Energy consumption optimization monitoring method and system for heat supply network fused with phase change material and related device

Abstract

The invention provides a heat supply network energy consumption optimization monitoring method and system fused with a phase-change material and a related device. The method comprises the following steps that S1, temperature information of the phase-change material and operation information of a heat supply network are collected in real time; s2, outputting a heat supply parameter optimization instruction based on the collected temperature information of the phase change material and the operation information of the heat supply pipe network in combination with a pre-constructed energy consumption optimization model; s3, according to the obtained heat supply parameter optimization instruction, the heat source output, the valve opening degree and the circulating pump frequency of the heat supply pipe network are adjusted; the system solves the problems that a traditional system is lagged in regulation and control, depends on a fixed threshold value, is weak in man-machine interaction and the like, and provides reliable technical support for achieving intelligent, efficient and low-carbon operation of a heat supply network.

Description

Technical Field

[0001] This invention belongs to the field of intelligent monitoring and energy consumption optimization technology for heating networks, specifically relating to a method, system and related device for energy consumption optimization monitoring of heating networks that integrates phase change materials. Background Technology

[0002] With the expansion of centralized heating, energy consumption management of heating networks has become a crucial aspect of energy conservation and emission reduction. Existing heating network energy consumption monitoring systems primarily rely on statistical analysis of network operating parameters, including flow rate, pressure, and temperature. These systems can only provide real-time display and historical data tracing, lacking proactive optimization and control capabilities. Meanwhile, phase change materials (PCMs), with their high heat storage density and constant temperature during phase change, have been increasingly applied to heat storage in heating networks. However, in current technologies, PCMs are merely used as simple heat storage carriers; their thermal characteristics are not deeply integrated with energy consumption monitoring. This prevents the use of PCM's heat storage and release status to guide network energy allocation, leading to problems such as insufficient handling of peak-valley load differences and low energy utilization efficiency. Furthermore, traditional energy consumption control often employs fixed thresholds or simple proportional adjustments, which are ill-suited to adapt to the dynamic changes in PCM's heat storage and release characteristics and network load. This results in low control precision and an inability to achieve accurate energy consumption management. Therefore, a technical solution that deeply integrates the thermal characteristics of PCMs with energy consumption monitoring and optimization is urgently needed to address the shortcomings of existing systems. Summary of the Invention

[0003] The purpose of this invention is to provide a method, system and related device for optimizing and monitoring the energy consumption of heating networks that incorporates phase change materials, thereby overcoming the aforementioned shortcomings in the prior art.

[0004] To achieve the above objectives, the technical solution adopted by the present invention is as follows: In a first aspect, the present invention provides a method for optimizing and monitoring the energy consumption of a heating network incorporating phase change materials, comprising the following steps: S1. Real-time acquisition of temperature information of phase change materials and operation information of heating pipe network; S2. Based on the collected temperature information of the phase change material and the operation information of the heating network, combined with the pre-built energy consumption optimization model, output the heating parameter optimization command; S3. Based on the obtained heating parameter optimization instructions, adjust the heat source output, valve opening and circulation pump frequency of the heating network.

[0005] Preferably, the temperature information of the phase change material includes the real-time temperature and heat storage / release rate of the phase change material, as well as the surface temperature of the phase change material container; the operating information of the heating network includes network power data, heat data, network medium flow rate, and pressure.

[0006] Preferably, the method for constructing the pre-built energy consumption optimization model is as follows: A pre-built energy consumption optimization model is obtained by using the random forest algorithm or the BP neural network algorithm.

[0007] Preferably, the phase change material is paraffin, fatty acid, or a composite phase change material.

[0008] Preferably, the phase change material has a thermally enhanced structure embedded inside.

[0009] Secondly, the present invention provides a heating network energy consumption optimization and monitoring system incorporating phase change materials, comprising: The operation information acquisition unit is used to collect temperature information of phase change materials and operation information of heating pipe network in real time; The heating parameter output unit is used to output heating parameter optimization instructions based on the collected temperature information of the phase change material and the operation information of the heating pipeline network, combined with the pre-built energy consumption optimization model. The instruction execution unit is used to optimize instructions based on the obtained heating parameters, and adjust the heat source output, valve opening and circulation pump frequency of the heating network.

