CN114909707B - Heat supply secondary network regulation and control method based on intelligent balance device and reinforcement learning - Google Patents
Heat supply secondary network regulation and control method based on intelligent balance device and reinforcement learning Download PDFInfo
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Abstract
The application discloses a heat supply secondary network regulation and control method based on an intelligent balancing device and reinforcement learning, which comprises the following steps: establishing a digital twin model of the heat supply secondary network unit building by adopting a mechanism modeling and data identification method; an intelligent balancing device, an electric regulating valve and a calorimeter for improving the circulation flow in the unit building are connected in front of the unit building with unfavorable circulation flow characteristics; based on a digital twin model of a unit building of a heat supply secondary network, historical heat supply operation data, weather data and indoor temperature are obtained, and the required heat in the unit building is obtained through calculation of a mixed prediction model and a model weight value; when the heat required in the unit building is unchanged and the circulation flow is insufficient, calculating and obtaining a regulation strategy of the intelligent balance device and the electric regulating valve by adopting a reinforcement learning algorithm, and improving the circulation flow in the unit building; and based on the digital twin model of the heat supply secondary network unit building, issuing and executing the regulation strategy after verifying and analyzing the regulation strategy in an energy-saving way.
Description
Technical Field
The application belongs to the technical field of intelligent heat supply, and particularly relates to a heat supply secondary network regulation and control method based on an intelligent balancing device and reinforcement learning.
Background
In the operation work of the current central heating system, the energy consumption of a unit building is gradually reduced, the design and construction of a heat supply diode network are gradually standardized, a heat supply enterprise pays more attention to the thermal mass for users and the energy consumption reduction, the level of equipment accessories for adjustment is continuously improved, the form of terminal heat dissipation equipment is more various, a large amount of electric appliance automatic control equipment is put into, the mode of heat supply operation adjustment is also continuously changed, and the comprehensive performance is stronger.
For the unit building with poor circulation characteristics of the system in the building, because no effective adjusting means is provided, users at the tail end of the system in the building change the radiator privately due to the fact that the room temperature does not reach the standard, the radiating area is increased, the flow is increased, the water supply temperature of the rear users is lower, the room temperature difference among the users becomes larger, the required flow of the whole unit building far exceeds the design flow, the heating station can only increase the consumption of electric energy by changing a larger water pump, and therefore the flow of a secondary network is further increased.
Aiming at a unit building with poor circulation characteristics in the building, how to improve the circulation flow in the unit building, improve the temperature of the water supply and return and realize heat supply and energy conservation is a problem which needs to be solved at present.
Based on the technical problems, a new heat supply secondary network regulation and control method based on an intelligent balance device and reinforcement learning needs to be designed.
Disclosure of Invention
The application aims to solve the technical problem of overcoming the defects of the prior art and providing a heat supply secondary network regulation and control method based on an intelligent balancing device and reinforcement learning.
In order to solve the technical problems, the technical scheme of the application is as follows:
the application provides a heat supply secondary network regulation and control method based on an intelligent balancing device and reinforcement learning, which comprises the following steps:
s1, establishing a digital twin model of a heat supply secondary network unit building by adopting a mechanism modeling and data identification method;
s2, an intelligent balancing device, an electric regulating valve and a calorimeter for improving the circulation flow in the unit building are connected in front of the unit building with unfavorable circulation flow characteristics;
step S3, based on a digital twin model of the unit building of the heat supply secondary network, historical heat supply operation data, weather data and indoor temperature are obtained, and the required heat in the unit building is obtained through calculation of a mixed prediction model and a model weight value;
s4, when the required heat in the unit building is unchanged and the circulation flow is insufficient, calculating and obtaining a regulation strategy of the intelligent balance device and the electric regulating valve by adopting a reinforcement learning algorithm, and improving the circulation flow in the unit building;
and S5, based on the digital twin model of the heat supply secondary network unit building, issuing and executing the regulation strategy after verifying and analyzing the energy conservation of the regulation strategy.
