Multi-energy station configuration method, device, equipment, medium and product based on semiconductor park load clustering

Abstract

The invention discloses a multi-energy station configuration method and device based on semiconductor park load clustering, equipment, a medium and a product. The method comprises the following steps: acquiring energy consumption data in a park; performing clustering analysis on the energy consumption data by adopting a K-means clustering algorithm to obtain multi-element energy demands and energy consumption characteristics of each user; obtaining a typical energy consumption curve of the single user through clustering of the energy consumption data of the single user; performing multi-user energy consumption data clustering based on the single-user typical energy consumption curve to obtain a multi-user typical energy consumption curve and determine energy demands and energy consumption characteristics of different types of users; and determining energy station site selection and corresponding equipment according to the clustering result of the energy consumption data in combination with the actual geographic position of each user park, and solving an energy station configuration result by taking the minimum sum of daily chemical investment cost and daily operation cost as a target. By clustering the park load, reliable energy station configuration is carried out according to different types of energy demands and energy consumption characteristics, the energy supply structure is optimized, and the energy utilization efficiency is improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of energy management technology, and in particular to a method, device, equipment, medium and product for configuring multiple energy stations based on semiconductor park load clustering. Background Art

[0002] The semiconductor manufacturing process involves a large number of precision equipment, and production requires a high degree of stability and continuity. The production equipment has high operating power, and key equipment needs to run 24 hours a day. In addition, the energy consumption of different process stages varies greatly. In addition to electricity, there are also multiple energy requirements such as natural gas, heating and cooling. In addition to high energy consumption, high and low temperature steam and return water will be generated. Associated resources, such as water, are used for power generation and heating through energy conversion technology. However, such resources have not yet been fully utilized.

[0003] Energy stations can be used as centralized energy supply centers to provide energy for the park. Through multi-source energy supply (such as electricity, natural gas, steam, etc.), the comprehensive utilization of multiple energy sources can be achieved. For example, technologies such as cogeneration of heat and power and trigeneration of cooling, heat and power can be used to use the waste heat generated during the power generation process for heating or cooling, thereby improving the comprehensive utilization efficiency of energy. Renewable energy sources such as solar energy and wind energy can also be integrated with traditional energy sources to optimize the energy structure and reduce dependence on a single energy source.

[0004] When selecting and configuring equipment for an energy station, different equipment or production links need to be considered. For semiconductor park loads, there may be equipment or production links with similar energy consumption characteristics. How to reliably select and configure equipment for the energy station based on the energy needs of various equipment or production links is an urgent problem to be solved. Summary of the invention

[0005] The present application provides a method, device, equipment, medium and product for configuring multiple energy stations based on semiconductor park load clustering to achieve reliable energy station location selection and equipment selection and configuration.

[0006] In a first aspect, an embodiment of the present application provides a multi-energy station configuration method based on semiconductor park load clustering, including:

[0007] S1. The load of the semiconductor park consists of four production links, namely silicon raw material preparation, wafer processing, chip manufacturing and finished product testing, as well as the living area of ​​the park. The energy consumption data of each user in the semiconductor park is collected through the sensors of the installed energy production and consumption equipment, including electric power data, load power demand data and gas data;

[0008] S2. Use K-means clustering algorithm to cluster the collected energy consumption data to obtain the multi-energy demand and energy consumption characteristics of each user, including:

[0009] By clustering the energy consumption data of a single user, a typical energy consumption curve of a single user is obtained;

[0010] Based on the typical energy consumption curve of a single user, cluster the energy consumption data of multiple users to obtain the typical energy consumption curve of multiple users, and determine the energy demand and energy consumption characteristics of different types of users;

[0011] S3. According to the clustering results of energy consumption data and the actual geographical location of each user's park, the location of the energy station and the corresponding equipment are determined, and the energy station configuration results are solved with the goal of minimizing the sum of daily investment cost and daily operating cost.

