CN116717839A - Heating control method, control device and heating system - Google Patents
Heating control method, control device and heating system Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24D—DOMESTIC- OR SPACE-HEATING SYSTEMS, e.g. CENTRAL HEATING SYSTEMS; DOMESTIC HOT-WATER SUPPLY SYSTEMS; ELEMENTS OR COMPONENTS THEREFOR
- F24D19/00—Details
- F24D19/10—Arrangement or mounting of control or safety devices
- F24D19/1006—Arrangement or mounting of control or safety devices for water heating systems
- F24D19/1009—Arrangement or mounting of control or safety devices for water heating systems for central heating
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
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- F24D3/00—Hot-water central heating systems
- F24D3/02—Hot-water central heating systems with forced circulation, e.g. by pumps
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- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B30/00—Energy efficient heating, ventilation or air conditioning [HVAC]
- Y02B30/70—Efficient control or regulation technologies, e.g. for control of refrigerant flow, motor or heating
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Abstract
The present disclosure relates to a heating control method, a control device and a heating system, the method comprising: establishing a prediction model of flow required by a user side, a power model of a water pump and a parameter model of an electric regulating valve; constructing an objective function by using the water pump power output by the power model of the water pump by adopting a sequential least square programming algorithm, and calculating the operation frequency of the water pump when the water pump power is minimum by using the heat supply total flow determined by the flow required by the user side output by the prediction model, the pressure drop balance equation of each loop in the pipe network system, the operation frequency of the water pump and the opening parameter construction constraint condition of the electric regulating valve; and controlling the operation of the water pump by using the operation frequency of the water pump when the power of the water pump is minimum so as to minimize the energy consumption of the water pump during heat supply.
Description
Technical Field
The present disclosure relates to heating technology, and more particularly, to a heating control method, a control apparatus, and a heating system.
Background
At present, the central heating system in China has the problems of hydraulic imbalance and heat imbalance of different degrees, the heat supply temperature of households close to the heat exchange station is high, and the heat supply temperature of users far away from the heat exchange station is low.
In order to solve the problems, a heating mode with large flow and small temperature difference is adopted, so that the circulating pump of the heat exchange station is large in selection, the working operation efficiency is low and the energy consumption is greatly wasted.
Disclosure of Invention
An object of the present invention is to provide a new solution for a heating control method.
According to a first aspect of the present invention, there is provided a heating control method comprising:
establishing a prediction model of flow required by a user side, a power model of a water pump and a parameter model of an electric regulating valve;
constructing an objective function by using the water pump power output by the power model of the water pump by adopting a sequential least square programming algorithm, and calculating the operation frequency of the water pump when the water pump power is minimum by using the heat supply total flow determined by the flow required by the user side output by the prediction model, the pressure drop balance equation of each loop in the pipe network system, the operation frequency of the water pump and the opening parameter construction constraint condition of the electric regulating valve;
and controlling the operation of the water pump by using the operation frequency of the water pump when the power of the water pump is minimum so as to minimize the energy consumption of the water pump during heat supply.
Optionally, the determining the total heat supply flow by the flow required by the user side output by the prediction model includes:
collecting indoor temperature values, the number of the user terminals and outdoor weather parameters of each user terminal;
inputting the indoor temperature value and the outdoor weather parameter of each user side into the prediction model to obtain the flow required by each user side;
and determining the total heat supply flow according to the flow required by each user side and the number of the user sides.
Optionally, the outdoor weather parameter includes at least one of outdoor dry bulb temperature, solar radiation intensity, outdoor air relative humidity, and wind speed.
Optionally, the prediction model includes any one of a BP neural network model, an SVR model, an MLP model, and an LSTM model.
Optionally, the power model of the water pump is a model established according to the running frequency of the water pump, the total heat supply flow and the power of the water pump.
Optionally, the parameter model of the electric regulating valve is a model established according to the opening degree and the resistance coefficient of the electric regulating valve.
Optionally, the pressure drop balance equation of each loop in the pipe network system is an equation corresponding to the relationship among the flow, the pressure drop and the resistance of each pipe section in each loop.