[0010] Thirdly, the present invention provides an electronic device including a processor and a memory, wherein the memory stores computer instructions, and when the computer instructions are executed by the processor, the electronic device performs the method described thereon.

[0011] Fourthly, the present invention provides a computing device cluster, comprising at least one computing device, each computing device including a processor and a memory; The processor of the at least one computing device is configured to execute instructions stored in the memory of the at least one computing device to cause the cluster of computing devices to perform the method according to the method.

[0012] Fifthly, the present invention provides a computer program product, the computer program product including computer-executable instructions, which, when executed, implement the method described.

[0013] In a sixth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the method described herein.

[0014] Compared with the prior art, the beneficial effects of the present invention are: This invention provides a heating network energy consumption optimization and monitoring method that integrates phase change materials. By deeply integrating the heat storage and release characteristics of phase change materials with the operation monitoring of heating network, and introducing a machine learning-based energy consumption optimization model for intelligent regulation, it solves the pain points of traditional systems such as lagging regulation, reliance on fixed thresholds, and weak human-computer interaction. It provides reliable technical support for realizing the intelligent, efficient, and low-carbon operation of heating networks.

[0015] This invention enables proactive optimization and precise control of energy consumption in heating networks. By collecting multi-dimensional data such as phase change material temperature, heat storage and release rate, and pipeline flow, pressure, and energy consumption in real time, the model constructed using random forest or BP neural network algorithms outputs optimization instructions to dynamically adjust heat source output, valve opening, and circulation pump frequency. This effectively smooths out load peak-valley differences and improves energy utilization efficiency, resulting in energy consumption reduction in actual measurements. Attached Figure Description

[0016] Figure 1 This is a flowchart illustrating an embodiment of the present invention. Detailed Implementation

[0017] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0018] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0019] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0020] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0021] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0022] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0023] Example 1 This embodiment provides a method for optimizing and monitoring the energy consumption of a heating network incorporating phase change materials, including the following steps: S1. Real-time acquisition of temperature information of phase change materials and operation information of heating pipe network; S2. Based on the collected temperature information of the phase change material and the operation information of the heating network, combined with the pre-built energy consumption optimization model, output the heating parameter optimization command; S3. Based on the obtained heating parameter optimization instructions, adjust the heat source output, valve opening and circulation pump frequency of the heating network.

[0024] Example 2 This embodiment provides a heating network energy consumption optimization and monitoring system incorporating phase change materials, including: The operation information acquisition unit is used to collect temperature information of phase change materials and operation information of heating pipe network in real time; The heating parameter output unit is used to output heating parameter optimization instructions based on the collected temperature information of the phase change material and the operation information of the heating pipeline network, combined with the pre-built energy consumption optimization model. The instruction execution unit is used to optimize instructions based on the obtained heating parameters, and adjust the heat source output, valve opening and circulation pump frequency of the heating network.

[0025] Example 3 This embodiment provides a heating network energy consumption optimization monitoring system incorporating phase change materials, including a phase change thermal storage unit, a data acquisition unit, a data processing unit, a parameter control unit, and a remote monitoring center. These units work collaboratively to form a closed-loop management process. The phase change heat storage unit is deployed at a key location in the heat exchange station of the heating network. It adopts a sealed heat storage container filled with phase change material. The phase change material is selected from paraffin-based, fatty acid-based, or composite phase change materials to ensure compatibility with the operating temperature of the heating network. Copper fins or graphene thermally conductive layers are embedded inside the heat storage container as a thermal conductivity enhancement structure to improve the heat storage and release rate of the phase change material and avoid local overheating or overcooling.

[0026] The data acquisition unit consists of a temperature sensor, an energy consumption sensor, a flow sensor, and a pressure sensor, wherein: The temperature sensors are divided into two groups: one group is embedded inside the phase change material to collect the real-time temperature and heat storage / release rate of the phase change material; the other group is installed on the wall of the heat storage container to collect the surface temperature of the container.

[0027] The energy consumption sensor is deployed on the main heating pipe of the heat exchange station to collect the electrical and thermal energy of the pipeline network per unit time in real time.