Further, in the step S1, a mechanism modeling and data identification method is adopted to build a digital twin model of the heating secondary network unit building, which specifically includes:
establishing a digital twin model comprising a physical entity, a virtual entity, a twin data service and connecting elements among the components of the two-level network unit building;
the physical entity is the basis of a digital twin model and is a data source driven by the whole digital twin model; the virtual entity and the physical entity are mapped one by one and interacted in real time, elements of the physical space are described from multiple dimensions and multiple scales, the actual process of the physical entity is simulated, and element data are analyzed, evaluated, predicted and controlled; the twin data service integrates the physical space information and the virtual space information, ensures the real-time performance of data transmission, provides knowledge base data comprising intelligent algorithms, models, rule standards and expert experiences, and forms a twin database by fusing the physical information, the multi-time space associated information and the knowledge base data; the connection between the components realizes the interconnection of the components, and the real-time acquisition and feedback of data are realized between the physical entity and the twin data service through the sensor and the protocol transmission specification; the physical entity and the virtual entity carry out data transmission through a protocol, physical information is transmitted to the virtual space in real time to update the correction model, and the virtual entity carries out real-time control on the physical entity through an executor; the information transfer between the virtual entity and the twin data service is realized through a database interface;
and identifying the digital twin model, accessing the multi-working-condition real-time operation data of the secondary network unit building into the established digital twin model, and adopting a reverse identification method to carry out self-adaptive identification correction on the simulation result of the digital twin model to obtain the digital twin model of the heat supply secondary network unit building after the identification correction.
Further, in the step S3, based on the digital twin model of the unit building of the heat supply secondary network, historical heat supply operation data, weather data and indoor temperature are obtained, and the required heat in the unit building is obtained through calculation of a mixed prediction model and a model weight value, which specifically comprises:
based on a digital twin model of a unit building of a heat supply secondary network, acquiring historical multidimensional heat supply data of the unit building and corresponding historical demand heat real data, preprocessing to obtain a sample set of a demand heat prediction model in the unit building, wherein the sample set at least comprises historical indoor temperature, weather data, unit building water supply and return temperature, unit building water supply flow and unit building demand heat, and dividing the sample set into a training data set and a test data set;
selecting two training prediction models, and sequentially training the two training prediction models through a training data set to obtain corresponding heat demand prediction models in the unit building;
sequentially inputting the test data sets into the two unit building internal demand heat prediction models to obtain corresponding demand heat test data;
according to the demand heat test data and the historical demand heat real data, calculating weight values of two training prediction models by adopting an optimal weight combination strategy;
the multi-dimensional heat supply data of the unit building at the next moment is obtained and is input into the two unit building internal demand heat prediction models to obtain corresponding demand heat prediction values;
and calculating according to the demand heat predicted values of the two training predicted models and the corresponding weight values to obtain the final demand heat in the unit building.
Further, according to the demand heat test data and the historical demand heat real data, calculating the weight values of the two training prediction models by adopting an optimal weight combination strategy, wherein the method specifically comprises the following steps:
set a first training pre-stageThe predicted value of the heat demand of the test model is f 1 The predicted value of the heat demand of the second training prediction model is f 2 Predicted value f of demand heat 1 The corresponding weight value is w 1 Predicted value f of demand heat 2 The corresponding weight value is w 2 ；
According to the heat demand test data and the historical heat demand real data, calculating to obtain predicted deviation values of two training predicted models, wherein the predicted deviation value of the first training predicted model is e 1 The prediction deviation value of the second training prediction model is e 2 ；
Calculating the sum of squares of the deviation of the two training prediction models to be S according to the weight value and the prediction deviation value;
setting an objective function of an optimal weight combination strategy as the square sum of deviation S minimization and constraint conditions as follows: w (w) 1 +w 2 ＝1；w 1 ＞0，w 2 ＞0；
And solving the objective function to obtain the optimal weight values of the two training prediction models.
