CN114580254A - A method and system for indoor temperature regulation of buildings based on model predictive control - Google Patents

Abstract

The invention discloses a building indoor temperature regulation and control method and system based on model predictive control, and relates to the field of indoor environment control. Firstly, an indoor temperature prediction model is established for predicting the room temperature. And establishing a cost function on the basis, wherein the cost function optimizes the cooling capacity of the air conditioning system to reduce the energy consumption of the system while ensuring the indoor temperature. And performing rolling optimization on the function by using a particle swarm algorithm to minimize the value in the predicted time domain and generate a control sequence of the cooling capacity. And controlling each device at the current moment according to the first value of the sequence, and reducing overshoot and ensuring indoor temperature to the maximum extent in a control time domain. And the deviation of the indoor temperature correction prediction model is fed back at the next moment, so that the prediction accuracy is ensured, the rolling optimization is repeated, and the system stability is enhanced. The invention distinguishes the current method for controlling the indoor temperature based on the feedback of the system temperature difference or pressure difference, reduces the energy consumption of the air conditioning system on the premise of improving the indoor thermal comfort and has good regulation and control quality on the indoor temperature and the cooling capacity.

Description

A method and system for indoor temperature regulation of buildings based on model predictive control

technical field

The present invention relates to the field of indoor environment control, and more particularly, to a method and system for regulating indoor temperature in buildings based on model predictive control.

Background technique

According to the statistics of the Energy Agency, building energy consumption accounts for 40% of the total energy consumption, and in the proportion of building energy consumption, the energy consumption of the air conditioning system is the main part of the building energy consumption, accounting for about 50% of the total building energy consumption. With the increasing demands on building comfort and energy saving, how to control the air-conditioning system to save energy as much as possible under the condition of thermal comfort is becoming more and more important.

The traditional PID feedback control strategy has shortcomings such as large overshoot, slow adjustment speed and poor stability, and cannot balance comfort and energy consumption. On the premise of ensuring human comfort for a period of time in the future, the required cooling/heat supply and control strategies are also changing. By rolling optimization of control variables, it is possible to achieve the purpose of reducing energy consumption on the premise of satisfying comfort.

SUMMARY OF THE INVENTION

The method and system for regulating indoor temperature in buildings based on model predictive control proposed by the present invention are theoretically different from the currently commonly used PID feedback control method, and realize the control of air-conditioning supply/cooling heat and building load under the condition of indoor thermal comfort. Dynamic matching achieves the maximum system energy-saving effect and realizes the optimal control of the air-conditioning system equipment.

The specific implementation steps of a building indoor temperature control method and system based on model predictive control proposed by the present invention are as follows:

Step 1. Establish a building indoor temperature prediction model according to the historical data of the air-conditioning system, and calculate the indoor temperature prediction value in the future under different cooling capacity and weather forecast data conditions.

y*(k+1)=a×u(k)+b×Q(k)+c×y(k)

Among them, y ^* (k+1) is the predicted value of the indoor temperature at time k+1, a, b, and c are the parameters to be identified, u(k) is the cooling capacity at time k, and Q(k) is the Building load under meteorological data, y(k) is the indoor temperature at time k.

Step 2. On the basis of the indoor temperature prediction model, the cost function J(k) is established on the basis of collecting the performance parameters of the air-conditioning system equipment. This function optimizes the cooling capacity of the air-conditioning system as much as possible to reduce the system temperature while ensuring the indoor temperature. energy consumption.

Among them, N is the prediction time domain that can be set by yourself, q is the temperature error weight coefficient, and y _set is the indoor temperature temperature setting value.

The first item on the right represents the cost of the temperature output error, which forces the output of the air conditioning system to be as close to the indoor temperature setpoint as possible; the second item on the right represents the cost of the change in the cooling capacity control variable, smoothing the control variable as much as possible and reducing the system energy consumption.

Step 3. In each control cycle, use the particle swarm algorithm to predict the indoor temperature and optimize the cost function rolling, so that the objective function J(k) in the prediction time domain N is minimized, and under the constraints of equipment performance parameters, the cooling supply is generated. The control sequence of the set value of the quantity, output the first u value to execute.

Step 4. According to the set value, the on-site controller optimizes the control of each equipment of the air-conditioning system, adjusts the cooling capacity of the air-conditioning system to the set value, and ensures that the overshoot is minimized and the indoor temperature is adjusted within the control time domain.

Step 5. Collect the building indoor temperature in real time for feedback correction, correct the deviation of the prediction model, ensure the accuracy of the prediction result and enhance the stability of the control system.

In general, compared with the prior art, the above technical solution conceived by the present invention can carry out model prediction control on the indoor temperature of the building, and can directly control the indoor temperature relative to the constant water supply temperature and constant pressure difference control, which is different from the traditional one. Compared with PID feedback control, indoor temperature model predictive control has the characteristics of feedforward control with small overshoot and no steady-state error.

