CN116989379A - Room temperature control method based on dynamic room temperature set value and model predictive control - Google Patents
Room temperature control method based on dynamic room temperature set value and model predictive control Download PDFInfo
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Abstract
The invention belongs to the technical field of heat supply room temperature control, and discloses a room temperature control method based on dynamic room temperature set value and model predictive control. The invention relates to a multi-objective optimization model which uses time-by-time heat load to represent energy consumption and PMV-PPD to represent indoor thermal comfort, and uses genetic algorithm to solve the room temperature set values at different time intervals in one day, the method can make a room temperature set value scheme which is energy-saving and comfortable in advance according to weather forecast; and the heating room temperature is regulated and controlled by adopting a model predictive control method so as to ensure that the room temperature changes according to the track of the dynamic room temperature set value. The method of the invention technically completes the establishment of a comfortable energy-saving room temperature set value scheme according to weather forecast on the hot user side of the heating system, realizes the comfort and energy-saving room temperature control effect on application, and enables the central heating system to continuously realize the optimization of a large-scale room temperature set value.
Description
Technical Field
The invention belongs to the technical field of heat supply room temperature control, and particularly relates to a room temperature control method based on dynamic room temperature set value and model predictive control.
Background
The central heating system is one of urban infrastructures and is one of urban modernization level marks, and along with the continuous increase of heating areas, the problems of poor thermal comfort and energy waste caused by overhigh indoor temperature are more and more emphasized, and the proper reduction of indoor temperature set values can reduce energy consumption and keep the thermal comfort of indoor personnel within an allowable range.
In order to ensure that the indoor temperature of a heat user meets the requirement, many heat supply enterprises generally perform design calculation according to the maximum value of an indoor temperature set value in a standard, but the design calculation is not energy-saving, and dynamic indoor temperature set values are prepared in advance along with weather changes, so that the heat comfort can be ensured, and the heating energy consumption can be reduced. However, how to make the heating system automatically roll and make the dynamic room temperature setting value of one day in the future according to weather forecast, and ensure that the thermal comfort of indoor heat users is within the allowable range, becomes a key problem for realizing the dynamic room temperature setting value strategy, and meanwhile, how to ensure that the room temperature changes according to the track of the dynamic room temperature setting value becomes a key problem for realizing the dynamic room temperature setting value.
Disclosure of Invention
In order to solve the problems, the invention particularly provides a room temperature control method based on dynamic room temperature set values and model predictive control, which relates to a multi-objective optimization model with time-by-time heat load representing energy consumption and PMV-PPD representing indoor heat comfort, and solves the room temperature set values at different time periods in one day by using a genetic algorithm, and the method can be used for preparing a scheme of the energy-saving and comfortable room temperature set values in advance according to weather forecast; and the heating room temperature is regulated and controlled by adopting a model predictive control method so as to ensure that the room temperature changes according to the track of the dynamic room temperature set value.
In order to achieve the above purpose, the technical scheme of the invention is as follows:
the room temperature control method based on dynamic room temperature set value and model predictive control comprises the following steps:
s1, determining a heating building or a heating room, and establishing a time-by-time heat load prediction model of the heating building or the heating room;
the time-by-time thermal load prediction model selects a neural network prediction model, training data is obtained through energy consumption simulation software or actual operation data is collected, and in order to improve the sensitivity of the time-by-time thermal load prediction model to a room temperature set value, the room temperature set value in the training data needs to be changed at 18-24 ℃;
the input variables of the time-by-time thermal load prediction model are: indoor design temperature; whether the current moment is working time or not; outdoor dry bulb temperature; solar radiation amount; the personnel room rate; the utilization rate of the lamplight; heat load values of 1h, 2h and 24h before the current moment;
the output variable of the time-by-time thermal load prediction model is the time-by-time thermal load value of the next moment;
s2, forming a multi-objective optimization model by using the time-by-time thermal load prediction model and the simplified PPD calculation program, and determining a solving method;