Further, in the step S4, when the heat required in the unit building is unchanged and the circulation flow is insufficient, the regulation strategy of the intelligent balancing device and the electric regulating valve is obtained by adopting the reinforcement learning algorithm, so as to promote the circulation flow in the unit building, which specifically comprises:
when the heat required in the unit building is unchanged and the circulation flow is detected to be insufficient, the water supply circulation flow cannot meet the heat required in the unit building, and the maximum Q value is obtained by combining rule constraint analysis and durable-DQN reasoning based on the acquired heat supply operation data, weather data and indoor temperature as states, the final intelligent balance device and the regulation strategy output action of the electric regulating valve are issued to actual execution equipment for execution, the circulation flow in the unit building is promoted, and the rewarding feedback of energy saving amount and the data state before and after the execution action are obtained after the execution; storing the data in a database, and performing training update of the lasting-DQN model by extracting the data at regular time to replace the old model;
the Dueling-DQN neural network algorithm splits the network structure into two branches before an output layer under the condition of not changing the input and output of the Dueling-DQN model: the value estimation branch and the advantage estimation branch are used for respectively estimating the value of the state and the values of different actions in the state, and then the two network branches are linearly combined into an output layer to realize accurate estimation of the Q value.
Further, the Dueling-DQN neural network algorithm is operated in a safe range by setting rule constraint, and meanwhile, parameters of the Dueling-DQN neural network are continuously optimized by setting a reward function;
and determining a plurality of safe action ranges according to the setting of different rule constraints, meeting the heat required in the unit building, calculating the action output of a water supply and return temperature set value through a lasting-DQN neural network algorithm, selecting the action with the largest Q value, judging whether the action is in the rule constraint range, if so, outputting the action to execute according to the lasting-DQN neural network algorithm, otherwise, outputting the action to execute according to the rule constraint, and executing punishment rewards for the action.
Further, the training update of the lasting-DQN model includes: taking the heat supply operation data, weather data and indoor temperature data stored in a database as the current state s, outputting an intelligent balancing device and an electric regulating valve action a through a lasting-DQN (direct current network) neural network algorithm, and obtaining instant rewards r after executing; when the environment changes to reach a new state s ', storing (s, a, r, s') as training sample data in a database, and introducing an experience playback mechanism for offline training of the model;
the lasting-DQN neural network algorithm trains updates at fixed times per day: loading current model parameters of the Dueling-DQN neural network algorithm, randomly extracting data samples from a memory bank each time for training to obtain new model parameters, and replacing an old model.
Further, the reward function settings of the lasting-DQN neural network algorithm include energy conservation and security constraint rewards.
Further, the Dueling-DQN neural network algorithm includes two common hidden layers, the value estimation branch includes one hidden layer, and the dominance estimation branch includes one hidden layer.
The beneficial effects of the application are as follows:
on the one hand, the method obtains the heat required in the unit building through calculation of the mixed prediction model and the model weight value, establishes two sub-prediction models, and establishes the mixed prediction model through weighting the two model weights, wherein the prediction precision of the model is higher; on the other hand, in order to save training time and improve training efficiency, a lasting DQN algorithm is provided for training a neural network of the intelligent balance device, and the behavior of the electric control valve is driven according to the input heat supply operation data, weather data, indoor temperature and the like to output a better action, so that the circulation flow in a building is changed, and the algorithm further improves the training stability while guaranteeing the performance.
Additional features and advantages will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the application. The objectives and other advantages of the application will be realized and attained by the structure particularly pointed out in the written description and drawings.
In order to make the above objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present application, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for regulating and controlling a heat supply secondary network based on an intelligent balance device and reinforcement learning;
FIG. 2 is a block diagram of a Dueling-DQN neural network of the present application;
FIG. 3 is a schematic block diagram of the application for obtaining a secondary network regulation strategy based on the lasting-DQN neural network algorithm.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the present application will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
FIG. 1 is a flow chart of a method for regulating and controlling a heat supply secondary network based on an intelligent balance device and reinforcement learning.