According to a second aspect of the present invention, there is provided a heating control device comprising:
the model building module is used for building a prediction model of flow required by a user side, a power model of the water pump and a parameter model of the electric regulating valve;
the calculation module is used for constructing an objective function by using the water pump power output by the power model of the water pump by adopting a sequential least square programming algorithm, constructing constraint conditions by using the heat supply total flow determined by the flow required by the user side output by the prediction model, the pressure drop balance equation of each loop in the pipe network system, the running frequency of the water pump and the opening parameter of the electric regulating valve, and calculating the running frequency of the water pump when the water pump power is minimum;
and the control module is used for controlling the operation of the water pump by utilizing the operation frequency of the water pump when the power of the water pump is minimum so as to minimize the energy consumption of the water pump during heat supply.
According to a third aspect of the present invention there is provided a heating control device comprising a memory and a processor, the memory storing a computer program for controlling the processor to operate to perform the heating control method according to any one of the first aspects of the present invention.
According to a fourth aspect of the present invention there is provided a heating system comprising a heating control device as in the second or third aspect of the present invention.
In the embodiment of the invention, a prediction model of flow required by a user side, a power model of a water pump and a parameter model of an electric regulating valve are established; constructing an objective function by using the water pump power output by the power model of the water pump by adopting a sequential least square programming algorithm, and calculating the operation frequency of the water pump when the water pump power is minimum by using the heat supply total flow determined by the flow required by the user side output by the prediction model, the pressure drop balance equation of each loop in the pipe network system, the operation frequency of the water pump and the opening parameter construction constraint condition of the electric regulating valve; and controlling the operation of the water pump by utilizing the operation frequency of the water pump when the power of the water pump is minimum, so that the energy consumption of the water pump is minimum when heating, and the energy-saving optimized operation of a heating system and the heat balance of end users of a pipe network are achieved.
Features of the embodiments of the present specification and their advantages will become apparent from the following detailed description of exemplary embodiments of the present specification with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the specification and, together with the description, serve to explain the principles of the embodiments of the specification.
Fig. 1 is a process flow diagram of a heating control method according to one embodiment of the present invention.
Fig. 2 is a schematic block diagram of a heating control device according to an embodiment of the present invention.
Fig. 3 is a schematic hardware configuration of a heating control device according to an embodiment of the present invention.
Detailed Description
Various exemplary embodiments of the present specification will now be described in detail with reference to the accompanying drawings.
The following description of at least one exemplary embodiment is merely exemplary in nature and is in no way intended to limit the disclosure, its application, or uses.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further discussion thereof is necessary in subsequent figures.
The heat supply system comprises a boiler, a heat exchange station and a user side. The heat supply working principle is that the circulating water pump conveys circulating water to the inlet of the boiler, the circulating water is conveyed to the heat exchange station for heat exchange after being heated by the boiler, and boiler backwater is conveyed to the inlet of the circulating water pump through the backwater pipe. Meanwhile, the secondary circulation system of the heat exchange station sends heat to each user, and the heat is released through the radiator, so that the temperature required by residents is ensured. After heat supply, backwater of the radiator returns to the heat supply station through the pipeline.
The pipe network system comprises a heat exchange station and a user side. The water pump related by the invention is a circulating water pump of a heat exchange station.
< method example >
In this embodiment, a heating control method is provided. According to fig. 1, the heating control method of the present embodiment may include the following steps S110 to S130.
Step S110, a prediction model of flow required by a user side, a power model of a water pump and a parameter model of an electric regulating valve are established.
The user side is provided with a temperature control panel, and the user sets the indoor temperature required by the user through the temperature control panel. The temperature control panel can collect indoor temperature set by the user side in real time and upload the collected indoor temperature to the heat supply control device.
The heating control device can also collect outdoor weather parameters. The outdoor weather parameters include at least one of outdoor dry bulb temperature, solar radiation intensity, outdoor air relative humidity, and wind speed.
In one embodiment, historical data is first obtained, wherein the historical data comprises indoor temperature of the user side, outdoor meteorological data and flow required by the user side, then a predictive model is trained by using the historical data, and the predictive model of the flow required by the user side is built.
The prediction model includes any one of a BP neural network model, a SVR model, an MLP model, and an LSTM model. Preferably an SVR model.