the reasonable and simplified method for the variables in the PPD calculation program comprises the following steps: the mechanical work W of human body is 0 and the basic thermal resistance of clothesI cl 1.4clo, human metabolism rate M1 Met, average radiation temperature T mrt Is lower than room temperature by 2 ℃ and indoor air humidity p a The indoor air flow velocity v is 0.07m/s for the room temperature Tn and the water vapor partial pressure at the relative humidity of 40%; the simplified PPD calculation program is noted as:
PPD＝f(T n ) (1)
the time-by-time thermal load prediction model and the simplified PPD calculation program form a multi-objective optimization model,
in the formula (2), f 1 F is the room heat load index 2 Is a room thermal comfort index;the sum of the heat load predicted values at the dynamic room temperature set value, namely the energy consumption predicted value of the whole day, kWh is within 24 hours; q (Q) i For the time-by-time thermal load prediction model in step S1, in actual use, Q i All variables required by the time-by-time thermal load prediction model mentioned in the step S1 are input, wherein the meteorological parameters are obtained by meteorological prediction; />PPD, the average percent predicted dissatisfaction for people in a day i Calculated by the formula (1);
the method comprises the steps of determining a decision variable of a multi-objective optimization model to be a room temperature set value, simultaneously prescribing that the room temperature set value is not changed within 3 hours, wherein the value range is {18 ℃,19 ℃,20 ℃,21 ℃,22 ℃,23 ℃,24 ℃, and the solving method is used for solving a genetic algorithm;
s3, designing and adding a model predictive controller for a heating building or a heating room;
adding a model predictive controller for each building or adding a model predictive controller for each heat user, and adding according to the needs of the heat user;
the predictive model selects either a white, black or gray box model, but needs to fulfil the following functions: inputting the current room temperature, the current meteorological conditions and the hot water flow of the radiator, and outputting the indoor temperature at the next moment;
the rolling optimization of the model predictive controller aims at obtaining a room temperature set value by minimum room temperature fluctuation and water pump conveying energy consumption tracking optimization, and an objective function is expressed as,
where N is the prediction horizon, k=0 is defined as the sampling instant, each time the scroll optimization starts with k=0, y k Room temperature predictor for the kth time step, y 0 For the actual measurement of the obtained room temperature true value, P k The energy consumption of the variable-frequency water pump in the kth time step in the time domain is predicted; 0.5 is a weight coefficient of the room temperature fluctuation and the energy consumption of the variable-frequency water pump;
in the rolling optimization of the model predictive controller, the predicted time-domain hot water flow sequence is represented by U, and the optimizing range of each component U is as follows:
0≤u≤u max (4)
wherein u is max Maximum flow allowed for the radiator;
the constraint conditions in the rolling optimization of the model predictive controller are:
18≤y k (5)
y s -△t≤y k ≤y s +△t (6)
wherein y is k The room temperature at any moment is larger than 18 ℃ specified by the specification for the room temperature predicted value of the kth time step; y is s Setting the temperature of the room at the moment, wherein Deltat is the deviation of the allowable fluctuation;
the internal logic of the model predictive controller is a rolling optimization process between the solving of a genetic algorithm and the sampling moment; the genetic algorithm is mainly used for solving the optimal hot water flow sequence at each sampling moment: firstly, randomly generating different hot water flow sequences through an SEGA algorithm, then inputting the hot water flow sequences into a prediction model together with a real value of the room temperature and a weather forecast, and obtaining a room temperature prediction track corresponding to the hot water flow sequences through circularly using the prediction model; then, the room temperature predicted track and the hot water flow are brought into an objective function in rolling optimization to calculate the fitness until an optimal hot water flow sequence and a corresponding room temperature predicted track with optimal cost are found; finally, the solution at the moment is completed by adjusting the frequency of the water pump to enable the flow of the hot water flowing through the radiator to be equal to the first item of the optimal hot water flow sequence; the heating building or the heating room radiates heat through a radiator for a period of time, the room temperature is changed, the real room temperature is input into a prediction model at the next sampling moment, and the genetic algorithm is used for solving again, so that rolling optimization is completed;
s4, inputting the duty ratio of the energy consumption and the thermal comfort index into the SEGA optimization model according to preference by a user to obtain an optimal room temperature set value track T;
the user's preference for both objective functions will ultimately determine the decision variable choices, and to derive the final decision variable according to the user's preference, normalize each objective function and then weight it:
wherein i is 1 or 2,lambda is the energy consumption index and the thermal comfort index respectively i Weights of the corresponding objective functions set for the user;
and S5, the model predictive controller controls the indoor temperature of the heating building or the heating room to change according to the optimal room temperature set value track T.