As shown in fig. 1, this embodiment provides a method for controlling a heating secondary network based on an intelligent balancing device and reinforcement learning, which includes:
s1, establishing a digital twin model of a heat supply secondary network unit building by adopting a mechanism modeling and data identification method;
s2, an intelligent balancing device, an electric regulating valve and a calorimeter for improving the circulation flow in the unit building are connected in front of the unit building with unfavorable circulation flow characteristics;
step S3, based on a digital twin model of the unit building of the heat supply secondary network, historical heat supply operation data, weather data and indoor temperature are obtained, and the required heat in the unit building is obtained through calculation of a mixed prediction model and a model weight value;
s4, when the required heat in the unit building is unchanged and the circulation flow is insufficient, calculating and obtaining a regulation strategy of the intelligent balance device and the electric regulating valve by adopting a reinforcement learning algorithm, and improving the circulation flow in the unit building;
and S5, based on the digital twin model of the heat supply secondary network unit building, issuing and executing the regulation strategy after verifying and analyzing the energy conservation of the regulation strategy.
In practical application, can unify the intelligent balancing unit, the electric control valve, the calorimeter of unit building front access into balancing unit before the building, at least, including outdoor temperature sensor, water supply temperature sensor, the water pump, manual balancing valve, electric control valve, differential pressure controller, return water temperature sensor, can separate the operating mode of diode network and the operating mode of intra-building system, solve the problem that the intra-building circulation characteristic is not good with intelligent balancing unit before the building and electric control valve, the second grade network is mutually noninterfered with the operating mode in the building, promote intra-building circulation flow and energy-conserving volume, solve the problem of intra-building water power imbalance. After the front balancing device of the building is connected, the main pipe network adopts a large-temperature-difference small-flow operation mode, the terminal unit building user adopts a large-flow small-temperature-difference operation mode, so that the resistance of the main pipe network is relatively small, and the resistance of the terminal user is relatively large. From the hydraulic stability, the running mode has good balance of the whole system, is not easy to generate hydraulic imbalance problem, and can be adjusted by adjusting the circulating flow even if the hydraulic imbalance problem occurs locally.
In this embodiment, in step S1, a mechanism modeling and data identification method is adopted to build a digital twin model of a heating secondary network unit building, which specifically includes:
establishing a digital twin model comprising a physical entity, a virtual entity, a twin data service and connecting elements among the components of the two-level network unit building;
the physical entity is the basis of a digital twin model and is a data source driven by the whole digital twin model; the virtual entity and the physical entity are mapped one by one and interacted in real time, elements of the physical space are described from multiple dimensions and multiple scales, the actual process of the physical entity is simulated, and element data are analyzed, evaluated, predicted and controlled; the twin data service integrates the physical space information and the virtual space information, ensures the real-time performance of data transmission, provides knowledge base data comprising intelligent algorithms, models, rule standards and expert experiences, and forms a twin database by fusing the physical information, the multi-time space associated information and the knowledge base data; the connection between the components realizes the interconnection of the components, and the real-time acquisition and feedback of data are realized between the physical entity and the twin data service through the sensor and the protocol transmission specification; the physical entity and the virtual entity carry out data transmission through a protocol, physical information is transmitted to the virtual space in real time to update the correction model, and the virtual entity carries out real-time control on the physical entity through an executor; the information transfer between the virtual entity and the twin data service is realized through a database interface;
and identifying the digital twin model, accessing the multi-working-condition real-time operation data of the secondary network unit building into the established digital twin model, and adopting a reverse identification method to carry out self-adaptive identification correction on the simulation result of the digital twin model to obtain the digital twin model of the heat supply secondary network unit building after the identification correction.
