Intelligent management and control method and system for heat pump energy system

Abstract

The invention discloses an intelligent management and control method and system for a heat pump energy system. The intelligent management and control method comprises the steps that S1-S4, indoor and outdoor environment parameters of a target building, working parameters of energy supply equipment, underground soil environment parameters and total energy consumption of the heat pump energy system are collected; s5, predicting indoor and outdoor environment parameters and soil environment parameters of the target building in a period of time in the future based on the collected data by adopting an RNN prediction model; and S6, on the basis of the decomposed multi-objective evolutionary algorithm and the prediction result, working parameters of the heat pump energy system are optimized to achieve the purposes of minimizing total energy consumption, minimizing the working frequency of the heat pump and minimizing the power of the circulating water pump, and the working parameters of the heat pump energy system are output to control the heat pump energy system. According to the method and system, accurate prediction and intelligent adjustment can be achieved, the energy utilization efficiency is improved, the operation cost of the heat pump energy system is reduced, and the stability of the heat pump energy system is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of energy management and control systems, and specifically to an intelligent control method and system for a heat pump energy system, which are used to improve the comfort of a building's indoor environment, energy efficiency, and the stability of a heat pump energy system. Background Art

[0002] At present, residents in southern China generally rely on air conditioners and electric heaters for household heating during the cold winter season. Although this practice is convenient to operate, it is accompanied by a series of problems. First, the high-power operation of air conditioners and electric heaters puts families who use them for a long time under considerable economic pressure. Secondly, the energy utilization efficiency in the household heating mode is low, which is not conducive to energy conservation and emission reduction, and poses a challenge to environmental protection. In addition, there are potential safety hazards in the use of household heating equipment, such as equipment aging and operating errors, which may trigger safety accidents such as fire or electric shock.

[0003] It is worth noting that compared with the northern regions, the climate conditions in some areas of the south are quite similar to those in some areas of the north, and the heating needs are also common. Therefore, for these southern regions, the implementation of centralized heating has many advantages. Centralized heating can significantly improve energy utilization efficiency and reduce energy waste through centralized management and allocation of energy. At the same time, it can also reduce the investment cost of household heating equipment, further reducing the economic burden on residents.

[0004] With the continuous improvement of residents' quality of life, the application of five-constant centralized systems such as ceiling radiant heating and cooling has become more and more widespread in southern China. The system uses radiation to transfer heat, ensuring the uniform distribution of indoor temperature and greatly improving the comfort of heating and cooling. However, the traditional five-constant centralized system control method is relatively simple, mainly relying on manual setting of system control points, resulting in low energy utilization and high operation and maintenance costs.

[0005] In order to improve the energy efficiency and stability of the Wuheng centralized system, intelligent management and control technology has received increasing attention. At present, the comprehensive control and monitoring technology of the Wuheng heat pump system has made significant progress, but it still faces some challenges. Specifically, the existing intelligent management and control technology still lacks advanced algorithms for coupling intelligent simulation operations with operating status, which makes the system unable to accurately grasp the internal and external environment of the building and the energy consumption demand and supply capacity of each link of the system, thus affecting the stability and energy efficiency of the system. Therefore, how to achieve intelligent and precise control of the heat pump energy system has become a technical problem that needs to be overcome urgently. Summary of the invention

[0006] An object of the present invention is to solve at least the above problems and to provide at least the advantages which will be described hereinafter.

[0007] In order to solve the above technical problems, the present invention proposes an intelligent control method and system for a heat pump energy system. The method and system of the present invention can realize accurate prediction and intelligent regulation of the heat pump energy system, improve energy utilization efficiency, reduce the operating cost of the heat pump energy system, and enhance the stability of the heat pump energy system.

[0008] In order to achieve these purposes and other advantages according to the present invention, a method for intelligent management and control of a heat pump energy system is provided, comprising the following steps: S1. Collect indoor and outdoor environmental parameters of the target building; S2. Collecting the working parameters of energy supply equipment; S3. Collect soil environmental parameters under the target building; S4, collecting the total energy consumption of the heat pump energy system; S5, using the RNN prediction model, based on the data collected in steps S1 to S4, predict the indoor and outdoor environmental parameters of the target building and the soil environmental parameters under the target building in the future; S6. Adopting a decomposition-based multi-objective evolutionary algorithm, based on the prediction result of step S5, the operating parameters of the heat pump energy system are optimized to minimize the total energy consumption, minimize the operating frequency of the heat pump, and minimize the power of the circulating water pump. The operating parameters of the heat pump energy system are output to perform energy management and control on the heat pump energy system.