Further, the room temperature control method of the present invention is required to be performed at 20:00 a night to 8 a day: 00, the calculation is carried out, and the calculation content is 8 on the next day: 00-next day 20: the optimum room temperature setpoint trajectory T for period 00.
The invention has the beneficial effects that: the invention particularly provides an operation step and an implementation scheme of a room temperature control method based on dynamic room temperature set values and model predictive control, which technically complete the establishment of an energy-saving and comfortable room temperature set value scheme in advance on the hot user side of a heating system according to weather forecast, realize the room temperature control effect of comfort and energy conservation on application, and enable a central heating system to continuously realize the optimization of a large-scale room temperature set value. Firstly, the implementation of the method can reduce the heating energy consumption by reasonably reducing the set value of the room temperature in a certain period; then, the design method of the model predictive controller provided by the invention can be matched with the dynamic room temperature set value strategy provided by the invention, so that the room temperature can be changed according to the dynamic room temperature set value; finally, the room temperature control method based on the dynamic room temperature set value and model predictive control can further reduce energy consumption, realize energy conservation and emission reduction, and lay a foundation for the establishment and realization of the dynamic room temperature set value of a heat supply user.
Drawings
Fig. 1 is a schematic diagram of the construction and operation of a room temperature control method based on dynamic room temperature set values and model predictive control according to the present invention.
FIG. 2 is a graph of the predicted effect of an example time-by-time thermal load prediction model.
FIG. 3 is a block diagram of the internal logic of a model predictive controller for an example.
In the figure: 1-a room temperature set value; 2-room temperature realism value; 3-weather forecast; 4-SEGA genetic algorithm; 5-a room temperature prediction model; 6-hot water flow sequence; 7-meteorological data; 8-constraint conditions; 9-an objective function; 10-optimizing the hot water flow; 11-room temperature sequence; 12-an optimal hot water flow sequence; 13-predicting a track at room temperature with optimal cost; 14-a first component of the optimal hot water flow sequence; 15-corresponding frequency converter frequency;
FIG. 4 is a schematic diagram of an example model predictive controller rolling optimization process.
In the figure: 1-the current moment; 2-the next time; 3-a hot water flow sequence generated at the current moment; 4-a real value of room temperature at the current moment; 5-weather forecast at the current moment; 6-SEGA genetic algorithm; 7-a room temperature prediction model; 8-constraint conditions formed by the room temperature set value at the current moment; 9-an objective function; 10-optimizing the hot water flow; 11-room temperature sequence at the current moment; 12-an optimal hot water flow sequence at the current moment; 13-predicting a track at the room temperature with optimal cost at the current moment; 14-the frequency converter frequency corresponding to the first component of the optimal hot water flow sequence at the current moment; 15-controlling a variable-frequency water pump of the radiator flow; 16-a hot water flow sequence generated at the next moment; 17-a room temperature true value at the next moment; 18-weather forecast at the next moment; 19-constraint conditions formed by the room temperature set value at the next moment; 20-room temperature sequence at the next moment; 21-an optimal hot water flow sequence at the next moment; 22-predicting a track at room temperature with optimal cost at the next moment; 23-the frequency converter frequency corresponding to the first component of the optimal hot water flow sequence at the next moment;
FIG. 5 is a graph showing the effect of model predictive control and conventional PID control on room temperature tracking at dynamic room temperature settings for an example simulation.
Fig. 6 is a graph illustrating the effect of room temperature control at different energy consumption to thermal comfort ratios in accordance with the present invention.
Fig. 7 is a graph illustrating the control effect of the example at the same room temperature level as the present invention under the conventional strategy.
Fig. 8 is a graph illustrating heating heat loss versus condition under the present and conventional strategies.
Fig. 9 is a graph illustrating heating pump consumption versus situation for the present invention and conventional strategy.
