model for predictive control of an air conditioner, which defines variables affecting the air conditioning thermal resistance through correlation analysis between variables.

Description

Method for Constructing an RC Model Based on Set-point Temperature for Predictive Control of an Air Handling Unit The present invention relates to a method for constructing an RC model by computer, and more specifically, to a method for constructing a set temperature-based RC model for air conditioner predictive control that improves the accuracy of indoor temperature prediction and is more suitable for set temperature control by explicitly linking the set temperature to the RC model. The air handling unit (AHU), a core component of the heating, ventilation, and air conditioning (HVAC) system, controls the heat output based on a set temperature to ensure the indoor temperature reaches a comfortable level of indoor air. As the importance of optimal control for HVAC systems grows, numerous studies are being conducted on Model Predictive Control (MPC), which operates air conditioners based on predictions from building load models. To effectively implement MPC, a load model that accurately describes the physical phenomena of the building must first be constructed. Among building models, RC models have the advantage of being able to be constructed with relatively little data because they are built based on physical principles and operational data. RC models generally calculate indoor temperature based on the amount of heat entering or removing through air conditioners, and use this HVAC heat quantity as a control variable for the model. However, since actual air conditioners use the set temperature rather than the cooling heat capacity as the control variable, there are limitations to applying the control scenarios of existing RC models as is. This approach leads to a discrepancy between the control variables used in RC models and those used in actual HVAC systems. In practice, AHUs control the set-point temperature, whereas conventional RC models use the heat output of the air conditioning system as the control variable. This discrepancy implies that control strategies developed in RC models cannot adequately reflect the physical changes in thermal energy actually occurring. In practice, the control applied to AHUs typically involves adjusting indoor set temperatures for heating and cooling; when the RC model is configured in this way, it is cost-effective and easy to implement as it does not require significant modifications to existing building energy management systems. Therefore, existing RC models without a set temperature node must be based on the assumption that the indoor temperature is similar to the set temperature. However, in reality, the indoor temperature and the set temperature are not the same. As shown in Fig. 1, there is a time delay after the AHU starts operating until the indoor temperature reaches the set temperature. This delay is called the set-up time and is particularly pronounced during the initial operation of the AHU due to the building's thermal capacity, resulting in a significant difference between the indoor temperature and the set temperature. Therefore, it is important to consider these differences when developing optimal control strategies. When the set temperature is not explicitly specified, it is difficult for conventional RC models to represent the difference between the set temperature and the room temperature in dynamic environments. In this invention, a new RC model was developed to improve the accuracy and efficiency of existing RC models while simultaneously resolving the discrepancy between the model's control input and the actual AHU operation. Figure 1 shows the change in indoor temperature according to the air handling unit (AHU) operating conditions and set temperature. Figure 2 shows the proposed model structure and development process based on the set temperature of the present invention. FIG. 3 shows the structure and components of the reference model of the present invention. FIG. 4 shows the structure and components of the proposed model of the present invention. Figure 5 is an algorithm for calculating the output of a linear time-varying model. FIG. 6 is a schematic diagram of the changes in input/output variables and matrix structure from the reference model of the present invention to the proposed model. FIG. 7 is a schematic diagram of a test air handling unit (AHU) of the present invention. FIG. 8 shows measurement data and weather data at the control point of the present invention. Figure 9 shows the results of the correlation analysis between the AHU heating speed and related variables of the present invention. Figure 10 shows the indoor temperature and AHU heating speed prediction results using the proposed model of the present invention. Figure 11 shows the relationship between the temperature difference and the air conditioning thermal resistance in the air conditioning heat quantity calculation formula of the present invention. FIG. 12 shows the results of changes in indoor temperature and AHU heating speed according to the set temperature adjustm