[0009] Preferably, in step S1, the indoor environmental parameters include the indoor temperature value, indoor humidity value, indoor carbon dioxide concentration value and indoor area of ​​the target building; the outdoor environmental parameters include the outdoor temperature value, outdoor humidity value, wind speed value and sunshine intensity value of the target building.

[0010] Preferably, in step S2, the energy supply equipment includes a heat pump host, a circulating water pump, water pipes and an indoor fan coil.

[0011] Preferably, in step S3, the soil environmental parameters include soil temperature, water temperature in the underground pipe, flow rate and valve opening.

[0012] Preferably, the RNN prediction model is trained with historical data to learn and simulate the patterns of changes in indoor and outdoor environmental parameters, soil environmental parameters, and working parameters of energy supply equipment over time.

[0013] Preferably, the decomposition-based multi-objective evolutionary algorithm decomposes the three objectives of minimizing total power consumption, minimizing heat pump operating frequency, and minimizing circulating water pump power into multiple sub-problems, searches for the optimal solution set through a parallel search strategy, and selects the optimal solution from the solution set for implementation based on actual needs.

[0014] Provided is an intelligent management and control system for a heat pump energy system, the heat pump energy system being used to execute the intelligent management and control method, the intelligent management and control system comprising: A data acquisition module, which is used to collect indoor and outdoor environmental parameters of the target building, working parameters of energy supply equipment, soil environmental parameters underground of the target building, and total energy consumption of the heat pump energy system; A prediction module, which is used to use a recurrent neural network to predict the indoor and outdoor environmental parameters of the target building and the soil environmental parameters under the target building within a period of time in the future based on the data collected by the data collection module; The optimization decision module is used to adopt a decomposition-based multi-objective evolutionary algorithm to optimize the working parameters of the heat pump energy system based on the prediction results of the prediction module, so as to minimize the total energy consumption, minimize the operating frequency of the heat pump, and minimize the power of the circulating water pump, and output the working parameters of the heat pump energy system to perform energy management and control of the heat pump energy system.

[0015] Preferably, the data acquisition module includes a sensor network, which is respectively arranged indoors and outdoors, in circulating water pumps, water pipes, indoor fan coils and soil of the target building, for real-time acquisition of various environmental parameters, soil environmental parameters, and working parameters of energy supply equipment.

[0016] Preferably, the intelligent management and control system further includes a user interface module, which is used to display the prediction results, optimized working parameters and current system status, and allows the user to adjust the optimization objectives or set priorities as needed.

[0017] The present invention has at least the following beneficial effects: First, by using the recurrent neural network (RNN) as the core algorithm of the prediction module, it can make full use of the characteristics of time series data and effectively capture the changing trends and periodic laws of environmental parameters (such as indoor temperature, outdoor temperature, humidity, soil temperature, etc.) over time; the cyclic structure of RNN enables it to process input sequences of any length, so that it can make high-precision predictions of environmental parameters in the future based on historical data, providing a reliable basis for optimizing the decision-making module; Second, the decomposition-based multi-objective evolutionary algorithm can simultaneously consider multiple optimization objectives (such as minimizing total energy consumption, minimizing heat pump operating frequency, and minimizing circulating water pump power), thereby achieving the optimal configuration of energy use while ensuring system performance; the algorithm searches for the optimal solution in the solution space by simulating natural selection and genetic mechanisms, and can handle complex nonlinear problems and constraints, thereby improving the overall energy efficiency of the heat pump energy system; Third, by combining the prediction module with the optimization decision module, this technical solution realizes the intelligent energy management and control of the heat pump energy system; this intelligent energy management and control system can automatically adjust the working parameters of the heat pump energy system according to the real-time collected data and prediction results to adapt to the changing environmental conditions and energy needs, which not only improves the energy efficiency and reliability of the heat pump energy system, but also reduces the cost and risk of manual intervention, providing strong support for the sustainable operation of the building; Fourth, it can achieve precise optimization of the working parameters of the heat pump energy system, thereby significantly reducing the total energy consumption of the system, the working frequency of the heat pump and the power of the circulating water pump, thereby achieving the goal of energy conservation and emission reduction, which is of great significance for alleviating the energy crisis, reducing greenhouse gas emissions, and promoting green buildings and sustainable development.

