Intelligent calculation method and system for material statistics and cost prediction of photovoltaic system

Abstract

The invention relates to an intelligent calculation method and system for material statistics and cost prediction of a photovoltaic system, and belongs to the field of photovoltaic systems. The method comprises the following steps: acquiring statistical data of a photovoltaic system material, wherein the statistical data of the photovoltaic system material is cost statistical data of an assembly material required for supporting normal operation of the photovoltaic system; obtaining photovoltaic system construction statistical data, wherein the photovoltaic system construction statistical data are all construction cost statistical data, except for component materials, required by photovoltaic system construction; the predicted power of the photovoltaic system is obtained through an improved secondary decomposition algorithm; according to the photovoltaic system material statistical data, the photovoltaic system construction statistical data and the photovoltaic system prediction power, calculating the photovoltaic system prediction cost and constructing a photovoltaic system cost prediction model, wherein the photovoltaic system prediction cost is the sum of the photovoltaic system material statistical data, the photovoltaic system construction statistical data and other costs. According to the invention, the cost of the photovoltaic system is scientifically predicted, regulated and controlled through the photovoltaic prediction power.

Description

Technical Field

[0001] The present invention belongs to the technical field of photovoltaic systems, and in particular relates to an intelligent calculation method and system for material statistics and cost prediction of photovoltaic systems. Background Art

[0002] Photovoltaic system is a power generation system that uses solar cells to directly convert solar energy into electrical energy. Its main components include solar cells, batteries, controllers and inverters. These components work together to ensure the efficient operation of the system and the stable output of electrical energy. By making detailed statistics on the various materials required for the photovoltaic system, such as silicon wafers, battery cells, packaging materials, etc., the total cost of the project can be accurately predicted. This is of great significance for the financial planning and investment decision-making of the project. At present, the statistical and cost prediction methods of photovoltaic system materials are relatively simple, and the impact of local natural conditions on the working efficiency of photovoltaic systems is rarely considered during construction. Summary of the invention

[0003] In order to solve the above problems existing in the prior art, the present invention provides an intelligent calculation method and system for material statistics and cost prediction of a photovoltaic system.

[0004] In order to achieve the above object, the present invention adopts the following technical solution: An intelligent calculation method for photovoltaic system material statistics and cost prediction, characterized by comprising the following steps: Obtaining statistical data on materials of a photovoltaic system, wherein the statistical data on materials of a photovoltaic system is statistical data on the cost of component materials required to support normal operation of the photovoltaic system; Obtaining photovoltaic system construction statistical data, wherein the photovoltaic system construction statistical data is all construction cost statistical data required for photovoltaic system construction except for component materials; The predicted power of photovoltaic system is obtained by improving the quadratic decomposition algorithm; The predicted cost of the photovoltaic system is calculated through the statistical data of photovoltaic system materials, the statistical data of photovoltaic system construction and the predicted power of the photovoltaic system, and a photovoltaic system cost prediction model is constructed. The predicted cost of the photovoltaic system is the sum of the statistical data of photovoltaic system materials, the statistical data of photovoltaic system construction and other costs.

[0005] A further improvement of the present invention is that obtaining the predicted power of the photovoltaic system by improving the secondary decomposition algorithm comprises: Obtain historical meteorological data of the location of the photovoltaic power plant and historical photovoltaic power data of the location, wherein the historical meteorological data of the location of the photovoltaic power plant include the average daily wind speed, maximum wind speed, average daily rainfall, maximum rainfall, average daily humidity, maximum humidity, average daily temperature, maximum temperature, average daily horizontal radiation, maximum horizontal radiation, average daily diffuse horizontal radiation, maximum diffuse horizontal radiation, average daily inclined radiation, maximum inclined radiation, average daily diffuse inclined radiation, and maximum diffuse inclined radiation of the ith day, i∈[1,365], and select photovoltaic-related meteorological factors according to the historical meteorological data of the location of the photovoltaic power plant, wherein the photovoltaic-related meteorological factors include the first photovoltaic-related meteorological factor, the second photovoltaic-related meteorological factor, the third photovoltaic-related meteorological factor, the fourth photovoltaic-related meteorological factor, the fifth photovoltaic-related meteorological factor, and the sixth photovoltaic-related meteorological factor; Clustering the dates of the previous year based on the photovoltaic-related meteorological factors to obtain date types and corresponding historical meteorological data, wherein the date types include a first date type, a second date type, and a third date type; Decomposing the corresponding historical meteorological data by using the improved secondary decomposition algorithm, wherein the improved secondary decomposition algorithm includes primary decomposition and secondary decomposition; A photovoltaic power prediction model is constructed by using a particle swarm algorithm, and L1+L2-1 vectors obtained by processing the improved quadratic decomposition algorithm are input into the photovoltaic power prediction model to obtain the predicted power of the photovoltaic system.