The essence of the SVR (Support Vector Regression ) model is to extend the classification ideas of the support vector machine into regression problems. In the support vector machine, a hyperplane is found, the data points of different categories are separated, and the classification interval is maximized to find the optimal hyperplane. In the regression problem, a hyperplane is also found, which fits the given data and minimizes the prediction error. In the SVR model, the flow y required by the user side is composed of an indoor and outdoor environment variable x and an error term epsilon, see the calculation formula (1),
calculation formula (1),
the flow y required by the user side is expressed as the sum of a function f (x) of an indoor and outdoor environment variable x and an error term epsilon, wherein f (x) is a regression function, and epsilon is the error term. The goal is to find a regression function f (x) that minimizes the error between the predicted value and the true value.
SVR builds models by minimizing spacing and error. The fit between the data is described by constructing the distance between the hyperplane and the nearest data point. The larger the interval, the more generalization capability of the model. The error is the difference between the predicted value and the true value, i.e., the residual between the fitting function and the target variable. SVR minimizes prediction errors and spacing by limiting the distance of the support vector to the hyperplane to no more than a certain range of errors.
SVR uses kernel functions to map input features into a high-dimensional space and performs linear or nonlinear regression in the high-dimensional space. Common kernel functions include linear kernel functions, polynomial kernel functions, radial basis functions, and the like. When selecting the kernel function, corresponding hyper-parameters, such as penalty factor C, kernel function parameter gamma, are determined to better train the model. The embodiment of the invention adopts a grid search method to find the optimal parameters of SVR.
In one embodiment of the invention, the indoor temperature value, the number of the user terminals and the outdoor weather parameters of each user terminal are collected; inputting the indoor temperature value and the outdoor weather parameter of each user side into a prediction model to obtain the flow required by each user side; and determining the total heat supply flow according to the flow required by each user side and the number of the user sides.
In one embodiment of the invention, the power model of the water pump is a model built from the operating frequency of the water pump, the total heat supply flow and the power of the water pump.
In the embodiment of the invention, the power model of the water pump is determined by the following calculation formulas (2) - (4). According to the power model of the water pump, the power of the water pump can be determined according to the running frequency of the water pump and the total heat supply flow,
calculation formula (2),
calculation formula (3),
-calculation formula (4),
wherein a, b, c and d, e, g are the performance parameters of the water pump, f and f, respectively 0 Operating frequency of water pump and rated frequency of water pump, Q and Q respectively 0 Respectively heating total flow and rated flow, H is the lift of the water pump,for the efficiency of the water pump, ρ is the density of water, g is the gravitational acceleration, and P is the power of the water pump.
In one embodiment of the invention, a least squares method is used to determine the parameters that need to be determined in the power model of the water pump.
The least squares method is a parameter identification method that can be used to estimate model parameters for both linear and nonlinear systems. The basic idea is to determine the parameter values of the model by minimizing the sum of squares of errors between the actual data and the data output by the model.
In the embodiment of the invention, it is assumed that there is a data set z containing N sample points k ={y k , q k , f k (k=1,., N), where y k Represents the actual power of the water pump, q k And f k The total heat supply flow and the operating frequency of the water pump are respectively. The power model of the water pump can be expressed as a function f (q k ,f k θ), where θ represents the model parameters to be determined. The objective of the least square method is to find an optimal parameter θ ́ to make the power model of the water pump output the predicted power value f (q k ,f k θ) and the actual power value y of the water pump k Sum of squares of errors betweenMinimum, see in particular equation (5),
-calculation formula (5).
In one embodiment of the invention, the parametric model of the electrically operated valve is a model built from the opening degree and the resistance coefficient of the electrically operated valve. The electric regulating valve related to the embodiment of the invention is arranged at a user side.
In the embodiment of the invention, a parameter model of the electric control valve is determined by the following calculation formula (6). According to the parameter model of the electric regulating valve, the resistance coefficient of the electric regulating valve can be determined according to the opening degree of the electric regulating valve,
-calculation formula (6),
wherein R is electric modulationThe resistance coefficient of the throttle valve,for maximum resistance coefficient of electrically controlled valve, +.>For the actual opening degree of the electric regulating valve, theta max And n and m are respectively adjustable parameter ratios of the electric regulating valve for controlling the curve shape of the resistance coefficient changing along with the opening. n controls the magnitude of the change in the resistance coefficient when the opening is less than 50%. m controls the variation amplitude of the resistance coefficient when the opening is greater than 50%. Maximum resistance coefficient of electric regulating valve +.>And a maximum opening degree theta of the electric control valve max Is a known parameter.