[0018] Other advantages, objectives and features of the present invention will be embodied in part through the following description, and in part will be understood by those skilled in the art through study and practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a flow chart of the intelligent control method of the heat pump energy system of the present invention. DETAILED DESCRIPTION

[0020] The present invention is further described in detail below in conjunction with the accompanying drawings so that those skilled in the art can implement the invention with reference to the description.

[0021] It should be noted that the experimental methods described in the following embodiments are conventional methods unless otherwise specified, and the reagents and materials are commercially available unless otherwise specified; in the description of the present invention, the orientation or positional relationship indicated by the terms is based on the orientation or positional relationship shown in the accompanying drawings, which is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0022] like Figure 1 As shown, an embodiment of the present invention provides an intelligent management and control method for a heat pump energy system, comprising the following steps: S1. Collect indoor and outdoor environmental parameters of the target building; specifically, indoor environmental parameters include indoor temperature, indoor humidity, indoor carbon dioxide concentration and indoor area of ​​the target building; outdoor environmental parameters include outdoor temperature, outdoor humidity, wind speed and sunshine intensity of the target building; indoor environmental parameters reflect the environmental requirements of the target building, and outdoor environmental parameters reflect the external environmental conditions of the target building, reflecting the difficulty or energy consumption of adjusting the outdoor temperature and humidity conditions to the indoor state through the heat pump energy system.

[0023] S2. Collect the working parameters of the energy supply equipment; specifically, the energy supply equipment includes a heat pump host, a circulating water pump, water pipes and indoor fan coils; for example, the heat pump host is the core equipment of the soil source heat pump energy system, responsible for extracting heat from the soil and transferring it to the indoor space. The heat pump host is usually composed of main components such as a compressor, a condenser, an evaporator and an expansion valve. Through the collaboration of these components, heat transfer and transportation are achieved; optionally, the working parameters of the heat pump host include the heat pump operating frequency, set temperature, hysteresis temperature, water discharge temperature, electric heating allowable temperature, electric heating hysteresis temperature, system pressure, etc.; the circulating water pump plays a key role in the soil source heat pump energy system, responsible for circulating water or other heat exchange media between the underground buried pipes and the heat pump host. Through the operation of the circulating water pump, the soil source heat pump energy system can continuously extract or release heat from the soil, thereby maintaining the comfort of the indoor space. ; Optionally, the working parameters of the circulating water pump include flow rate, speed and power; the water pipe is the key part connecting the heat pump host, underground buried pipes and indoor terminal equipment, and is responsible for transmitting heat or cold from underground to the heat pump host, and then transporting heat or cold to the room; Optionally, the working parameters of the water pipe include flow rate, water temperature and flow rate; the indoor fan coil is the indoor terminal equipment in the soil source heat pump energy system, which is responsible for releasing the heat or cold provided by the heat pump host into the indoor space, thereby adjusting the indoor temperature. The indoor fan coil is usually composed of a fan and a heat exchanger, which can efficiently adjust the indoor temperature; Optionally, the working parameters of the indoor fan coil include air volume, wind speed and power; the control system is the brain of the energy supply equipment, responsible for monitoring and adjusting the operating status of the energy supply equipment, and controlling the stable and orderly operation of the energy supply equipment through precise sensor networks and intelligent management and control methods. Preferably, the energy supply equipment working parameters select equipment status parameters (on, off and standby of heat pumps, circulating water pumps and indoor fan coils) and setting parameters (setting parameters of heat pumps, circulating water pumps and indoor fan coils, such as target temperature, flow, pressure, wind speed, etc.).

[0024] S3. Collect soil environmental parameters underground of the target building. Specifically, soil environmental parameters include soil temperature, water temperature in underground pipes, flow rate and valve opening. After the soil source heat pump energy system is in operation, it will continuously absorb or release heat from the soil, causing changes in soil temperature.