Description of drawings

FIG. 1 is a flow chart of a method and system for regulating indoor temperature in a building based on model predictive control of the present invention.

FIG. 2 is a system diagram of an embodiment of the present invention.

Fig. 3 is the embodiment of the present invention and PID indoor temperature simulation regulation effect comparison diagram

Fig. 4 is the embodiment of the present invention and PID indoor temperature experiment control effect comparison diagram

Fig. 5 is the embodiment of the present invention and the simulation effect diagram of PID energy consumption

Fig. 6 is the embodiment of the present invention and the PID energy consumption experiment effect diagram

Detailed ways

In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention, but not to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

With reference to accompanying drawing 1, a kind of building indoor temperature regulation method and system based on model predictive control of the present invention mainly comprises the following steps:

(1) Collect historical data of the air conditioning system, including supply and return water temperature, flow rate, indoor temperature, building structural parameters, historical outdoor dry bulb temperature and relative humidity data. Use the supply and return water temperature and flow to calculate the cooling capacity at different times, the outdoor dry bulb temperature, relative humidity, solar irradiance and building structure parameters to calculate the building cooling load at different times, and finally form the training sample data with the indoor temperature at the corresponding time. The algorithm trains the following formula.

y*(k+1)=a×u(k)+b×Q(k)+c×y(k)

Among them, y ^* (k+1) is the predicted value of the indoor temperature at time k+1, a, b, and c are the parameters to be identified, u(k) is the cooling capacity at time k, and Q(k) is the Building load under meteorological data, y(k) is the indoor temperature at time k.

Finally, the trained prediction model of this embodiment is as follows.

Among them, C _p , V, ρ represent the specific heat capacity, volume and density of the building, Q represents the cooling load, the ΔT control step is 1 hour, y* represents the predicted indoor temperature, and y represents the real-time indoor temperature.

(2) On the basis of the indoor temperature prediction model, the performance parameters of the air-conditioning system equipment are collected, mainly the cooling capacity of each chiller. A constrained cost function J(k) is established, which optimizes the cooling capacity of the air-conditioning system as much as possible to reduce system energy consumption while ensuring the indoor temperature.

The cost function is:

Among them, the operating time of the air conditioner is from 8:00 to 19:00, the prediction time domain N=12, the indoor temperature y _set = 25°C q=[10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10 ,2].

The constraints are: u _min ≤ u(k+i) ≤ u _max , i=0, 1, ..., N-1

Among them, u _min and u _max are the minimum and maximum cooling capacity of the air-conditioning system, respectively, 0kW and 40kW in this example.

(3) In each control cycle, the particle swarm algorithm is used to predict the indoor temperature and the cost function rolling optimization, so that the cost function J(k) in the prediction time domain N is minimized, and the control sequence U of the cooling capacity setting value is generated. , output the first value u(k) and execute. The objective function of the particle swarm optimization rolling optimization in this embodiment is:

(4) The air-conditioning system optimizes the control of each unit according to the set value. First, the number of chillers to be turned on is determined according to u(k), and secondly, the particle swarm algorithm is used to calculate the optimal supply under the cooling capacity with the energy consumption as the goal. Return water temperature and flow. Finally, the on-site controller starts and stops the group, sets the chilled water supply and return water temperature, and controls the electric control valve and water pump to adjust the system flow to ensure that the overshoot is minimized and the indoor temperature is satisfied within the control time domain.

(5) When the next moment starts, the indoor temperature of the building is collected for feedback correction, the deviation of the prediction model is corrected, and the rolling optimization is performed again.

e(k+1)=y(k+1)-y ^* (k+1)

Among them, y(k+1) is the actual value of the indoor temperature at time k+1, and y* is the predicted value at time k+1. This deviation e is used to correct predictions for future moments.

Y*(k+1)=y*(k+1)+e(k+1)

Among them, Y*(k+1) is the correction value of indoor predicted temperature at time k+1.

The comparison of simulation results is shown in Figure 3 and Figure 5. The indoor temperature of MPC reaches the set point of 25 °C 1 hour earlier than that of PID; MPC has almost no overshoot phenomenon, PID has a large amount of overshoot, and its stability is worse than MPC. The calculated average indoor temperatures of MPC and PID are 25.1°C and 25.3°C, respectively. Compared with PID cooling tower, MPC saves energy by 15%, water pump by 16%, chiller by 13.9%, and finally total energy consumption by 14.8%. The comparison of experimental results is shown in Figure 4 and Figure 6. The indoor temperature of MPC reaches the set point of 25 °C about 1.5 hours earlier than that of PID. There is almost no overshoot in MPC, and there is a supercooling overshoot of 24.2 °C in PID. Compared with the PID control strategy, the cooling tower can save 8.9%, the water pump can save 7.3%, the chiller can save 8.6%, and the total energy consumption can be reduced by 8.1%. Therefore, both simulation and experiments show that the indoor temperature and energy consumption regulation effect of MPC is better than the traditional PID strategy.