[0012] In the second aspect, the embodiment of the present application further provides a multi-energy station configuration device based on semiconductor park load clustering, wherein the semiconductor park load consists of four production links of silicon raw material preparation, wafer processing, chip manufacturing and finished product testing, as well as a park living area; the device comprises:

[0013] The collection module is used to collect energy consumption data of each user in the semiconductor park through sensors of installed energy-producing and energy-consuming equipment, including electric power data, load power demand data and gas data;

[0014] The clustering module is used to perform cluster analysis on the collected energy consumption data using the K-means clustering algorithm to obtain the multi-energy demand and energy consumption characteristics of each user; the clustering module specifically includes:

[0015] A single-user clustering unit, used for obtaining a typical energy consumption curve of a single user by clustering the energy consumption data of the single user;

[0016] A multi-user clustering unit is used to cluster the energy consumption data of multiple users based on the typical energy consumption curve of a single user, obtain the typical energy consumption curve of multiple users, and determine the energy demand and energy consumption characteristics of different types of users;

[0017] The configuration module is used to determine the location of the energy station and the corresponding equipment based on the clustering results of the energy consumption data and the actual geographical location of each user's park, and to solve the energy station configuration results with the goal of minimizing the sum of daily investment cost and daily operating cost.

[0018] In a third aspect, an embodiment of the present application provides an electronic device, including:

[0019] one or more processors;

[0020] A storage device for storing one or more programs;

[0021] When the one or more programs are executed by the one or more processors, the one or more processors implement the multi-energy station configuration method based on semiconductor park load clustering as described in the first aspect.

[0022] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the multi-energy station configuration method based on semiconductor park load clustering as described in the first aspect.

[0023] In a fifth aspect, an embodiment of the present application further provides a computer program product, comprising a computer program and / or instructions, which, when executed by a processor, implements a multi-energy station configuration method based on semiconductor park load clustering as described in any of the above embodiments.

[0024] The embodiments of the present application provide a method, device, equipment, medium and product for configuring a multi-energy station based on semiconductor park load clustering. The method for configuring a multi-energy station based on semiconductor park load clustering includes: the semiconductor park load consists of four production links, namely silicon raw material preparation, wafer processing, chip manufacturing and finished product testing, as well as a park living area. The energy consumption data of each user in the semiconductor park is collected through sensors of installed energy production and consumption equipment, including electric power data, load power demand data and gas data; the K-means clustering algorithm is used to perform cluster analysis on the collected energy consumption data to obtain the multi-energy demand and energy consumption characteristics of each user, specifically including: obtaining a typical energy consumption curve of a single user by clustering the energy consumption data of a single user; clustering the energy consumption data of multiple users based on the typical energy consumption curve of a single user to obtain a typical energy consumption curve of multiple users, and determining the energy demand and energy consumption characteristics of different types of users; determining the site selection and corresponding equipment of the energy station based on the clustering results of the energy consumption data and the actual geographical location of the park of each user, and solving the energy station configuration results with the goal of minimizing the sum of daily investment cost and daily operating cost. The above technical solution, through cluster analysis of the park loads, can reliably select the location of the energy station and configure the equipment according to the energy needs and energy consumption characteristics of different types of users, and configure it with the goal of minimizing the sum of daily investment cost and daily operating cost. It can optimize the energy supply structure and improve energy utilization efficiency, and provide energy to the park continuously, stably and efficiently. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and the originals and elements are not necessarily drawn to scale.

[0026] Figure 1 A flowchart of a multi-energy station configuration method based on semiconductor park load clustering provided in an embodiment of the present application;

[0027] Figure 2 A schematic diagram of an energy station site selection provided in an embodiment of the present application;

[0028] Figure 3 A schematic diagram of the structure of a multi-energy station configuration device based on semiconductor park load clustering provided in an embodiment of the present application;

[0029] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0030] The present application will be further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the present application, rather than to limit the present application. It should also be noted that, for ease of description, only the parts related to the present application, rather than all structures, are shown in the accompanying drawings.

[0031] It should be mentioned before discussing the exemplary embodiments in more detail that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the steps as sequential processes, many of the steps therein can be implemented in parallel, concurrently or simultaneously. In addition, the order of the steps can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the accompanying drawings. The process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0032] It should be noted that the concepts of "first", "second", etc. mentioned in the embodiments of the present application are only used to distinguish different devices, modules, units or other objects, and are not used to limit the order or interdependence of the functions performed by these devices, modules, units or other objects.

[0033] In addition, the embodiments in the present application and the features in the embodiments may be combined with each other if there is no conflict.

[0034] The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws and regulations.

[0035] It should be noted that in the embodiments of the present application, certain software, components, models and other existing solutions in the industry may be mentioned, and they should be regarded as exemplary. Their purpose is only to illustrate the feasibility of implementing the technical solution of the present application, but it does not mean that the applicant has or will necessarily use the relevant content of the solution.