[0025] S4. Collect the total energy consumption of the heat pump energy system; specifically, the total energy consumption is measured by the total power consumption of the entire energy system, which is detected and collected through an electric energy meter.

[0026] During the data collection process, a time synchronization mechanism is used to ensure that the timestamps of all collected data are consistent, which facilitates subsequent analysis and prediction.

[0027] S5. Using the RNN prediction model, based on the data collected in steps S1 to S4, the indoor and outdoor environmental parameters of the target building and the soil environmental parameters under the target building in the future are predicted; using a recurrent neural network (RNN), combined with a long short-term memory (LSTM) unit or a gated recurrent unit (GRU), to enhance the modeling ability of time series data, based on the collected data, the following parameters in the future are predicted with high precision, preferably, indoor temperature value, indoor humidity value and indoor carbon dioxide concentration value, outdoor environmental parameters of the target building outdoor temperature value, outdoor humidity value, wind speed value and sunshine intensity value, soil temperature and water temperature in the underground buried pipe; preferably, before the prediction, it also includes data preprocessing steps, such as outlier detection and processing, missing value filling, etc., to improve the accuracy of the prediction. Preferably, the RNN prediction model can learn and simulate the law of changes in environmental parameters and working parameters of energy supply equipment over time through historical data training to improve the accuracy of the prediction; preferably, when the RNN prediction model is trained through historical data, the model parameters are optimized by cross-validation and grid search methods to improve the accuracy of the prediction. At the same time, the RNN prediction model has online learning capabilities and can be continuously updated and optimized according to actual operating data.

[0028] S6. Adopting a decomposition-based multi-objective evolutionary algorithm, based on the prediction results of step S5 (indoor temperature value, indoor humidity value and indoor carbon dioxide concentration value, outdoor temperature value, outdoor humidity value, wind speed value and sunshine intensity value, soil temperature and water temperature in underground pipes), the working parameters of the heat pump energy system (the on, off and standby state parameters of the heat pump, circulating water pump and indoor fan coil unit, the setting parameters of the heat pump, circulating water pump and indoor fan coil unit, such as target temperature, flow rate, pressure, wind speed, etc.) are optimized to minimize the total energy consumption, minimize the operating frequency of the heat pump, minimize the power of the circulating water pump, and output the working parameters of the heat pump energy system to perform energy management and control on the heat pump energy system. The minimization of total energy consumption aims to reduce energy consumption by adjusting the working parameters of the heat pump energy system, thereby reducing operating costs; by reducing the working frequency of the heat pump, its energy consumption and wear can be reduced, the equipment life can be extended, and noise pollution can be reduced; reducing the power of the circulating water pump can reduce the energy consumption of the water circulation system; specifically, the multi-objective evolutionary algorithm based on decomposition adopts elite retention strategy, dynamic weight adjustment and other methods in the solution process to maintain the diversity and convergence of the solution set. At the same time, the algorithm has an adaptive adjustment mechanism, which can dynamically adjust parameters according to feedback information during the optimization process. Preferably, according to the output working parameters of the heat pump energy system (such as the working parameters of the heat pump host and the circulating water pump), the working parameters of energy equipment such as the heat pump host and the circulating water pump can be automatically adjusted to achieve the optimization of energy use and improve the stability of the operation of the heat pump energy system.

[0029] This implementation case uses the RNN prediction model to learn and simulate the laws of changes in environmental parameters and working parameters of energy supply equipment over time, so as to achieve accurate prediction and simulation of future environmental parameters of the target heat pump energy system, and provide a data basis for the adjustment of control parameters. By adopting a multi-objective evolutionary algorithm based on decomposition, not only indoor environmental parameters and energy equipment working parameters are considered, but also external environmental data. Under the premise of reducing total energy consumption, the start-stop frequency of energy supply equipment can be reduced, the stable and orderly operation of energy supply equipment can be controlled, and the comfort of the indoor environment can be guaranteed.

[0030] In another technical solution, the RNN prediction model is trained with historical data to learn and simulate the patterns of changes in indoor and outdoor environmental parameters, soil environmental parameters, and working parameters of energy supply equipment over time.

[0031] One embodiment includes: Data Collection: Indoor environmental data: collect indoor temperature, indoor humidity and indoor carbon dioxide concentration values ​​for the past year, and record them once every minute; Outdoor environmental data: collect outdoor temperature, outdoor humidity, wind speed and sunshine intensity values ​​at the same time, and record them once every hour.