In this embodiment, in step S3, based on a digital twin model of a unit building of a heat supply secondary network, historical heat supply operation data, weather data and indoor temperature are obtained, and the required heat in the unit building is obtained through calculation of a mixed prediction model and a model weight value, which specifically includes:
based on a digital twin model of a unit building of a heat supply secondary network, acquiring historical multidimensional heat supply data and corresponding historical demand heat real data of the unit building, preprocessing to obtain a sample set of a demand heat prediction model in the unit building, wherein the sample set at least comprises historical indoor temperature, weather data, unit building water supply and return temperature, unit building water supply flow and unit building demand heat, and dividing the sample set into a training data set and a test data set;
selecting two training prediction models, and sequentially training the two training prediction models through a training data set to obtain corresponding heat demand prediction models in the unit building;
sequentially inputting the test data sets into the heat demand prediction models in the two unit buildings to obtain corresponding heat demand test data;
according to the demand heat test data and the historical demand heat real data, calculating weight values of two training prediction models by adopting an optimal weight combination strategy;
the multi-dimensional heat supply data of the unit building at the next moment is obtained and is input into the two unit building internal demand heat prediction models to obtain corresponding demand heat prediction values;
and calculating according to the demand heat predicted values of the two training predicted models and the corresponding weight values to obtain the final demand heat in the unit building.
In this embodiment, the calculating the weight values of the two training prediction models according to the required heat test data and the historical required heat real data by adopting the optimal weight combination strategy specifically includes:
let the predicted value of the heat demand of the first training predictive model be f 1 The predicted value of the heat demand of the second training prediction model is f 2 Predicted value f of demand heat 1 The corresponding weight value is w 1 Predicted value f of demand heat 2 The corresponding weight value is w 2 ；
According to the heat demand test data and the historical heat demand real data, calculating to obtain predicted deviation values of two training predicted models, wherein the predicted deviation value of the first training predicted model is e 1 The prediction deviation value of the second training prediction model is e 2 ；
Calculating the sum of squares of the deviation of the two training prediction models to be S according to the weight value and the prediction deviation value;
setting an objective function of an optimal weight combination strategy as the square sum of deviation S minimization and constraint conditions as follows: w (w) 1 +w 2 ＝1；w 1 ＞0，w 2 ＞0；
And solving the objective function to obtain the optimal weight values of the two training prediction models.
FIG. 2 is a block diagram of a Dueling-DQN neural network in accordance with the present application.
FIG. 3 is a functional block diagram of a two-level network regulation strategy based on the Dueling-DQN neural network algorithm in accordance with the present application.
In the embodiment, as shown in fig. 2 and 3, in the step S4, when the required heat in the unit building is unchanged and the circulation flow is insufficient, the regulation strategy of the intelligent balancing device and the electric regulating valve is obtained by adopting the reinforcement learning algorithm to calculate, so as to promote the circulation flow in the unit building, which specifically includes:
when the heat required in the unit building is unchanged and the circulation flow is detected to be insufficient, the water supply circulation flow cannot meet the heat required in the unit building, and the maximum Q value is obtained by combining rule constraint analysis and durable-DQN reasoning based on the acquired heat supply operation data, weather data and indoor temperature as states, the final intelligent balance device and the regulation strategy output action of the electric regulating valve are issued to actual execution equipment for execution, the circulation flow in the unit building is promoted, and the rewarding feedback of energy saving amount and the data state before and after the execution action are obtained after the execution; storing the data in a database, and performing training update of the lasting-DQN model by extracting the data at regular time to replace the old model;
the Dueling-DQN neural network algorithm splits the network structure into two branches before an output layer under the condition of not changing the input and output of the Dueling-DQN model: the value estimation branch and the advantage estimation branch are used for respectively estimating the value of the state and the values of different actions in the state, and then the two network branches are linearly combined into an output layer to realize accurate estimation of the Q value.
In practical application, the lasting-DQN neural network algorithm splits the Q network into two parts, namely a cost function and a dominance function, wherein the cost function is only related to the state s and is unrelated to the action a to be adopted, and is recorded as V (s, w, beta); the dominance function is determined by the state s and action a, denoted as a (s, a, w, α), and the final Q value is expressed as:
Q(s,a,w,α,β)＝V(s,w,β)+A(s,a,w,α)；
w is a network parameter of the public part; α is a network parameter of the individual dominance function; beta is a network parameter of the individual cost function.