[0006] A further improvement of the present invention is that clustering the dates of the previous year based on the photovoltaic-related meteorological factors to obtain date types and corresponding historical meteorological data includes: A photovoltaic characteristic vector is constructed based on the photovoltaic related meteorological factors. The expression of the photovoltaic characteristic vector is: , where X i is the photovoltaic eigenvector, is the daily average value of the jth photovoltaic-related meteorological factor on the i-th day, is the maximum value of the jth photovoltaic-related meteorological factor on the i-th day, j∈[1,6]; The date type is obtained by clustering based on the CLARANS algorithm and the photovoltaic feature vector. The basic steps of the CLARANS algorithm are: (1) Set the maximum number of local optimal solutions and the maximum number of neighbors for the parameter, initialize the minimum cost, and initialize y to 1, where y is the number of local optimal solutions for the parameter; (2) Selecting k elements from the photovoltaic feature vector to form a feature set Current; (3) Let j = 1, where j is the number of neighbors; (4) randomly selecting an element from the remaining 12-k elements of the photovoltaic feature vector to replace any element in the feature set Current, obtaining a new feature set Current, and obtaining the cost difference between the feature set Current and the new feature set Current; (5) Setting a cost difference threshold. When the cost difference is less than the cost difference threshold, using the new feature set Current to replace the feature set Current, and returning to step (3). (6) When the cost difference is greater than or equal to the cost difference threshold, set j = j + 1; when j is less than or equal to the maximum number of neighbors, return to step (4); (7) When j is greater than the maximum number of neighbors, compare the cost of the current feature set Current with the minimum cost; when the cost of the current feature set Current is less than the minimum cost, set the minimum cost as the cost of the current feature set Current; (8) Let y = y + 1. When y is greater than the maximum number of local optimal solutions for the parameter, output the current feature set Current. When y is less than or equal to the maximum number of local optimal solutions for the parameter, return to step (2).

[0007] A further improvement of the present invention is that the decomposing the corresponding historical meteorological data by the improved secondary decomposition algorithm comprises: Performing the first decomposition by a variational mode decomposition algorithm to obtain L1 first decomposition vectors; The secondary decomposition is performed using a set empirical mode decomposition algorithm to obtain L2 secondary decomposition vectors.

[0008] A further improvement of the present invention is that the process of the variational mode decomposition algorithm is: Taking the corresponding historical meteorological data as an input signal and decomposing it into an intrinsic mode function; Calculating the analytical signal of the intrinsic mode function by Hilbert transform to obtain a single-sided spectrum; Modulating the spectrum of the intrinsic mode function to a corresponding baseband; Demodulating the input signal through Gaussian smoothing to obtain the bandwidth of the intrinsic mode function and obtain the corresponding constrained variational problem; The corresponding constrained variational problem is transformed into an unconstrained problem by using quadratic penalty and Lagrange multipliers, which is mathematically described as , where L is the augmented Lagrangian function, u is the set of intrinsic mode functions, is the set of central frequencies of the intrinsic mode functions, λ is the Lagrange multiplier, α is the quadratic penalty factor, T is the number of intrinsic mode functions, To find the derivative with respect to time t, is the Dirac function, j is the imaginary part, * is the convolution, f (t) is the constraint condition; The final modal function and the final center frequency are obtained through iterative updating based on the alternating direction multiplier method.

[0009] A further improvement of the present invention is that the process of the ensemble empirical mode decomposition algorithm is as follows: Initialize the white noise standard deviation and the white noise average times; Adding white noise to the corresponding historical meteorological data to obtain a superimposed signal; Decomposing the superimposed signal to obtain a secondary decomposition intrinsic mode function and a first-order intrinsic mode component thereof; Obtaining the residual of the intrinsic modal component and obtaining the second-order intrinsic modal component; adding white noise to the residual; The residual is updated to obtain a k-order residual.

[0010] A further improvement of the present invention is that the step of calculating the predicted cost of the photovoltaic system and constructing a photovoltaic system cost prediction model comprises: A photovoltaic system power threshold is preset, and when the photovoltaic system predicted power is greater than the photovoltaic system power threshold, the photovoltaic system material statistical data is reduced to a preset minimum material cost threshold, and the photovoltaic system predicted cost is updated and calculated; When the photovoltaic system predicted power is less than or equal to the photovoltaic system power threshold, the photovoltaic system predicted cost is directly calculated.