[0032] Soil environmental parameters: For the ground source heat pump energy system, data on soil temperature, water temperature in underground pipes, flow rate and valve opening are collected and recorded every hour.

[0033] Energy supply equipment parameters: record the on, off and standby status parameters of the heat pump, circulating water pump and indoor fan coil unit, and the setting parameters of the heat pump, circulating water pump and indoor fan coil unit, such as target temperature, flow, pressure, wind speed, etc., once every hour.

[0034] Data preprocessing: clean the data, remove outliers and missing values; normalize and scale all data to between 0 and 1 to improve model training efficiency. Prediction model construction: use RNN architecture to build the prediction model. The input layer receives all the preprocessed historical data mentioned above. The hidden layer is designed as 4 layers of LSTM units. Each layer is followed by a Dropout layer to prevent overfitting. The output layer predicts the indoor environment parameters, outdoor environment parameters, soil environment parameters and working parameters of energy supply equipment in the next 24 hours.

[0035] Prediction model training and optimization: 5-fold cross validation is used to evaluate model performance to ensure the generalization ability of the model on different data subsets. The grid search method is used to adjust hyperparameters such as the number of LSTM layers, number of units, learning rate, batch size, etc. to maximize prediction accuracy. The mean square error (MSE) is selected as the loss function, and the optimizer is Adam.

[0036] Prediction model verification and deployment: Test the model performance on the validation set to ensure that the prediction error is within an acceptable range, deploy the trained model to the control system of the ground-source heat pump energy system, receive new data in real time, and output prediction results.

[0037] In another technical solution, the decomposition-based multi-objective evolutionary algorithm decomposes the three objectives of minimizing total power consumption, minimizing heat pump operating frequency, and minimizing circulating water pump power into multiple sub-problems, searches for the optimal solution set through a parallel search strategy, and selects the optimal solution from the solution set according to actual needs.

[0038] One embodiment includes: T1. Problem definition: 1. Determine the decision variables: x1: indoor environmental parameters; x2: outdoor environmental parameters; x3: soil environmental parameters; y: energy supply equipment working parameters; 2. Determine the objective function: Minimizing the total power consumption E can be expressed as a function of decision variables: E=f1(x1,x2,x3,y); The heat pump operating frequency V is minimized and can be expressed as a function of the decision variables: V = f2 (x1, x2, x3, y); The circulating water pump power W is minimized and can be expressed as a function of decision variables: W = f3 (x1, x2, x3, y); 3. Set constraints: The soil temperature and water temperature in the outdoor environmental parameter x2 and the soil environmental parameter x3 remain unchanged, and are equal to the predicted value output by the RNN prediction model or the collected value; The indoor environment parameter x1 is set to a specific range value, such as a temperature of 20°C to 26°C, a humidity of 40% to 60%, or is equal to the predicted value output by the RNN prediction model or the collected value; The operating parameters of the energy supply equipment are set within the physical limitations of the equipment itself; 4. Establish mathematical model: The relationship between the total power E, the heat pump operating frequency V, the circulating water pump power W and the decision variables can be expressed as a nonlinear function: E=a1⋅x1 2 +b1⋅x2 2 +c1⋅x3 2+d1⋅y 2 +e1⋅x1⋅x2+f1⋅x1⋅x3+g1⋅x1⋅y+h1⋅x2⋅x3+i1⋅x2⋅y+j1⋅x3⋅y+k1⋅x1⋅x2⋅x3+l1⋅x1⋅x2⋅y+m1⋅x2⋅x3⋅y+n1⋅x1⋅x2⋅x3⋅y; V=a2⋅x1 2 +b2⋅x2 2 +c2⋅x3 2 +d2⋅y 2 +e2⋅x1⋅x2+f2⋅x1⋅x3+g2⋅x1⋅y+h2⋅x2⋅x3+i2⋅x2⋅y+j2⋅x3⋅y+k2⋅x1⋅x2⋅x3+l2⋅x1⋅x2⋅y+m2⋅x2⋅x3⋅y+n2⋅x1⋅x2⋅x3⋅y; W=a3⋅x1 2 +b3⋅x2 2 +c3⋅x3 2 +d3⋅y 2 +e3⋅x1⋅x2+f3⋅x1⋅x3+g3⋅x1⋅y+h3⋅x2⋅x3+i3⋅x2⋅y+j3⋅x3⋅y+k3⋅x1⋅x2⋅x3+l3⋅x1⋅x2⋅y+m3⋅x2⋅x3⋅y+n3⋅x1⋅x2⋅x3⋅y; Among them, a1, b1, c1.....n1, a2, b2 , c2, ....n2, a3, b3, c3, ....n3 are the coefficients to be determined, which can be obtained by fitting historical data; T2. Algorithm settings: 1. Based on the MOEA / D algorithm framework, the nonlinear programming algorithm (particle swarm algorithm) is used to solve the mathematical model and obtain the optimal values ​​of x1, x2, x3, and y; 2. Set the fitness function: used to evaluate the quality of each solution, specifically: Fitness function of total power consumption E: F1(x,y)=E, where x=[x1,x2,x3] represents environmental parameters, and y represents the working parameters of the energy supply equipment. This fitness function directly reflects the total power consumption of the system. The smaller the value, the better the solution. Fitness function of heat pump operating frequency V: F2(x,y)=V, this fitness function evaluates the operating frequency of the heat pump. Similarly, the smaller the value, the better the solution; Fitness function of circulating water pump power W: F3(x,y)=W, this fitness function reflects the power consumption of the circulating water pump. The smaller the value, the better the solution. In addition, in MOEA / D, since the algorithm is based on decomposition, it decomposes the multi-objective optimization problem into a series of single-objective sub-problems. Each sub-problem will have a weight vector associated with it, which is used to weigh between multiple objectives. The algorithm will then solve these sub-problems independently and evaluate the pros and cons of the solutions by combining multiple objectives into a single objective value through aggregation functions. However, in this application, there are already three clear objectives, and each objective has a clear fitness function, which can be used directly to evaluate the pros and cons of the solution without the need for additional aggregation functions. Within the framework of MOEA / D, this is achieved by assigning a weight vector to each sub-problem, which reflects the priority or importance of the objective.