Detailed Description
The following description of the embodiments of the present invention will be made more clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention. All other examples, based on examples in this invention, which a person of ordinary skill in the art would obtain without making any inventive effort, are within the scope of the invention. It should be noted that the examples of the present invention use simulation to examine the gain effects of the present invention, and experiments may also be used to examine the gain effects of the present invention. If directional indications (such as up, down, left, right, east, south, west, north … …) are involved in the present example, the directional indications are merely used to explain the relative positional relationship between the rooms, etc. in the present example, and if the present invention is applied to other buildings, the relative positional relationship between the rooms, etc. should be determined as appropriate.
In addition, if there is a description of "first", "second", etc. in the examples of the present invention, the description of "first", "second", etc. is for descriptive purposes only and is not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions between the examples can be combined with each other, but it is necessary to base that the technical solutions can be realized by those skilled in the art, and when the technical solutions are contradictory or cannot be realized, the combination of the technical solutions should be considered to be absent and not within the protection scope of the present invention.
The invention provides a room temperature control method based on dynamic room temperature set values and model predictive control, which aims to formulate energy-saving and comfortable dynamic room temperature set values according to weather, change the room temperature as much as possible according to the track of the set values, reduce energy consumption, realize energy conservation and emission reduction, and bring economic benefits to heat supply enterprises.
Referring to fig. 1, in an example of the present invention, a room temperature control method based on a dynamic room temperature setting and model predictive control includes the steps of:
s1, determining a heating building or a heating room, and establishing a time-by-time heat load prediction model of the heating building or the heating room;
s2, forming a multi-objective optimization model by using the time-by-time thermal load prediction model and the simplified PPD calculation program, and determining a solving method;
s3, designing and adding a model predictive controller for a heating building or a room;
s4, inputting the duty ratio of the energy consumption and the thermal comfort index into the SEGA optimization model according to preference by a user to obtain an optimal room temperature set value track T
S5, controlling the indoor temperature of a heating building or a heating room by the model prediction controller to change according to an optimal room temperature set value track T;
in the example, in the step S1, a heating building is determined to be a heating experiment room in Dalian city, the north and south outer walls of the experiment room are respectively provided with an outer window, the outer doors are positioned on the north outer wall, the length, width and height of the room are 5m multiplied by 4m multiplied by 3m, the heating equipment in winter adopts a common radiator, the using time of the room is 8:00-20:00, the setting value of the room temperature is fixed at 18 ℃ during the non-using time, and the holidays are avoided;
the time-by-time thermal load prediction model input variables in the step S1 of the example are "indoor design temperature", "whether the current time is working time", "outdoor dry bulb temperature", "solar radiation amount", "personnel' S room rate", "light utilization rate", "thermal load values of 1h, 2h and 24h before the current time"; the output variable of the time-by-time thermal load prediction model is the time-by-time thermal load value of the next moment; for convenience of description it will be noted that,
Q＝f 1 (T n ,t work ,T a ,Solar,Peo,Light,Q ago1 ,Q ago2 ,Q ago24 ) (8)
wherein T is n Set at room temperature, t work For the current time is the working time, T a For outdoor dry bulb temperature, solar is Solar radiation, peo is personnel in-room rate, light is Light utilization rate, Q ago1 For the heat load value 1h before the current moment, Q ago2 For the heat load value 2h before the current moment, Q ago24 Is the thermal load value 24h before the current moment. The predictive effect of the time-by-time thermal load predictive model is shown in fig. 2.