In one embodiment, the least squares method is used to determine the adjustable parameter ratios n and m for the electrically operated regulator valve.
In the embodiment of the invention, the pressure drop balance equation of each loop in the pipe network system is an equation corresponding to the relation among the flow, pressure drop and resistance of each pipe section in each loop.
And converting the topological structure information of the pipe network system into a matrix form by adopting graph theory. And taking each pipe section in the pipe network system as a branch, and taking an intersection point generated by the intersection of different pipe sections as a node. Then, a loop matrix describing the topology structure of the pipe network system is constructedThe construction method is as follows:
，
incidence matrixThe relationship between the pipe section and the node can be represented, and the construction method is as follows:
。
for any node in the pipe network system, the algebraic sum of the flow flowing out of the node and the flow flowing in is zero. Describing the flow of each node by the product of the correlation matrix and the flow vector in the pipe section, and obtaining according to a flow balance equation:
wherein, the liquid crystal display device comprises a liquid crystal display device,the correlation matrix is the correlation matrix of the pipe network system; />(j=0, 1,2,., m) is a vector derived from the flow on each pipe segment; />(i=0, 1,2,) n is a vector generated by each node traffic.
For any loop in the pipe network system, the branch pressure drop on all pipe sections is equal to zero. The pressure drop balance of each loop can thus be described by the product of the basic loop matrix and the spool piece pressure drop vector, as can be obtained from the pressure drop balance equation:
，
wherein, the liquid crystal display device comprises a liquid crystal display device,the loop matrix is the loop matrix corresponding to the j-th loop of the pipe network system; />(i=0, 1,2,., m) is the pressure drop over each tube segment.
And step S120, constructing an objective function by using the water pump power output by the power model of the water pump by adopting a sequential least square programming algorithm, constructing constraint conditions by using the heat supply total flow determined by the flow required by the user side output by the prediction model, the pressure drop balance equation of each loop of the pipe network system, the operating frequency of the water pump and the opening parameter of the electric regulating valve, and calculating the operating frequency of the water pump when the water pump power is minimum.
In one embodiment of the invention, the number of the water pumps in the pipe network system is at least one. The water pump power is expressed by a calculation formula (7),
calculation formula (7),
wherein, the liquid crystal display device comprises a liquid crystal display device,the number of the water pumps is>For the power of the i-th water pump, +.>Is the power of the water pump group.
In one embodiment, the constraint corresponding to the total heat supply flow is that the actual operating flow of the water pump meets the total heat supply flow requirement, see equation (8),
-calculation formula (8),
wherein, the liquid crystal display device comprises a liquid crystal display device,for the actual running flow of the water pump +.>Is the total flow of heat supply.
The constraint conditions corresponding to the pressure drop balance of each loop of the pipe network system are as follows:。
the constraint conditions corresponding to the operation frequency of the water pump are as follows:，Hz。
the constraint conditions corresponding to the opening parameters of the electric regulating valve are as follows:%。
in the embodiment of the invention, the objective function and the constraint condition can be expressed as follows:
,
,
,
,
。
according to the objective function and the constraint condition, the operating frequency f of the water pump and the valve opening of the electric regulating valve are selectedAs an optimization variable, taking the minimum power of the water pump group as an optimization target, taking pressure drop balance of each loop of the pipe network system and total heat supply flow demand as constraint conditions, and establishing an optimization model.
In the embodiment of the invention, the objective function is solved by adopting a sequential least square programming algorithm. The algorithm is an optimization algorithm for solving the constrained nonlinear programming problem. The principle of the algorithm is that a constrained nonlinear programming problem is converted into an unconstrained least square problem, in each iteration step, a quadratic programming sub-problem is used for solving an optimal solution of a current point, and in the sub-problem, a quadratic approximation is performed on an objective function, so that the optimal solution meeting constraint conditions is solved.