To make it easier for those skilled in the art to understand, the above are only preferred embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present invention etc., should be included within the protection scope of the present invention.

Claims (9)

1. a building indoor temperature control method and system based on model predictive control, is characterized in that comprising the steps: Step (1), establish a building indoor temperature prediction model according to the historical data of the air-conditioning system, and calculate the indoor temperature prediction value in the future time under different cooling capacity and weather forecast data conditions; In step (2), a cost function J(k) is established on the basis of the indoor temperature prediction model and the performance parameters of the air-conditioning system. This function optimizes the cooling capacity of the air-conditioning system as much as possible to reduce the system energy while ensuring the indoor temperature. consume; Step (3), in each control cycle, use the intelligent algorithm to predict the indoor temperature and optimize the cost function rolling, so that the objective function J(k) in the prediction time domain is minimized, and under the constraints of equipment performance parameters, the cooling capacity is generated. The control sequence of the set value, output the first value to execute; In step (4), the on-site controller performs optimal control on each equipment of the air-conditioning system according to the set value, and adjusts the cooling capacity of the air-conditioning system to the set value to ensure that the overshoot is reduced to the greatest extent and the indoor temperature is adjusted within the control time domain; In step (5), the indoor temperature of the building is collected in real time for feedback correction, and the deviation of the prediction model is corrected, so as to ensure the accuracy of the prediction result and enhance the stability of the control system. 2. a kind of building indoor temperature regulation method and system based on model predictive control as claimed in claim 1, is characterized in that, described air-conditioning system historical data comprises supply and return water temperature, flow rate, indoor temperature, building structure parameter, outdoor Dry bulb temperature, relative humidity data and solar irradiance. 3 . The method and system for regulating indoor temperature in buildings based on model predictive control according to claim 1 , wherein the weather forecast data includes outdoor dry bulb temperature, relative humidity and solar irradiance. 4 . 4. a kind of building indoor temperature regulation method and system based on model predictive control as claimed in claim 1 is characterized in that, described feedback correction comprises real-time building indoor temperature, outdoor dry bulb temperature, relative humidity and total solar irradiance . 5. a kind of building indoor temperature control method and system based on model predictive control as claimed in claim 1, is characterized in that, described indoor temperature prediction model adopts mechanism method to establish, utilizes genetic algorithm and air-conditioning system historical data identification model For parameters a, b, c, the accuracy of the prediction model is within ±10%. The formula is as follows: y ^* (k+1)=a×u(k)+b×Q(k)+c×y(k) Among them, y ^* (k+1) is the predicted value of indoor temperature at time k+1, u(k) is the cooling capacity at time k, Q(k) is the building load under the meteorological data at time k, y(k ) is the indoor temperature at time k. 6. The method and system for indoor temperature regulation and control based on model predictive control according to claim 1, wherein the equipment parameters include cooling/heat, constant/variable frequency and input power, each Pump head, flow and input power. 7. The method and system for regulating indoor temperature in buildings based on model predictive control as claimed in claim 1, wherein the cost function J(k) is established as follows

Among them, N is the prediction time domain that can be set by yourself, q is the temperature error weight coefficient, and y _set is the indoor temperature temperature setting value. The first item on the right represents the cost of the temperature output error, which forces the output of the air conditioning system to be as close to the indoor temperature setpoint as possible; the second item on the right represents the cost of the change in the cooling capacity control variable, smoothing the control variable as much as possible and reducing the system energy consumption. 8. a kind of building indoor temperature regulation method and system based on model predictive control as claimed in claim 1, is characterized in that, the concrete implementation mode of described step (3) is: At time k, the particle swarm algorithm is used to globally optimize the cost function J(k) at the next N time, and the minimum value of J(k) is calculated. The optimized variable in J(k) is the cooling capacity u, and the output of the u sequence is obtained. A u(k) is executed at time k. 9. a kind of building indoor temperature regulation method and system based on model predictive control as claimed in claim 1, is characterized in that, the concrete implementation mode of described step (4) is: According to the set value of cooling capacity u(k), the operating parameters of the current air-conditioning system are optimized, and the number of chillers to be turned on at this set value of cooling capacity is determined mainly based on energy consumption, and the temperature of supply and return water is optimized by particle swarm algorithm. Set point and chilled water flow to ensure the lowest operating energy consumption. According to the optimized parameters, the on-site actuator execution mainly includes starting and stopping the unit and setting the water supply or return water temperature of the unit. The DDC controller adjusts the cooling capacity by controlling the bypass flow of the electric regulating valve and the frequency of the water pump.

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