[0036] Figure 1A flowchart of a multi-energy station configuration method based on semiconductor park load clustering is provided for an embodiment of the present application. This embodiment can be applicable to the case of energy station configuration. Specifically, the multi-energy station configuration method based on semiconductor park load clustering can be executed by a multi-energy station configuration device based on semiconductor park load clustering, and the multi-energy station configuration device based on semiconductor park load clustering can be implemented by software and / or hardware and integrated in an electronic device. Electronic devices include but are not limited to computers, smart phones, servers and other devices.

[0037] like Figure 1 As shown, the method specifically comprises the following steps:

[0038] S1. Collect energy consumption data of each user in the semiconductor park through sensors of installed energy-producing and energy-consuming equipment, including electric power data, load power demand data and gas data.

[0039] Among them, the semiconductor park load consists of four production links: silicon raw material preparation, wafer processing, chip manufacturing and finished product testing, as well as the park living area. Energy consumption data includes electric power data, load heat (cold) power demand data and gas data. Electric power data mainly refers to the company's time-sharing electric power. For example, the company's electric power at 8 o'clock is 1MW. The load heat (cold) power demand data refers to the heat energy MJ required in a certain hour of the company's production process converted to electric energy kWh. For example, the company requires 100MJ of heat energy during the 7-8 period, which is converted to 97kWh of electricity. The heat power demand at 8 o'clock is (97 / 1)kW. Gas data mainly refers to the amount of purchased natural gas consumed by the company at different times. For example, the flow rate of purchased natural gas consumed by the company at 8 o'clock is 500m 3 / h.

[0040] S2. Use K-means clustering algorithm to perform cluster analysis on the collected energy consumption data to obtain the multi-energy demand and energy consumption characteristics of each user.

[0041] Specifically, by clustering the energy consumption data of a single user, a typical energy consumption curve of a single user is obtained, and the typical energy consumption curve of a single user can be used to describe the energy demand and energy consumption characteristics of each user; based on the typical energy consumption curve of a single user, cluster the energy consumption data of multiple users to obtain the typical energy consumption curve of multiple users, and on this basis determine the energy demand and energy consumption characteristics of different types of users. Among them, the energy consumption curve is a curve that represents the relationship between energy such as electricity, heat, cold, and gas and time. For example, the horizontal axis of the energy consumption curve can be electric power, load heat (cold) power, and natural gas volume, and the vertical axis can be 24 times a day. Energy demand mainly refers to the enterprise's demand for electricity, heat (cold), gas and other energy. For example, an enterprise consumes 10MWh of electricity, 2MWh of heat (cold), and 2000m of natural gas a day. 3Energy consumption characteristics mainly refer to the values ​​of different energy sources required by the enterprise at different times of the day. For example, the enterprise needs 2MW of electric power, 0.5MW of load heating (cooling) power, and 30m of natural gas flow at one moment. 3 / h, 3MW of electric power is required at 2 moments, 0.8MW of heat (cold) load power, and 100m of natural gas flow rate 3 / h.

[0042] S3. According to the clustering results of energy consumption data and the actual geographical location of each user's park, the location of the energy station and the corresponding equipment are determined, and the energy station configuration results are solved with the goal of minimizing the sum of daily investment cost and daily operating cost.

[0043] In this embodiment, based on the diverse energy demands and energy usage characteristics of various types of users obtained through clustering, combined with the actual geographical location of each user in the park, energy stations are sited for each type and corresponding equipment is configured, such as electrical equipment, thermal equipment and / or multi-energy cogeneration and energy conversion equipment; in addition, the energy station configuration results are solved with the aim of minimizing the sum of daily investment cost and daily operating cost by reasonably determining the capacity configuration of energy station equipment corresponding to each category.

[0044] The embodiment of the present application provides a multi-energy station configuration method based on semiconductor park load clustering. Through the load clustering analysis of the park, according to the energy needs and energy usage characteristics of different types of users, the energy station location and equipment selection configuration are reliably performed for each type, and the configuration is performed with the goal of minimizing the sum of daily investment cost and daily operating cost. It can optimize the energy supply structure and improve energy utilization efficiency, and continuously, stably and efficiently provide energy to the park.