[0028] The flow sensor and pressure sensor are installed on the main pipeline and branch nodes of the pipeline network, respectively, to collect medium flow and pressure data. The acquisition frequency is 1-5Hz to meet the dynamic monitoring requirements.

[0029] The data processing unit and the data acquisition unit establish a connection via a LoRa or 5G communication module to receive real-time acquired data. A multi-dimensional energy consumption optimization model is constructed using a random forest algorithm or a BP neural network algorithm. The real-time acquired temperature information of the phase change material and the operating information of the heating network are used as inputs to the multi-dimensional energy consumption optimization model, and the output is an optimization command for heating parameters.

[0030] The parameter control unit is communicatively connected to the data processing unit and the heating network actuator, which includes a heat source unit, regulating valves, and a circulating pump. Upon receiving optimization commands, it adjusts the heat source output, valve opening, and circulating pump operating frequency via electric actuators to achieve precise control of heating parameters. Simultaneously, the parameter control unit has a built-in feedback detection module that collects network operation data after parameter adjustments and feeds it back to the data processing unit for dynamic iterative updates of the model.

[0031] The remote monitoring center includes a data storage module, a visualization module, and an alarm module. The data storage module uses a cloud server to store historical data, optimized model parameters, and control records, supporting data retrospective analysis. The visualization module uses a system flowchart as its core, intuitively presenting the entire process of "thermal characteristic acquisition → energy consumption modeling → parameter optimization → operation monitoring → feedback calibration," while also displaying the phase change material status, pipeline energy consumption data, and optimization effects in chart form. The alarm module triggers audible and visual alarms and pushes notifications to management personnel terminals when energy consumption exceeds limits, phase change material temperature is abnormal, or sensors malfunction.

[0032] Example 4 In this embodiment, a paraffin-based phase change material is selected, with a phase change temperature of 50℃. The heat storage container adopts a stainless steel sealed structure, with embedded copper fins as a thermal conductivity enhancement structure. The fin spacing is 5cm to improve thermal conductivity. The temperature sensor of the data acquisition unit uses a PT100 platinum resistance sensor with an acquisition accuracy of ±0.1℃; the energy consumption sensor uses an ultrasonic energy consumption monitor; the flow sensor uses an electromagnetic flow meter; and the pressure sensor uses a diffused silicon pressure transmitter, with the acquisition frequency set to 3Hz. The machine learning algorithm module of the data processing unit adopts the random forest algorithm, based on 500 sets of historical operating data, covering different seasons and different load conditions for model training. The model input parameters include phase change material temperature, heat storage and release rate, pipeline flow, pressure, and energy consumption data. The output parameters are heat source output adjustment value, valve opening, and circulating pump frequency. The actuator of the parameter control unit adopts an electric regulating valve with an adjustment accuracy of ±1% and a variable frequency circulating pump, supporting remote control and automatic adjustment. The remote monitoring center uses Alibaba Cloud servers to store data, and the visual interface is deployed via a web platform. Administrators can access it via computer or mobile device to view system flowcharts and operational data in real time. During system operation, when the heat exchange station load is at its peak, the phase change thermal storage unit releases heat to assist in heating. The data acquisition unit detects an accelerated rate of temperature decrease in the phase change material and an increase in pipeline energy consumption. After model analysis, the data processing unit outputs optimized commands to reduce heat source output by 10%, maintain valve opening at 80%, and maintain the circulating pump frequency at 50Hz. After the parameter control unit executes the commands, the feedback detection module detects an 18% decrease in pipeline energy consumption and a stable heating temperature within the set range. The model then iteratively updates based on this feedback data.