In the embodiment, the Dueling-DQN neural network algorithm is operated in a safe range by setting rule constraint, and meanwhile, parameters of the Dueling-DQN neural network are continuously optimized by setting a reward function;
and determining a plurality of safe action ranges according to the setting of different rule constraints, meeting the heat required in the unit building, calculating the action output of a water supply and return temperature set value through a lasting-DQN neural network algorithm, selecting the action with the largest Q value, judging whether the action is in the rule constraint range, if so, outputting the action to execute according to the lasting-DQN neural network algorithm, otherwise, outputting the action to execute according to the rule constraint, and executing punishment rewards for the action.
In this embodiment, the training update of the lasting-DQN model includes: taking the heat supply operation data, weather data and indoor temperature data stored in a database as the current state s, outputting an intelligent balancing device and an electric regulating valve action a through a lasting-DQN (direct current network) neural network algorithm, and obtaining instant rewards r after executing; when the environment changes to reach a new state s ', storing (s, a, r, s') as training sample data in a database, and introducing an experience playback mechanism for offline training of the model;
the lasting-DQN neural network algorithm trains updates at fixed times per day: loading current model parameters of the Dueling-DQN neural network algorithm, randomly extracting data samples from a memory bank each time for training to obtain new model parameters, and replacing an old model.
In this embodiment, the reward function settings of the lasting-DQN neural network algorithm include energy conservation and security constraint rewards.
In this embodiment, the Dueling-DQN neural network algorithm includes two common hidden layers, the value estimation branch includes one hidden layer, and the dominance estimation branch includes one hidden layer.
It should be noted that, the lasting-DQN neural network algorithm includes, in addition to the three neural networks of the input layer, the hidden layer and the output layer, two sub-network structures corresponding to the cost function and the dominance function network portions, respectively, and the output layer of the Q network is obtained by linearly combining the outputs of the cost function and the dominance function network.
The algorithm flow of the lasting-DQN neural network comprises the following steps:
algorithm input: iteration round number T, state characteristic dimension n, action set A, exploration rate epsilon, batch gradient decreasing sample number m, attenuation factor gamma, current Q network Q, target Q network Q', and target Q network parameter updating frequency P.
Algorithm output: q network parameters.
(1) Initializing the Q network parameter omega and the parameter omega '=omega of the target Q network Q', and initializing all states and values Q corresponding to actions. The experience playback unit D is initialized.
(2) And (5) performing iteration.
1) The first state S in the sequence of states is initialized and its eigenvector is Φ (S).
2) And taking phi (S) as input in the Q network, obtaining Q values of outputs corresponding to all actions, and selecting a corresponding action A by an E-greedy method.
3) And selecting and executing the current action A in the state S to obtain a characteristic vector phi (S') and a reward value R of the next state, and judging whether the state is a termination state.
4) { Φ (S), a, R, Φ (S') } is stored in the empirical playback unit D.
5) Let s=s'
6) Collecting m samples from an experience playback unit D, and calculating a current target Q value y j Where j=1, 2,..m, then:
7) According to the mean square error loss functionAll parameters ω of the Q network are updated with back propagation.
8) When i% p=1, the target Q network parameter ω' =ω is updated.
9) And (3) judging whether the S' is in a termination state, if so, ending the current round of iteration, and if not, turning to the step (2).
In the several embodiments provided in the present application, it should be understood that the disclosed system and method may be implemented in other manners as well. The system embodiments described above are merely illustrative, for example, of the flowcharts and block diagrams in the figures that illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that 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.
In addition, functional modules in the embodiments of the present application may be integrated together to form a single part, or each module may exist alone, or two or more modules may be integrated to form a single part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored on a computer readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method of the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
With the above-described preferred embodiments according to the present application as an illustration, the above-described descriptions can be used by persons skilled in the relevant art to make various changes and modifications without departing from the scope of the technical idea of the present application. The technical scope of the present application is not limited to the description, but must be determined according to the scope of claims.