The present example follows step S2 to build a multi-objective optimization model,
wherein, the reasonable and simplified variable method in the PPD calculation program is that the mechanical work W of the human body is 0, and the basic thermal resistance I of the clothing cl 1.4clo, human metabolism rate M is 1Met, and average radiation temperature T mrt Is lower than room temperature by 2 ℃, and indoor air humidity p a The indoor air flow velocity v was 0.07m/s for the partial pressure of water vapor at room temperature Tn and a relative humidity of 40%. The simplified PPD calculation program is recorded as，
PPD＝f(T n ) (9)
The time-by-time thermal load prediction model and the simplified PPD calculation program form a multi-objective optimization model,
in the formula (10), f 1 F is the room heat load index 2 Is a room thermal comfort index;the sum of the heat load predicted values at the dynamic room temperature set value, namely the energy consumption predicted value of the whole day, kWh is within 24 hours; q (Q) i For the time-by-time thermal load prediction model in step S1, in actual use, Q i All variables required by the prediction model mentioned in the step S1 are input, wherein the weather parameters are obtained by weather forecast; />PPD, the average predicted percent dissatisfaction for a person during a day of work i As calculated from equation (9), since the use time of this example is 8:00 to 20:00, the thermal comfort of the non-use time is not considered, and only the PPD average of the working time is taken as the optimizing target.
The decision variable is determined to be a room temperature set value according to the step S2, the room temperature set value is not changed within 3 hours, the value range is {18 ℃,19 ℃,20 ℃,21 ℃,22 ℃,23 ℃,24 ℃, and the solving method is that the NSGA-II genetic algorithm is solved;
the logic inside the model predictive controller in step S3 of this example is shown in fig. 3.
The model predictive controller scroll optimization process in step S3 of this example is shown in fig. 4.
The internal logic of the model predictive controller in step S3 of this example adopts a genetic algorithm (SEGA) to enhance elite retention to solve the rolling optimization process between the sampling instant. The SEGA genetic algorithm is mainly used for solving the optimal hot water flow sequence at each sampling moment: firstly, randomly generating different hot water flow sequences, then inputting the hot water flow sequences into a prediction model together with a real value of the room temperature and a weather forecast, obtaining a room temperature prediction track corresponding to the hot water flow sequence through circularly using the prediction model, then bringing the room temperature prediction track and the hot water flow into an objective function to calculate the fitness until an optimal hot water flow sequence and a room temperature prediction track corresponding to the optimal hot water flow sequence with optimal cost are found, and finally, completing the solution at the moment by adjusting the frequency of a water pump to enable the hot water flow flowing through a radiator to be equal to the first term of the optimal hot water flow sequence. The heating room radiates heat through a radiator for a period of time, the room temperature is changed, the real room temperature is input into a prediction model at the next sampling moment, and the genetic algorithm is used for solving again, so that rolling optimization is completed.
The room temperature prediction model in this example selects the black box model, satisfying the following functions: the current room temperature, the current meteorological conditions and the hot water flow of the radiator are input, and the indoor temperature at the next moment is output.
The goals in the rolling optimization of the model predictive controller in this example are: the minimum room temperature fluctuation and the water pump delivery energy consumption are used for tracking and optimizing the obtained room temperature set value, the objective function is,
where N is the prediction horizon, k=0 is defined as the sampling instant, each time the scroll optimization starts with k=0, y k Room temperature predictor for the kth time step, y 0 For the actual measurement of the obtained room temperature true value, P k The energy consumption of the variable-frequency water pump in the kth time step in the time domain is predicted; and 0.5 is a weight coefficient of the room temperature fluctuation and the energy consumption of the variable-frequency water pump. The predicted time domain and the control time domain are 2 hours, and the time step is 15 minutes, i.e. the room temperature is adjusted every 15 minutes.
In the rolling optimization of the model predictive controller, the predicted time-domain hot water flow sequence is represented by U, the optimizing range of each component U is as follows,
0≤u≤u max (12) Wherein u is max The optimizing range is limited to 0.15kg/s in this example for the maximum flow rate allowed by the radiator.
The constraints in the rolling optimization of the model predictive controller in this example are,
18≤y k (13)
y s -△t≤y k ≤y s in the formula + [ delta ] t (14), y k For the predicted value of the room temperature, the room temperature at any moment should be larger than 18 ℃ specified by the specification; y is s For the room temperature setting at this time, Δt is the deviation of the allowable fluctuation, Δt= ±0.5 ℃ in this example.
The solution method in the rolling optimization of the model predictive controller in this example is the SEGA genetic algorithm. The tracking effect of the model predictive control and the traditional PID on the dynamic room temperature set value is shown in the figure 5, the model predictive control has the capability of quick response and stabilizing the room temperature near the target value as soon as possible, meanwhile, the room temperature under the model predictive control deviates from the set value to a smaller extent, the model predictive control is more suitable for tracking the dynamic room temperature set value, and the model predictive control has more superiority in the aspect of room temperature control of heating rooms.