[0045] In one embodiment, a typical energy consumption curve of a single user is obtained by clustering the energy consumption data of a single user, including:

[0046] S21, input n single-user energy consumption data samples, denoted as X = x1, x2, ..., x n

[0047] According to the clustering requirements, select the category centers of m clusters, denoted as κ1,κ2,...,κ m ;

[0048] S22, mark energy consumption data sample x i is the distance from the class center κ j Recent Category L i , recorded as Among them, the same category L j Sample x i Composition set A j ;

[0049] S23, update each category center to the mean of all samples belonging to that category, denoted as

[0050]

[0051] in, For set A j The number of samples in set A j are all samples belonging to category j;

[0052] Repeat S22 and S23 until the change rate of the category center is less than the threshold η, which is recorded as Then the algorithm converges and the typical energy consumption curve of a single user is obtained;

[0053] Furthermore, based on the typical energy consumption curve of a single user, clustering of multi-user energy consumption data is performed to obtain a typical energy consumption curve of multiple users, including:

[0054] Based on the typical energy consumption curve of a single user, steps S21 to S23 are repeated to obtain typical energy consumption curves of multiple users.

[0055] Figure 2 A schematic diagram of an energy station site selection provided in an embodiment of the present application. Figure 2 As shown, in one embodiment, according to the clustering result of the energy consumption data and in combination with the actual geographical location of each user's park, the energy station site is determined, including:

[0056] S31. Draw the semiconductor park as a two-dimensional plane coordinate map, represent each user in the form of coordinates, and for each category, use the point group median center calculation method to mark the geographical location points of all users in the category after clustering, and select the location of the point with the smallest distance to all user points as the proposed point for energy station site selection (i.e., the preliminary address), recorded as: Where D is the number of users in this category, point (a0, b0) is the proposed location of the energy station, and point (a d ,b d ) is the geographic location point of the dth user;

[0057] S32. If the selected proposed point is a non-constructible point (i.e., the selected proposed point is not within the constructible area), then the constructible point closest to the proposed point is selected as the new proposed point (i.e., the addressing of the energy station, which can also be called the site selection of the energy station). If the selected proposed point is a constructible point (i.e., the selected proposed point is within the constructible area), then the proposed point can be used as the final addressing of the energy station. Among them, the constructible area can be flexibly set according to actual needs, and this embodiment does not limit this. On this basis, the feasibility of the site selection of the energy station can be guaranteed, and considering the distribution of users of various categories, the energy supply structure can be optimized and energy consumption can be reduced.

[0058] In one embodiment, the corresponding device includes at least one of the following:

[0059] Electrical equipment, including photovoltaic equipment, wind power equipment, gas turbine equipment and / or electrical energy storage equipment;

[0060] Thermal equipment, including gas boilers and / or thermal energy storage equipment;

[0061] Cogeneration and energy conversion equipment, including combined cooling, heating and power (CCHP) units, low-temperature waste heat generators, electric boilers, air conditioning units and / or absorption chillers;

[0062] According to the clustering results of energy consumption data, the corresponding equipment is determined, including:

[0063] S33. Select corresponding equipment from the list of optional equipment based on the energy demand and energy usage characteristics corresponding to the clustering results.

[0064] For example, for each category, the required energy type and amount can be determined according to energy demand and energy usage characteristics, such as electrical energy, thermal energy and / or multiple energy sources, and the corresponding type of equipment is selected for energy supply. The list of optional equipment can be flexibly set according to actual needs, and this embodiment does not specifically limit this.

[0065] In one embodiment, determining corresponding equipment according to the clustering result of energy consumption data includes:

[0066] If the energy demand and energy use characteristics analysis corresponding to the clustering result is only electric energy, the energy station of the corresponding category only selects configuration from electric equipment;

[0067] If the energy demand and energy use characteristics corresponding to the clustering results include multiple energy sources, the corresponding category of energy station selects configuration from electrical equipment, thermal equipment, multi-energy cogeneration and energy conversion equipment.