[0039] 3. Select genetic operations (crossover or mutation) to generate new solutions, introduce parallel search strategies, and accelerate algorithm convergence; T3. Data preparation: Collect historical data, including energy supply equipment operating parameters and environmental data; Use data preprocessing techniques, such as data cleaning and normalization, to ensure data quality; The RNN prediction model of this application is used to predict future data.

[0040] T4. Algorithm execution: Initialize the population, generate a set of initial solutions, iteratively perform genetic operations, generate new solutions and evaluate their fitness, update the solution set, retain the optimal solution, and monitor algorithm performance.

[0041] T5, solution set evaluation and selection: In the solution set, the optimal solution is selected according to the preset priority and speed conditions; In the above technical solution, by implementing a decomposition-based multi-objective evolutionary algorithm, the intelligent management and control method can achieve more efficient energy utilization, reduce total power consumption, heat pump operating frequency and circulating water pump power, and improve the comfort of the indoor environment.

[0042] Based on the same inventive concept, the present invention provides an intelligent management and control system for a heat pump energy system, the heat pump energy system is used to execute the intelligent management and control method, and the heat pump energy system includes: A data acquisition module is used to collect indoor and outdoor environmental parameters of the target building, working parameters of the energy supply equipment, soil environmental parameters underground of the target building and the total energy consumption of the heat pump energy system; optionally, the indoor environmental parameters are derived from various monitoring probes indoors of the target building, such as temperature and humidity probes, and carbon dioxide concentration probes; the outdoor environmental parameters are derived from various monitoring probes outdoors of the target building, such as temperature and humidity probes, sunshine intensity probes and anemometers; optionally, the working parameters of the energy supply equipment are derived from their respective monitoring instruments, such as ammeters, voltmeters, flow meters, thermometers, etc.; it is also necessary to simultaneously record the specific time of data collection, such as date, time (hours, minutes, etc.); preferably, the data acquisition module includes a sensor network, which is respectively arranged indoors, outdoors, in circulating water pumps, water pipes, indoor fan coils and soil of the target building, for real-time acquisition of various environmental parameters, soil environmental parameters, and working parameters of the energy supply equipment.