According to the embodiment, according to step S4, the user inputs the energy consumption and the occupation ratio of the thermal comfort index into a multi-objective optimization model according to own preference, and an optimal room temperature set value track T is obtained;
in the method, in the process of the invention,lambda is a different objective function i Weights of the corresponding objective functions set for the user;
according to step S4, the present example determines own preference, and sets the weights of the energy consumption objective function and the thermal comfort objective function in the formula (15) as: (1, 0), (0.75,0.25), (0.5 ), (0.25,0.75), (0, 1) and the results of the set values of room temperature were obtained from 12 months 31 in the model year to 1 month 2 in the next year of Dalian city.
The present example uses the model predictive controller to control the heating room indoor temperature to vary according to the five different optimal room temperature setpoint trajectories T of example step S4, according to step S5.
Fig. 6 is a graph showing the effect of controlling room temperature at different energy consumption and thermal comfort ratios in this example.
Fig. 7 is a graph showing the control effect of this example at the same room temperature level as the present invention under the conventional strategy.
Fig. 8 is a graph of heating heat loss versus the present example under the present and conventional strategies.
Fig. 9 is a graph of heating pump consumption versus the present example under the present and conventional strategies.
Compared with the traditional strategy, the invention has the advantages that the heating heat consumption under the control of the invention is respectively reduced by 12.2%, 10.3%, 9.5%, 8.3% and 6.9% under five working conditions, and the greater the preference of a user to an energy-saving target is, the greater the reduction degree of the heating heat consumption is, and the more obvious the energy-saving effect is. According to calculation, under the same indoor temperature level, the room temperature control method based on the dynamic room temperature set value and model predictive control is more energy-saving than the traditional strategy, and can averagely reduce the total heating energy consumption including the heating heat consumption and the water pump conveying energy consumption by 9.7%.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, and it should be noted that it is possible for those skilled in the art to make several improvements and modifications without departing from the technical principle of the present invention, and these improvements and modifications should also be regarded as the protection scope of the present invention.
Claims (2)
1. The room temperature control method based on the dynamic room temperature set value and model predictive control is characterized by comprising the following steps:
s1, determining a heating building or a heating room, and establishing a time-by-time heat load prediction model of the heating building or the heating room;
the time-by-time thermal load prediction model selects a neural network prediction model, training data is obtained through energy consumption simulation software or actual operation data is collected, and in order to improve the sensitivity of the time-by-time thermal load prediction model to a room temperature set value, the room temperature set value in the training data needs to be changed at 18-24 ℃;
the input variables of the time-by-time thermal load prediction model are: indoor design temperature; whether the current moment is working time or not; outdoor dry bulb temperature; solar radiation amount; the personnel room rate; the utilization rate of the lamplight; heat load values of 1h, 2h and 24h before the current moment;
the output variable of the time-by-time thermal load prediction model is the time-by-time thermal load value of the next moment;
s2, forming a multi-objective optimization model by using the time-by-time thermal load prediction model and the simplified PPD calculation program, and determining a solving method;
the reasonable and simplified method for the variables in the PPD calculation program comprises the following steps: the mechanical work W of human body is 0 and the basic thermal resistance I of clothing cl 1.4clo, human metabolism rate M1 Met, average radiation temperature T mrt Is lower than room temperature by 2 ℃ and indoor air humidity p a The indoor air flow velocity v is 0.07m/s for the room temperature Tn and the water vapor partial pressure at the relative humidity of 40%; the simplified PPD calculation program is noted as:
PPD＝f(T n ) (1)
the time-by-time thermal load prediction model and the simplified PPD calculation program form a multi-objective optimization model,
in the formula (2), f 1 F is the room heat load index 2 Is a room thermal comfort index;the sum of the heat load predicted values at the dynamic room temperature set value, namely the energy consumption predicted value of the whole day, kWh is within 24 hours; q (Q) i For the time-by-time thermal load prediction model in step S1, in actual use, Q i The calculation of (1) requires the step S1 to be performedAll variables required by the time-by-time thermal load prediction model are input, wherein weather parameters are obtained by weather forecast; />PPD, the average percent predicted dissatisfaction for people in a day i Calculated by the formula (1);