First, the objective function and constraint conditions of the sequential least squares programming algorithm are expressed as:
,
,
,
wherein, the liquid crystal display device comprises a liquid crystal display device,is an objective function->And->Respectively representing an equality constraint and an inequality constraint, the equality constraintFor m, inequality constraint +.>P. In the embodiment of the invention, the objective function +.>Is->. Equality constraint->Comprises two, respectively->,/>. The inequality constraint includes two, respectively。
First, the inequality constraint is converted into an equality constraint:
。
then, the objective function and the equality constraint are expressed as a Lagrangian function:
wherein, the liquid crystal display device comprises a liquid crystal display device,and->Lagrangian multipliers representing equality constraints and inequality constraints, respectively. A new function is then defined:
wherein, the liquid crystal display device comprises a liquid crystal display device,is an approximation of the hessian matrix,/>Is a variable->Is a variable amount of (a). In order to be->Where a constraint is found +.>So that->Minimizing.
For the equation constraint, the following linear system of equations is solved:
wherein, the liquid crystal display device comprises a liquid crystal display device,is->Is a jacobian matrix of (c). Then, will->Substituting into the lagrangian function, the following form of function is obtained:
for a pair ofPerforming secondary approximation to obtain:
wherein, the liquid crystal display device comprises a liquid crystal display device,and->Respectively indicate->The gradient vector at that point is approximated by a hessian matrix. For->Optimizing to obtain optimal ++>. The sub-problem may be solved in particular using a restrictive newton method or other quadratic programming algorithm.
For inequality constraint, pruning techniques can be used to ensure that the range of values of the variables always meets the constraint. In particular, the step size can be adjustedThe method is divided into the following forms:
wherein, the liquid crystal display device comprises a liquid crystal display device,is->In the gradient direction of the current point, +.>Is a pruning vector that satisfies the constraint. Then, solve +.>：
Wherein, the liquid crystal display device comprises a liquid crystal display device,is->Is a jacobian matrix of (c). Finally, determine->And->And update the current point. Repeating the above steps until the convergence condition is satisfied or the maximum number of iterations is reached.
And step S130, controlling the operation of the water pump by using the operation frequency of the water pump when the power of the water pump is minimum so as to minimize the energy consumption of the water pump when heating.
The heat supply control method provided by the invention realizes the minimum energy consumption of the water pump during heat supply, and achieves the energy-saving optimized operation of a heat supply system and the heat balance of end users of a pipe network.
< device example >
One embodiment of the present invention provides a heating control device as shown in fig. 2. The heating control device 200 includes a model building module 210, a calculation module 220, and a control module 230.
The model building module 210 is used for building a prediction model of flow required by a user, a power model of the water pump and a parameter model of the electric regulating valve.
The calculation module 220 is configured to construct an objective function by using the water pump power output by the power model of the water pump by adopting a sequential least square programming algorithm, and calculate the operation frequency of the water pump when the water pump power is minimum by using the heat supply total flow determined by the flow required by the user side output by the prediction model, the pressure drop balance equation of each loop in the pipe network system, the operation frequency of the water pump and the opening parameter construction constraint condition of the electric regulating valve.
The control module 230 is used for controlling the operation of the water pump by using the operation frequency of the water pump when the power of the water pump is minimum so as to minimize the energy consumption of the water pump when heating.
In one embodiment of the present invention, determining the total heating flow by predicting the flow required by the client output by the model includes: collecting indoor temperature values, the number of the user terminals and outdoor weather parameters of each user terminal; inputting the indoor temperature value and the outdoor weather parameter of each user side into a prediction model to obtain the flow required by each user side; and determining the total heat supply flow according to the flow required by each user side and the number of the user sides.
The outdoor weather parameters include at least one of outdoor dry bulb temperature, solar radiation intensity, outdoor air relative humidity, and wind speed.
The prediction model includes any one of a BP neural network model, a SVR model, an MLP model, and an LSTM model.
The power model of the water pump is a model established according to the running frequency of the water pump, the total heat supply flow and the power of the water pump.
The parameter model of the electric regulating valve is a model established according to the opening degree and the resistance coefficient of the electric regulating valve.
The pressure drop balance equation of each loop in the pipe network system is an equation corresponding to the relation among the flow, pressure drop and resistance of each pipe section in each loop.
One embodiment of the present invention provides a heating control device as shown in fig. 3. The heating control device 300 includes a memory 320 and a processor 310. The memory 320 stores a computer program for controlling the processor 310 to operate to perform the heating control method in any of the above embodiments.