[0033] Example 5 In this embodiment, a fatty acid-based composite phase change material is selected as the phase change material, with a phase change temperature of 45°C. A graphene thermally conductive layer is embedded inside the heat storage container as a thermal conductivity enhancement structure, increasing the thermal conductivity to 30 W / (m·K). The data acquisition unit's acquisition frequency is set to 5Hz, and the communication module uses 5G communication to ensure real-time data transmission. The machine learning algorithm module of the data processing unit adopts the BP neural network algorithm, with three hidden layers in the model and the ReLU function as the activation function. After training with 1000 sets of historical data, the model's prediction accuracy reaches 92%. The alarm module of the remote monitoring center sets the energy consumption exceedance threshold to 110% of the design energy consumption. When the pipeline energy consumption exceeds this threshold, an audible and visual alarm is immediately triggered, and a text message notification is sent to the management personnel. During system operation, when the phase change material experiences an abnormal temperature rise exceeding the phase change temperature by 10°C, the data acquisition unit transmits the abnormal data to the data processing unit. The model determines that the heat storage is abnormal and outputs control commands to shut down the heat source input and increase the circulation pump frequency to 60Hz. After the parameter control unit executes the commands, the phase change material temperature gradually returns to normal, and the energy consumption returns to a reasonable range. The entire control process takes ≤3 minutes, demonstrating the system's rapid response capability.

[0034] Example 6 This embodiment also provides a computing device. The computing device includes a bus, a processor, a memory, and a communication interface. The processor, memory, and communication interface communicate with each other via the bus. The computing device can be a server or a terminal device. It should be understood that this application does not limit the number of processors and memory in the computing device.

[0035] A bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, a bus can include a path for transmitting information between various components of a computing device (e.g., memory, processor, communication interfaces).

[0036] The processor may include any one or more of the following: central processing unit (CPU), graphics processing unit (GPU), tensor processing unit (TPU), application specific integrated circuit (ASIC), field-programmable gate array (FPGA), microprocessor (MP), or digital signal processor (DSP).

[0037] Memory can include volatile memory, such as random access memory (RAM). Processors can also include non-volatile memory. volatile memory, such as read-only memory (ROM). ROM (memory only), flash memory, hard disk drive (HDD), or solid state drive (SSD).

[0038] The memory stores executable program code, which the processor executes to implement the functions of the aforementioned units, thereby achieving, for example, the method described in Embodiment 1. That is, the memory may store instructions for the methods and functions relating to the computing device in any of the above embodiments.

[0039] The communication interface uses transceiver modules such as, but not limited to, network interface cards and transceivers to enable communication between computing devices and other devices or communication networks.

[0040] Example 7 This embodiment also provides a computing device cluster. The computing device cluster includes at least one computing device. The computing device can be a server, such as a central server, an edge server, or a local server in a local data center. In some embodiments, the computing device can also be a terminal device such as a desktop computer, a laptop computer, or a smartphone.

[0041] The computing device cluster includes at least one computing device. The memory of one or more computing devices in the computing device cluster may store the same instructions for performing the methods and functions related to the computing devices in any of the above embodiments.

[0042] In some possible implementations, the memory of one or more computing devices in the computing device cluster may also store partial instructions for performing the methods and functions of the computing devices involved in any of the above embodiments. In other words, a combination of one or more computing devices can jointly execute the instructions for performing the methods and functions of the computing devices.

[0043] It should be noted that the memory in different computing devices within a computing device cluster can store different instructions, which are used to execute parts of the device's functions.

[0044] In some possible implementations, one or more computing devices in a computing device cluster can be connected via a network. This network can be a wide area network (WAN) or a local area network (LAN), etc. Two computing devices are connected to each other via the network. Specifically, they connect to the network through communication interfaces in each computing device.

[0045] Embodiments of this disclosure also provide a computer program product containing instructions that, when run on a computer, cause the computer to perform the methods and functions related to a computing device in any of the above embodiments.

[0046] Example 8 This embodiment also provides a computer-readable storage medium storing computer instructions that, when executed by a processor, cause the processor to perform the methods and functions of the computing device involved in any of the above embodiments.

[0047] Generally, the various embodiments of this disclosure can be implemented in hardware or dedicated circuitry, software, logic, or any combination thereof. Some aspects can be implemented in hardware, while others can be implemented in firmware or software, which can be executed by a controller, microprocessor, or other computing device. Although various aspects of the embodiments of this disclosure are shown and described as block diagrams, flowcharts, or represented using some other illustration, it should be understood that the blocks, apparatuses, systems, techniques, or methods described herein can be implemented as, as non-limiting examples, in hardware, software, firmware, dedicated circuitry or logic, general-purpose hardware or controllers or other computing devices, or some combination thereof.