the method comprises the steps of determining a decision variable of a multi-objective optimization model to be a room temperature set value, simultaneously prescribing that the room temperature set value is not changed within 3 hours, wherein the value range is {18 ℃,19 ℃,20 ℃,21 ℃,22 ℃,23 ℃,24 ℃, and the solving method is used for solving a genetic algorithm;
s3, designing and adding a model predictive controller for a heating building or a heating room;
adding a model predictive controller for each building or adding a model predictive controller for each heat user, and adding according to the needs of the heat user;
the predictive model selects either a white, black or gray box model, but needs to fulfil the following functions: inputting the current room temperature, the current meteorological conditions and the hot water flow of the radiator, and outputting the indoor temperature at the next moment;
the rolling optimization of the model predictive controller aims at obtaining a room temperature set value by minimum room temperature fluctuation and water pump conveying energy consumption tracking optimization, and an objective function is expressed as,
where N is the prediction horizon, k=0 is defined as the sampling instant, each time the scroll optimization starts with k=0, y k Room temperature predictor for the kth time step, y 0 For the actual measurement of the obtained room temperature true value, P k The energy consumption of the variable-frequency water pump in the kth time step in the time domain is predicted; 0.5 is a weight coefficient of the room temperature fluctuation and the energy consumption of the variable-frequency water pump;
in the rolling optimization of the model predictive controller, the predicted time-domain hot water flow sequence is represented by U, and the optimizing range of each component U is as follows:
0≤u≤u max (4) Wherein u is max Maximum flow allowed for the radiator;
the constraint conditions in the rolling optimization of the model predictive controller are:
18≤y k (5)y s -△t≤y k ≤y s in the formula + [ delta ] t (6), y k The room temperature at any moment is larger than 18 ℃ specified by the specification for the room temperature predicted value of the kth time step; y is s Setting the temperature of the room at the moment, wherein Deltat is the deviation of the allowable fluctuation;
the internal logic of the model predictive controller is a rolling optimization process between the solving of a genetic algorithm and the sampling moment; the genetic algorithm is mainly used for solving the optimal hot water flow sequence at each sampling moment: firstly, randomly generating different hot water flow sequences through an SEGA algorithm, then inputting the hot water flow sequences into a prediction model together with a real value of the room temperature and a weather forecast, and obtaining a room temperature prediction track corresponding to the hot water flow sequences through circularly using the prediction model; then, the room temperature predicted track and the hot water flow are brought into an objective function in rolling optimization to calculate the fitness until an optimal hot water flow sequence and a corresponding room temperature predicted track with optimal cost are found; finally, the solution at the moment is completed by adjusting the frequency of the water pump to enable the flow of the hot water flowing through the radiator to be equal to the first item of the optimal hot water flow sequence; the heating building or the heating room radiates heat through a radiator for a period of time, the room temperature is changed, the real room temperature is input into a prediction model at the next sampling moment, and the genetic algorithm is used for solving again, so that rolling optimization is completed;
s4, inputting the duty ratio of the energy consumption and the thermal comfort index into the SEGA optimization model according to preference by a user to obtain an optimal room temperature set value track T;
the user's preference for both objective functions will ultimately determine the decision variable choices, and to derive the final decision variable according to the user's preference, normalize each objective function and then weight it:
wherein i is 1 or 2,lambda is the energy consumption index and the thermal comfort index respectively i Weights of the corresponding objective functions set for the user;
and S5, the model predictive controller controls the indoor temperature of the heating building or the heating room to change according to the optimal room temperature set value track T.
2. The room temperature control method based on dynamic room temperature setting and model predictive control according to claim 1, wherein the room temperature control method is performed at 20:00 a night to 8 a day after: 00, the calculation is carried out, and the calculation content is 8 on the next day: 00-next day 20: the optimum room temperature setpoint trajectory T for period 00.
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