Claims (9)
1. The heat supply secondary network regulation and control method based on the intelligent balance device and reinforcement learning is characterized by comprising the following steps:
s1, establishing a digital twin model of a heat supply secondary network unit building by adopting a mechanism modeling and data identification method;
s2, an intelligent balancing device, an electric regulating valve and a calorimeter for lifting the circulation flow in the unit building are connected in front of the unit building with unfavorable circulation flow characteristics, wherein the intelligent balancing device comprises a water pump, a manual balancing valve and a differential pressure controller;
step S3, based on a digital twin model of the unit building of the heat supply secondary network, historical heat supply operation data, weather data and indoor temperature are obtained, and the required heat in the unit building is obtained through calculation of a mixed prediction model and a model weight value;
s4, when the required heat in the unit building is unchanged and the circulation flow is insufficient, calculating and obtaining a regulation strategy of the intelligent balance device and the electric regulating valve by adopting a reinforcement learning algorithm, and improving the circulation flow in the unit building;
and S5, based on the digital twin model of the heat supply secondary network unit building, issuing and executing the regulation strategy after verifying and analyzing the energy conservation of the regulation strategy.
2. The method for regulating and controlling a heat supply secondary network according to claim 1, wherein in the step S1, a mechanism modeling and data identification method is adopted to build a digital twin model of a heat supply secondary network unit building, and the method specifically comprises the following steps:
establishing a digital twin model comprising a physical entity, a virtual entity, a twin data service and connecting elements among the components of the two-level network unit building;
the physical entity is a data source of the whole digital twin model;
the virtual entity performs simulation on the actual process of the physical entity, and performs analysis data, evaluation, prediction and control on the element data;
the twin data service integrates the physical space information and the virtual space information, ensures the real-time performance of data transmission, provides knowledge base data comprising intelligent algorithms, models, rule standards and expert experiences, and forms a twin database by fusing the physical information, the multi-time space associated information and the knowledge base data;
the connection between the components is used for realizing interconnection of the components, and the real-time acquisition and feedback of data are realized between the physical entity and the twin data service through the sensor and the protocol transmission specification;
the physical entity and the virtual entity perform data transmission through a protocol, physical information is transmitted to the virtual space in real time to update the correction model, and the virtual entity performs real-time control on the physical entity through an actuator;
the virtual entity and the twin data service are subjected to information transfer through a database interface;
and identifying the digital twin model, accessing the multi-working-condition real-time operation data of the secondary network unit building into the established digital twin model, and adopting a reverse identification method to carry out self-adaptive identification correction on the simulation result of the digital twin model to obtain the digital twin model of the heat supply secondary network unit building after the identification correction.
3. The method according to claim 1, wherein in step S3, based on the digital twin model of the unit building of the heat supply secondary network, historical heat supply operation data, weather data and indoor temperature are obtained, and the required heat in the unit building is obtained by calculating a mixed prediction model and a model weight value, specifically comprising:
based on a digital twin model of a unit building of a heat supply secondary network, acquiring historical multidimensional heat supply data of the unit building and corresponding historical demand heat real data, preprocessing to obtain a sample set of a demand heat prediction model in the unit building, wherein the sample set at least comprises historical indoor temperature, weather data, unit building water supply and return temperature, unit building water supply flow and unit building demand heat, and dividing the sample set into a training data set and a test data set;
selecting two training prediction models, and sequentially training the two training prediction models through a training data set to obtain corresponding heat demand prediction models in the unit building;
sequentially inputting the test data sets into the two unit building internal demand heat prediction models to obtain corresponding demand heat test data;
according to the demand heat test data and the historical demand heat real data, calculating weight values of two training prediction models by adopting an optimal weight combination strategy;
the multi-dimensional heat supply data of the unit building at the next moment is obtained and is input into the two unit building internal demand heat prediction models to obtain corresponding demand heat prediction values;
and calculating according to the demand heat predicted values of the two training predicted models and the corresponding weight values to obtain the final demand heat in the unit building.