[0043] A prediction module, which is used to use a recurrent neural network to predict the indoor and outdoor environmental parameters of the target building and the soil environmental parameters under the target building within a period of time in the future based on the data collected by the data collection module; The optimization decision module is used to optimize the working parameters of the heat pump energy system based on the prediction results of the prediction module by using a multi-objective evolutionary algorithm based on decomposition, so as to minimize the total energy consumption, minimize the working frequency of the heat pump, and minimize the power of the circulating water pump, and output the working parameters of the heat pump energy system to perform energy management and control on the heat pump energy system. The output working parameters of the heat pump energy system are preferably the state parameters of the opening, closing and standby of the heat pump, circulating water pump and indoor fan coil, and the setting parameters of the heat pump, circulating water pump and indoor fan coil, such as target temperature, flow, pressure, and wind speed.

[0044] In the above technical solution, by adopting the recurrent neural network (RNN) as the core algorithm of the prediction module, the characteristics of time series data can be fully utilized to effectively capture the changing trends and periodic laws of environmental parameters (such as indoor temperature, outdoor temperature, humidity, soil temperature, etc.) over time; the cyclic structure of RNN enables it to process input sequences of any length, so that it can make high-precision predictions of environmental parameters in the future based on historical data, providing a reliable basis for optimizing the decision-making module; The optimization decision module adopts a multi-objective evolutionary algorithm based on decomposition, which can simultaneously consider multiple optimization objectives (such as minimizing total energy consumption, minimizing heat pump operating frequency, and minimizing circulating water pump power), so as to achieve the optimal configuration of energy use while ensuring system performance; the algorithm searches for the optimal solution in the solution space by simulating natural selection and genetic mechanisms, and can handle complex nonlinear problems and constraints, thereby improving the overall energy efficiency of the heat pump energy system; By combining the prediction module with the optimization decision module, this technical solution realizes the intelligent energy management and control of the heat pump energy system; this intelligent energy management and control system can automatically adjust the working parameters of the heat pump energy system according to the real-time collected data and prediction results to adapt to the changing environmental conditions and energy needs, which not only improves the energy efficiency and reliability of the heat pump energy system, but also reduces the cost and risk of manual intervention, providing strong support for the sustainable operation of the building; Based on this technical solution, it is possible to achieve precise optimization of the operating parameters of the heat pump energy system, thereby significantly reducing the total energy consumption of the system, the operating frequency of the heat pump and the power of the circulating water pump, thereby achieving the goal of energy conservation and emission reduction, which is of great significance for alleviating the energy crisis, reducing greenhouse gas emissions, and promoting green buildings and sustainable development.

[0045] After testing, the prediction module of this intelligent management and control system has a prediction accuracy of up to 90.84%. The combination of the prediction module and the optimization decision-making module reduces the operating cost per square meter and saves energy by up to 41% compared with pure manual control.

[0046] In another technical solution, the intelligent management and control system of the heat pump energy system also includes a user interface module, which is used to display the prediction results, optimized working parameters and current system status, and allows the user to adjust the optimization goals or set priorities as needed.

[0047] In the above technical solution, the user interface module provides an intuitive and easy-to-use interface, allowing users to clearly view the prediction results, optimized working parameters and the current operating status of each system; users can adjust the optimization goals or set the priorities of different goals through the interface according to actual needs to achieve more personalized energy management and control strategies.

[0048] The division of modules in the embodiments of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation. In addition, each functional module in each embodiment of the present invention may be integrated into a processor, or may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated modules may be implemented in the form of hardware or in the form of software functional modules.

[0049] The present invention provides an electronic device, comprising: one or more processors; and a memory in communication with the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor executes the intelligent management and control method. The electronic device may be any terminal device including a mobile phone, a laptop computer, a desktop computer, a tablet computer, a car computer, etc.

[0050] The present invention provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the intelligent management and control method is implemented.

[0051] Through the description of the above implementation mode, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software plus necessary general hardware, and of course, it can also be implemented by special hardware including special integrated circuits, special CPUs, special memories, special components, etc. In general, all functions completed by computer programs can be easily implemented by corresponding hardware, and the specific hardware structure used to implement the same function can also be various, such as analog circuits, digital circuits or special circuits. However, for the present invention, software program implementation is a better implementation mode in more cases. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a readable storage medium, such as a computer floppy disk, a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a disk or an optical disk, etc., including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present invention.