< heating System example >
An embodiment of the present invention provides a heating control system including the heating control device of any one of the above embodiments.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. For the electric vehicle embodiment, the relevant points are referred to in the description of the method embodiment.
The foregoing describes specific embodiments of the present disclosure. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
Embodiments of the present description may be systems, methods, and/or computer program products. The computer program product may include a computer-readable storage medium having computer instructions for causing a processor to implement aspects of embodiments of the present description.
The computer readable storage medium may be a tangible device that can hold and store computer instructions for use by a computer instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: portable computer disks, hard disks, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static Random Access Memory (SRAM), portable compact disk read-only memory (CD-ROM), digital Versatile Disks (DVD), memory sticks, floppy disks, mechanical coding devices, punch cards or in-groove protrusion structures such as punch cards or grooves having computer instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media, as used herein, are not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through wires.
The computer instructions described herein may be downloaded from a computer readable storage medium to a respective computing/processing device or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers and/or edge servers. The network interface card or network interface in each computing/processing device receives computer instructions from the network and forwards the computer instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present description. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of computer instructions, which comprises one or more executable computer instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, implementation by software, and implementation by a combination of software and hardware are all equivalent.
The embodiments of the present specification have been described above, and the above description is illustrative, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope of the various embodiments described. The terminology used herein was chosen in order to best explain the principles of the embodiments, the practical application, or the improvement of technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (10)
1. A heating control method, characterized by comprising:
establishing a prediction model of flow required by a user side, a power model of a water pump and a parameter model of an electric regulating valve;
constructing an objective function by using the water pump power output by the power model of the water pump by adopting a sequential least square programming algorithm, and calculating the operation frequency of the water pump when the water pump power is minimum by using the heat supply total flow determined by the flow required by the user side output by the prediction model, the pressure drop balance equation of each loop in the pipe network system, the operation frequency of the water pump and the opening parameter construction constraint condition of the electric regulating valve;
and controlling the operation of the water pump by using the operation frequency of the water pump when the power of the water pump is minimum so as to minimize the energy consumption of the water pump during heat supply.
2. The method of claim 1, wherein determining the total flow of heat supply from the flow required by the client output by the prediction model comprises:
collecting indoor temperature values, the number of the user terminals and outdoor weather parameters of each user terminal;
inputting the indoor temperature value and the outdoor weather parameter of each user side into the prediction model to obtain the flow required by each user side;
and determining the total heat supply flow according to the flow required by each user side and the number of the user sides.
3. The method of claim 2, wherein the outdoor weather parameters include at least one of outdoor dry bulb temperature, solar radiation intensity, outdoor air relative humidity, and wind speed.
4. The method of claim 1 or 2, wherein the predictive model comprises any one of a BP neural network model, an SVR model, an MLP model, and an LSTM model.
5. The method of claim 1, wherein the power model of the water pump is a model built from the operating frequency of the water pump, the total heat supply flow, and the power of the water pump.
6. The method of claim 1, wherein the parametric model of the electrically operated valve is a model built from an opening degree and a resistance coefficient of the electrically operated valve.
7. The method of claim 1, wherein the pressure drop equilibrium equation for each circuit in the pipe network system is an equation corresponding to the relationship between flow, pressure drop and resistance for each pipe segment in each circuit.
8. A heating control device, characterized by comprising:
the model building module is used for building a prediction model of flow required by a user side, a power model of the water pump and a parameter model of the electric regulating valve;
the calculation module is used for constructing an objective function by using the water pump power output by the power model of the water pump by adopting a sequential least square programming algorithm, constructing constraint conditions by using the heat supply total flow determined by the flow required by the user side output by the prediction model, the pressure drop balance equation of each loop in the pipe network system, the running frequency of the water pump and the opening parameter of the electric regulating valve, and calculating the running frequency of the water pump when the water pump power is minimum;
and the control module is used for controlling the operation of the water pump by utilizing the operation frequency of the water pump when the power of the water pump is minimum so as to minimize the energy consumption of the water pump during heat supply.
9. A heating control device, characterized by comprising a memory and a processor, the memory storing a computer program for controlling the processor to operate to perform the heating control method according to any one of claims 1-7.
10. A heating system comprising a heating control device as claimed in claim 8 or 9.
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