4. The method for controlling a heat supply secondary network according to claim 3, wherein the calculating the weight values of the two training prediction models by adopting the optimal weight combination strategy according to the required heat test data and the historical required heat real data specifically comprises the following steps:
let the predicted value of the heat demand of the first training predictive model be f 1 The predicted value of the heat demand of the second training prediction model is f 2 Predicted value f of demand heat 1 The corresponding weight value is w 1 Predicted value f of demand heat 2 The corresponding weight value is w 2 ；
According to the heat demand test data and the historical heat demand real data, calculating to obtain predicted deviation values of two training predicted models, wherein the predicted deviation value of the first training predicted model is e 1 The prediction deviation value of the second training prediction model is e 2 ；
Calculating the sum of squares of the deviation of the two training prediction models to be S according to the weight value and the prediction deviation value;
setting an objective function of an optimal weight combination strategy as the square sum of deviation S minimization and constraint conditions as follows:
w 1 +w 2 ＝1；w 1 ＞0，w 2 ＞0；
and solving the objective function to obtain the optimal weight values of the two training prediction models.
5. The method according to claim 1, wherein in step S4, when the heat required in the unit building is unchanged and the circulation flow is insufficient, the method adopts reinforcement learning algorithm to calculate and obtain the regulation strategy of the intelligent balancing device and the electric regulating valve, and promotes the circulation flow in the unit building, and the method specifically comprises:
when the heat required in the unit building is unchanged and the circulation flow is detected to be insufficient, the water supply circulation flow cannot meet the heat required in the unit building, and the maximum Q value is obtained by combining rule constraint analysis and durable-DQN reasoning based on the acquired heat supply operation data, weather data and indoor temperature as states, the final intelligent balance device and the regulation strategy output action of the electric regulating valve are issued to actual execution equipment for execution, the circulation flow in the unit building is promoted, and the rewarding feedback of energy saving amount and the data state before and after the execution action are obtained after the execution; storing the data in a database, and performing training update of the lasting-DQN model by extracting the data at regular time to replace the old model;
the Dueling-DQN neural network algorithm splits the network structure into two branches before an output layer under the condition of not changing the input and output of the Dueling-DQN model: the value estimation branch and the advantage estimation branch are used for respectively estimating the value of the state and the values of different actions in the state, and then the two network branches are linearly combined into an output layer to realize accurate estimation of the Q value.
6. The method for regulating and controlling a heat supply secondary network according to claim 5, wherein: the method also comprises the steps of enabling the Dueling-DQN neural network algorithm to operate in a safe range by setting rule constraint, and continuously optimizing the parameters of the Dueling-DQN neural network by setting a reward function;
and determining a plurality of safe action ranges according to the setting of different rule constraints, meeting the heat required in the unit building, calculating the action output of a water supply and return temperature set value through a lasting-DQN neural network algorithm, selecting the action with the largest Q value, judging whether the action is in the rule constraint range, if so, outputting the action to execute according to the lasting-DQN neural network algorithm, otherwise, outputting the action to execute according to the rule constraint, and executing punishment rewards for the action.
7. The method for regulating and controlling a heat supply secondary network according to claim 5, wherein: the method also comprises the step of training and updating the Dueling-DQN model, and specifically comprises the following steps:
taking the heat supply operation data, weather data and indoor temperature data stored in a database as the current state s, outputting an intelligent balancing device and an electric regulating valve action a through a lasting-DQN (direct current network) neural network algorithm, and obtaining instant rewards r after executing; when the environment changes to reach a new state s ', storing (s, a, r, s') as training sample data in a database, and introducing an experience playback mechanism for offline training of the model;
the Dueling-DQN neural network algorithm trains updates at fixed times per day: loading current model parameters of the Dueling-DQN neural network algorithm, randomly extracting data samples from a memory bank each time for training to obtain new model parameters, and replacing an old model.
8. The method of claim 7, wherein the rewarding function setting of the Dueling-DQN neural network algorithm includes energy conservation and safety constraint rewards.
9. The method for regulating and controlling a heating secondary network according to claim 8, wherein the Dueling-DQN neural network algorithm comprises two common hidden layers, the value estimation branch comprises one hidden layer, and the dominance estimation branch comprises one hidden layer.
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