[0052] Although the embodiments of the present invention have been disclosed as above, they are not limited to the applications listed in the specification and the implementation modes, and they can be fully applied to various fields suitable for the present invention. For those familiar with the art, additional modifications can be easily implemented. Therefore, without departing from the general concept defined by the claims and the scope of equivalents, the present invention is not limited to the specific details and the illustrations shown and described herein.

Claims

1. An intelligent control method for a heat pump energy system, characterized in that: The following steps are involved: S1. Collect indoor and outdoor environmental parameters of the target building; S2. Collecting the working parameters of energy supply equipment; S3. Collect soil environmental parameters under the target building; S4, collecting the total energy consumption of the heat pump energy system; S5, using the RNN prediction model, based on the data collected in steps S1 to S4, predict the indoor and outdoor environmental parameters of the target building and the soil environmental parameters under the target building in the future; S6. Adopting a decomposition-based multi-objective evolutionary algorithm, based on the prediction result of step S5, the operating parameters of the heat pump energy system are optimized to minimize the total energy consumption, minimize the operating frequency of the heat pump, and minimize the power of the circulating water pump. The operating parameters of the heat pump energy system are output to perform energy management and control on the heat pump energy system.

2. The intelligent control method of the heat pump energy system according to claim 1, characterized in that: In step S1, the indoor environmental parameters include the indoor temperature value, indoor humidity value, indoor carbon dioxide concentration value and indoor area of ​​the target building; the outdoor environmental parameters include the outdoor temperature value, outdoor humidity value, wind speed value and sunshine intensity value of the target building.

3. The intelligent control method of the heat pump energy system according to claim 1, characterized in that: In step S2, the energy supply equipment includes a heat pump host, a circulating water pump, water pipes and an indoor fan coil.

4. The intelligent control method of the heat pump energy system according to claim 1, characterized in that: In step S3, the soil environmental parameters include soil temperature, water temperature in the underground pipe, flow rate and valve opening.

5. The intelligent control method of the heat pump energy system according to claim 1, characterized in that: The RNN prediction model is trained through historical data to learn and simulate the patterns of changes in indoor and outdoor environmental parameters, soil environmental parameters, and working parameters of energy supply equipment over time.

6. The intelligent control method of the heat pump energy system according to claim 1, characterized in that: The decomposition-based multi-objective evolutionary algorithm decomposes the three objectives of minimizing total power consumption, minimizing heat pump operating frequency, and minimizing circulating water pump power into multiple sub-problems, searches for the optimal solution set through a parallel search strategy, and selects the optimal solution from the solution set according to actual needs for implementation.

7. Intelligent management and control system of heat pump energy system, characterized in that: The intelligent management and control system is used to execute the intelligent management and control method according to any one of claims 1 to 6, and the intelligent management and control system includes: A data acquisition module, which is used to collect indoor and outdoor environmental parameters of the target building, working parameters of energy supply equipment, soil environmental parameters underground of the target building, and total energy consumption of the heat pump energy system; A prediction module, which is used to use a recurrent neural network to predict the indoor and outdoor environmental parameters of the target building and the soil environmental parameters under the target building within a period of time in the future based on the data collected by the data collection module; The optimization decision module is used to adopt a decomposition-based multi-objective evolutionary algorithm to optimize the working parameters of the heat pump energy system based on the prediction results of the prediction module, so as to minimize the total energy consumption, minimize the operating frequency of the heat pump, and minimize the power of the circulating water pump, and output the working parameters of the heat pump energy system to perform energy management and control of the heat pump energy system.

8. The intelligent management and control system of the heat pump energy system according to claim 7, characterized in that: The data acquisition module includes a sensor network, which is arranged indoors and outdoors, in circulating water pumps, water pipes, indoor fan coils and soil of the target building, and is used to obtain various environmental parameters, soil environmental parameters, and working parameters of energy supply equipment in real time.

9. The intelligent management and control system of the heat pump energy system according to claim 7, characterized in that: The intelligent management and control system also includes a user interface module, which is used to display the prediction results, optimized working parameters and current system status, and allows the user to adjust the optimization goals or set priorities as needed.

Images