[0068] In one embodiment, the energy station configuration result is solved with the goal of minimizing the sum of daily investment cost and daily operation cost, including: obtaining the capacity configuration result of each device in each type of energy station with the goal of minimizing the sum of daily investment cost and daily operation cost; the objective function is expressed as: Among them, μ i is the daily average unit investment cost of equipment i, R i is the investment capacity of equipment i, C j is the unit purchase cost of energy j, W jis the daily consumption of energy j, and N is the total number of devices. For example, a gradient descent method or a heuristic optimization algorithm can be used to solve the problem, which is not specifically limited in this embodiment. On this basis, the daily chemical investment cost and daily operating cost can be reduced, and the energy utilization efficiency can be improved. Figure 3 A schematic diagram of the structure of a multi-energy station configuration device based on semiconductor park load clustering provided in an embodiment of the present application. In this embodiment, the semiconductor park load consists of production links such as silicon raw material preparation, wafer processing, chip manufacturing and finished product testing, as well as the park living area. Figure 3 As shown, the multi-energy station configuration device based on semiconductor park load clustering provided in this embodiment includes:

[0069] The collection module 210 is used to collect energy consumption data of each user in the semiconductor park through sensors of installed energy production and consumption equipment, including electric power data, load power demand data and gas data;

[0070] The clustering module 220 is used to perform cluster analysis on the collected energy consumption data using the K-means clustering algorithm to obtain the multi-energy demand and energy consumption characteristics of each user; the clustering module specifically includes:

[0071] A single-user clustering unit, used for obtaining a typical energy consumption curve of a single user by clustering the energy consumption data of the single user;

[0072] A multi-user clustering unit is used to cluster the energy consumption data of multiple users based on the typical energy consumption curve of a single user, obtain the typical energy consumption curve of multiple users, and determine the energy demand and energy consumption characteristics of different types of users;

[0073] Configuration module 230 is used to determine the location of the energy station and the corresponding equipment based on the clustering results of the energy consumption data and the actual geographical location of each user's park, and to solve the energy station configuration results with the goal of minimizing the sum of daily investment cost and daily operating cost.

[0074] The device performs cluster analysis on the park loads and reliably selects the location of the energy station and the equipment configuration according to the energy demands and energy usage characteristics of different types of users.

[0075] Optionally, the single-user clustering unit is specifically used for:

[0076] S21, input n single-user energy consumption data samples, denoted as X = x1, x2, ..., x n

[0077] According to the clustering requirements, select the category centers of m clusters, denoted as κ1,κ2,...,κ m ;

[0078] S22, mark energy consumption data sample x iis the distance from the class center κ j Recent Category L i , recorded as

[0079]

[0080] Belong to the same category L j Sample x i Composition set A j ;

[0081] S23, update each category center to the mean of all samples belonging to that category, denoted as

[0082]

[0083] in, For set A j The number of samples in set A j are all samples belonging to category j;

[0084] Repeat S22 and S23 until the rate of change of the category center is less than the threshold η, denoted as

[0085]

[0086] The algorithm converges and the typical energy consumption curve of a single user is obtained.

[0087] Optional, multi-user clustering unit, specifically used for:

[0088] Based on the typical energy consumption curve of a single user, steps S21 to S23 are repeated to obtain typical energy consumption curves of multiple users.

[0089] Optionally, the configuration module 230 includes: a site selection unit, configured to:

[0090] The semiconductor park is drawn as a two-dimensional plane coordinate map, each user is represented in the form of coordinates, the geographical location points of all users after clustering are marked by the calculation method of the median center of the point group, and the location of the point with the smallest distance to all user points is selected as the proposed point for the energy station site selection, which is recorded as

[0091]

[0092] Among them, D is the number of clustered users, point (a0, b0) is the proposed location of the energy station, and point (a d ,b d ) is the geographic location point of the dth user;

[0093] If the selected proposed point is not a constructible point, the constructible point closest to the proposed point will be selected as the new proposed point.

[0094] Optionally, the corresponding device includes at least one of the following:

[0095] Electrical equipment, including photovoltaic equipment, wind power equipment, gas turbine equipment and / or electrical energy storage equipment;

[0096] Thermal equipment, including gas boilers and / or thermal energy storage equipment;

[0097] Cogeneration and energy conversion equipment, including combined cooling, heating and power (CCHP) units, low-temperature waste heat generators, electric boilers, air conditioning units and / or absorption chillers;

[0098] The configuration module 230 includes: a device selection unit, configured to:

[0099] According to the energy demand and energy usage characteristics corresponding to the clustering results, the corresponding equipment is selected from the list of optional equipment.