[0048] Example 9 This embodiment provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer-executable instructions, such as instructions included in program modules, which execute in a device on a target real or virtual processor to perform the processes / methods as described above with reference to the accompanying drawings. Typically, program modules include routines, programs, libraries, objects, classes, components, data structures, etc., that perform specific tasks or implement specific abstract data types. In various embodiments, the functionality of program modules can be combined or divided among program modules as needed. The machine-executable instructions for the program modules can execute within a local or distributed device. In a distributed device, the program modules can reside in both local and remote storage media.

[0049] Computer program code used to implement the methods of this disclosure may be written in one or more programming languages. This computer program code may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus, such that when executed by the computer or other programmable data processing apparatus, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be performed. The program code may be executed entirely on a computer, partially on a computer, as a stand-alone software package, partially on a computer and partially on a remote computer, or entirely on a remote computer or server.

[0050] In the context of this disclosure, computer program code or related data may be carried on any suitable carrier to enable a device, apparatus, or processor to perform the various processes and operations described above. Examples of carriers include signals, computer-readable media, and so on. Examples of signals may include electrical, optical, radio, sound, or other forms of propagation signals, such as carrier waves, infrared signals, etc.

[0051] Computer-readable media can be any tangible medium that contains or stores programs for or relating to an instruction execution system, apparatus, or device, or a data storage device such as a data center containing one or more available media. Computer-readable media can be computer-readable signal media or computer-readable storage media. Computer-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. More detailed examples of computer-readable storage media include electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0052] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for optimizing and monitoring the energy consumption of a heating network incorporating phase change materials, characterized in that, Includes the following steps: S1. Real-time acquisition of temperature information of phase change materials and operation information of heating pipe network; S2. Based on the collected temperature information of the phase change material and the operation information of the heating network, combined with the pre-built energy consumption optimization model, output the heating parameter optimization command; S3. Based on the obtained heating parameter optimization instructions, adjust the heat source output, valve opening and circulation pump frequency of the heating network.

2. The method for optimizing and monitoring the energy consumption of a heating network incorporating phase change materials according to claim 1, characterized in that, The temperature information of the phase change material includes the real-time temperature and heat storage / release rate of the phase change material, as well as the surface temperature of the phase change material container; the operation information of the heating network includes network power data, heat data, network medium flow rate and pressure.

3. The method for optimizing and monitoring the energy consumption of a heating network incorporating phase change materials according to claim 1, characterized in that, The method for constructing the pre-built energy consumption optimization model is as follows: A pre-built energy consumption optimization model is obtained by using the random forest algorithm or the BP neural network algorithm.

4. The method for optimizing and monitoring the energy consumption of a heating network incorporating phase change materials according to claim 1, characterized in that, The phase change material is paraffin, fatty acid, or a composite phase change material.

5. The method for optimizing and monitoring the energy consumption of a heating network incorporating phase change materials according to claim 1, characterized in that, The phase change material has a thermally enhanced structure embedded inside.

6. A heating network energy consumption optimization and monitoring system incorporating phase change materials, characterized in that, include: The operation information acquisition unit is used to collect temperature information of phase change materials and operation information of heating pipe network in real time; The heating parameter output unit is used to output heating parameter optimization instructions based on the collected temperature information of the phase change material and the operation information of the heating pipeline network, combined with the pre-built energy consumption optimization model. The instruction execution unit is used to optimize instructions based on the obtained heating parameters, and adjust the heat source output, valve opening and circulation pump frequency of the heating network.

7. An electronic device, characterized in that, It includes a processor and a memory, the memory storing computer instructions that, when executed by the processor, cause the electronic device to perform the method of any one of claims 1 to 5.

8. A computing device cluster, characterized in that, It includes at least one computing device, each computing device including a processor and memory; The processor of the at least one computing device is configured to execute instructions stored in the memory of the at least one computing device to cause the cluster of computing devices to perform the method according to any one of claims 1 to 5.

9. A computer program product, characterized in that, The computer program product includes computer-executable instructions that, when executed, implement the method of any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions that, when executed by a processor, implement the method of any one of claims 1 to 5.

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