[0100] Optionally, the configuration module 230 is specifically used for:

[0101] If the energy demand and energy use characteristics analysis corresponding to the clustering result is only electric energy, the energy station of the corresponding category only selects configuration from electric equipment;

[0102] If the energy demand and energy use characteristics corresponding to the clustering results include multiple energy sources, the corresponding category of energy station selects configuration from electrical equipment, thermal equipment, multi-energy cogeneration and energy conversion equipment.

[0103] Optionally, the energy station configuration result is solved with the goal of minimizing the sum of daily investment cost and daily operation cost, including:

[0104] With the goal of minimizing the sum of daily investment cost and daily operating cost, the capacity configuration results of each equipment in various energy stations are obtained;

[0105] The objective function can be expressed as:

[0106] Among them, μ i is the daily average unit investment cost of equipment i, R i is the investment capacity of equipment i, C j is the unit purchase cost of energy j, W j is the energy consumption in j days, and N is the total number of equipment.

[0107] The multi-energy station configuration device based on semiconductor park load clustering provided in an embodiment of the present application can be used to execute the multi-energy station configuration method based on semiconductor park load clustering provided in any of the above embodiments, and has corresponding functions and beneficial effects.

[0108] Figure 4A schematic diagram of an electronic device 10 that can be used to implement an embodiment of the present application is shown. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device 10 can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, user equipment, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or required herein.

[0109] like Figure 4 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0110] Multiple components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, wireless networks.

[0111] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above.

[0112] In some embodiments, the method of the above embodiment may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform any of the above embodiment methods in any other appropriate manner (e.g., by means of firmware).

[0113] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0114] The computer programs for implementing the methods of the present application may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer programs are executed by the processor, the functions / operations specified in the flow charts and / or block diagrams are implemented. The computer programs may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0115] In the context of the present application, a computer readable storage medium may be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, device or equipment. A computer readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer readable storage medium may be a machine readable signal medium. A more specific example of a machine readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0116] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device 10 having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device 10. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0117] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0118] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0119] An embodiment of the present application also provides a computer program product, including a computer program and / or instructions, which, when executed by a processor, implements a multi-energy station configuration method based on semiconductor park load clustering as described in any of the above embodiments.

[0120] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps recorded in this application can be executed in parallel, sequentially or in different orders, as long as the expected results of the technical solution of this application can be achieved, and this document is not limited here.

[0121] The above specific implementations do not constitute a limitation on the protection scope of this application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this application should be included in the protection scope of this application.

Claims

1. A method for configuring an energy station based on semiconductor park load clustering, characterized in that: The following steps are involved: S1. The load of the semiconductor park consists of four production links, namely silicon raw material preparation, wafer processing, chip manufacturing and finished product testing, as well as the living area of ​​the park. The energy consumption data of each user in the semiconductor park is collected through the sensors of the installed energy production and consumption equipment, including electric power data, load power demand data and gas data; S2. Use K-means clustering algorithm to cluster the collected energy consumption data to obtain the multi-energy demand and energy consumption characteristics of each user, including: By clustering the energy consumption data of a single user, a typical energy consumption curve of a single user is obtained; Based on the typical energy consumption curve of a single user, cluster the energy consumption data of multiple users, obtain the typical energy consumption curve of multiple users, and determine the energy demand and energy consumption characteristics of different types of users; S3. According to the clustering results of energy consumption data and the actual geographical location of each user's park, the location of the energy station and the corresponding equipment are determined, and the energy station configuration results are solved with the goal of minimizing the sum of daily investment cost and daily operating cost.

2. The energy station configuration method based on semiconductor park load clustering according to claim 1 is characterized in that: By clustering the energy consumption data of a single user, a typical energy consumption curve of a single user is obtained, including: S21, input n single-user energy consumption data samples, denoted as X = x1, x2, ..., x n According to the clustering requirements, select the category centers of m clusters, denoted as κ1,κ2,...,κ m ; S22, mark energy consumption data sample x i is the distance from the class center κ j Recent Category L i , recorded as Belong to the same category L j Sample x i Composition set A j ; S23, update each category center to the mean of all samples belonging to that category, denoted as in, For set A j The number of samples in set A j are all samples belonging to category j; Repeat S22 and S23 until the change rate of the category center is less than the threshold η, which is recorded as Then the algorithm converges and the typical energy consumption curve of a single user is obtained; Based on the typical energy consumption curve of a single user, cluster the energy consumption data of multiple users to obtain the typical energy consumption curve of multiple users, including: Based on the typical energy consumption curve of a single user, steps S21 to S23 are repeated to obtain typical energy consumption curves of multiple users.

3. The energy station configuration method based on semiconductor park load clustering according to claim 1 is characterized in that: Based on the clustering results of energy consumption data and the actual geographical location of each user's park, the energy station site is determined, including: The semiconductor park is drawn as a two-dimensional plane coordinate map, each user is represented in the form of coordinates, the geographical location points of all users after clustering are marked by the calculation method of the median center of the point group, and the location of the point with the smallest distance to all user points is selected as the proposed point for the energy station site selection, which is recorded as Among them, D is the number of clustered users, point (a0, b0) is the proposed location of the energy station, and point (a d ,b d ) is the geographic location point of the dth user; If the selected proposed point is not a constructible point, the constructible point closest to the proposed point will be selected as the new proposed point.

4. The energy station configuration method based on semiconductor park load clustering according to claim 1 is characterized in that: The corresponding device includes at least one of the following: Electrical equipment, including photovoltaic equipment, wind power equipment, gas turbine equipment and / or electrical energy storage equipment; Thermal equipment, including gas boilers and / or thermal energy storage equipment; Cogeneration and energy conversion equipment, including CCHP combined cooling, heating and power units, low-temperature waste heat generators, electric boilers, air conditioning units and / or absorption chillers; According to the clustering results of energy consumption data, the corresponding equipment is determined, including: According to the energy demand and energy usage characteristics corresponding to the clustering results, select the corresponding equipment from the list of optional equipment.

5. The energy station configuration method based on semiconductor park load clustering according to claim 1 is characterized in that: According to the clustering results of energy consumption data, the corresponding equipment is determined, including: If the energy demand and energy use characteristics analysis corresponding to the clustering result is only electric energy, the energy station of the corresponding category only selects configuration from electric equipment; If the energy demand and energy use characteristics corresponding to the clustering results include multiple energy sources, the corresponding category of energy station selects configuration from electrical equipment, thermal equipment, multi-energy cogeneration and energy conversion equipment.

6. The energy station configuration method based on semiconductor park load clustering according to claim 1 is characterized in that: With the goal of minimizing the sum of daily investment cost and daily operating cost, the energy station configuration results are solved, including: With the goal of minimizing the sum of daily investment cost and daily operating cost, the capacity configuration results of each equipment in various energy stations are obtained; The objective function can be expressed as: Among them, μ i is the daily average unit investment cost of equipment i, R i is the investment capacity of equipment i, C j is the unit purchase cost of energy j, W j is the energy consumption in j days, and N is the total number of equipment.

7. A multi-energy station configuration device based on semiconductor park load clustering, characterized in that: The semiconductor park load consists of four production links: silicon raw material preparation, wafer processing, chip manufacturing and finished product testing, as well as the park living area; the device includes: The collection module is used to collect energy consumption data of each user in the semiconductor park through sensors of installed energy-producing and energy-consuming equipment, including electric power data, load power demand data and gas data; The clustering module is used to perform cluster analysis on the collected energy consumption data using the K-means clustering algorithm to obtain the multi-energy demand and energy consumption characteristics of each user; the clustering module specifically includes: A single-user clustering unit, used for obtaining a typical energy consumption curve of a single user by clustering the energy consumption data of the single user; A multi-user clustering unit is used to cluster the energy consumption data of multiple users based on the typical energy consumption curve of a single user, obtain the typical energy consumption curve of multiple users, and determine the energy demand and energy consumption characteristics of different types of users; The configuration module is used to determine the location of the energy station and the corresponding equipment based on the clustering results of the energy consumption data and the actual geographical location of each user's park, and to solve the energy station configuration results with the goal of minimizing the sum of daily investment cost and daily operating cost.

8. An electronic device, characterized in that: include: at least one processor; a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the multi-energy station configuration method based on semiconductor park load clustering as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, it implements the multi-energy station configuration method based on semiconductor park load clustering as described in any one of claims 1-6.

10. A computer program product comprising a computer program and / or instructions, characterized in that: When the computer program and / or instructions are executed by the processor, the multi-energy station configuration method based on semiconductor park load clustering as described in any one of claims 1-6 is implemented.

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