[0011] An intelligent computing system for material statistics and cost prediction of a photovoltaic system, comprising a data statistics module, a power prediction module and a cost prediction module; The data statistics module is used to obtain statistical data on photovoltaic system materials, which are statistical data on the costs of component materials required to support the normal operation of the photovoltaic system; and obtain statistical data on photovoltaic system construction, which are statistical data on all construction costs other than component materials required for the construction of the photovoltaic system; The power prediction module is used to obtain the predicted power of the photovoltaic system by improving the quadratic decomposition algorithm; The cost prediction module is used to calculate the predicted cost of the photovoltaic system through the statistical data of photovoltaic system materials, the statistical data of photovoltaic system construction, and the predicted power of the photovoltaic system, and to construct a cost prediction model of the photovoltaic system. The predicted cost of the photovoltaic system is the sum of the statistical data of photovoltaic system materials, the statistical data of photovoltaic system construction, and other costs.

[0012] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the steps of the intelligent computing method are implemented when the processor executes the program.

[0013] A storage medium containing computer executable instructions, which are used to perform the steps of the intelligent computing method when executed by a computer processor.

[0014] Compared with the prior art, the present invention has at least the following beneficial technical effects: The present invention provides an intelligent calculation method and system for material statistics and cost prediction of a photovoltaic system. The method calculates the predicted cost of the photovoltaic system through statistical data on photovoltaic system materials, statistical data on photovoltaic system construction, and predicted power of the photovoltaic system, and constructs a photovoltaic system cost prediction model. The method scientifically and reasonably predicts the cost of the photovoltaic system, and comprehensively considers the influence of local meteorological factors on the power of the photovoltaic system. If the local meteorological factors have a large positive feedback on the power of the photovoltaic system, the material cost of the photovoltaic system can be appropriately reduced to achieve the effect of saving resources. The method performs a primary decomposition through a variational mode decomposition algorithm. Variational mode decomposition is a signal decomposition method that can adaptively decompose an input signal into a set of intrinsic mode functions, has good noise resistance, and effectively solves the problem of modal component aliasing. The method performs a secondary decomposition through an ensemble empirical mode decomposition algorithm. Gaussian white noise is added to the ensemble empirical mode decomposition, thereby solving the problem of residual white noise increasing the reconstruction error, and making the reconstruction error almost zero. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 The present invention is a flow chart of an intelligent calculation method for photovoltaic system material statistics and cost prediction.

[0017] Figure 2 The present invention is a structural block diagram of an intelligent computing system for material statistics and cost prediction of a photovoltaic system. DETAILED DESCRIPTION

[0018] In the following, only some exemplary embodiments are briefly described. As those skilled in the art will appreciate, the described embodiments may be modified in various ways without departing from the spirit or scope of the present invention. Therefore, the drawings and descriptions are considered to be exemplary and non-restrictive in nature.

[0019] It should be understood that when used in this specification and the appended claims, the terms "include" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0020] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0021] It should be further understood that the term "and / or" used in the present description and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0022] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0023] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0024] Example 1 like Figure 1 As shown, the present invention provides an intelligent calculation method for photovoltaic system material statistics and cost prediction, including: S1: Obtaining statistical data of materials for a photovoltaic system, where the statistical data of materials for a photovoltaic system is statistical data of costs of component materials required to support normal operation of the photovoltaic system; S2: Obtaining photovoltaic system construction statistical data, which are all construction cost statistics required for photovoltaic system construction except for component materials, such as land occupation costs, labor costs, etc.; S3: Obtaining the predicted power of the photovoltaic system by improving the quadratic decomposition algorithm; S4: Calculate the predicted cost of the photovoltaic system through the statistical data of photovoltaic system materials, the statistical data of photovoltaic system construction and the predicted power of the photovoltaic system, and construct a photovoltaic system cost prediction model. The predicted cost of the photovoltaic system is the sum of the statistical data of photovoltaic system materials, the statistical data of photovoltaic system construction and other costs. The other costs include but are not limited to photovoltaic system maintenance costs and other costs generated during the photovoltaic operation process.

[0025] In this embodiment, the photovoltaic system predicted power is obtained by improving the quadratic decomposition algorithm, which can be implemented by the following steps: S301: Acquire historical meteorological data of the location of the photovoltaic power plant and historical photovoltaic power data of the location, wherein the historical meteorological data of the location of the photovoltaic power plant is the average data set and the maximum data set of meteorological factor data of the location of the photovoltaic power plant in the previous year during the period of 07:00-19:00 every day, and the historical photovoltaic power data of the location is the historical daily average data set of photovoltaic power of other photovoltaic systems in the location, and the historical meteorological data of the location of the photovoltaic power plant includes the average daily wind speed, maximum wind speed, average daily rainfall, maximum rainfall, average daily humidity, maximum humidity, average daily temperature, maximum temperature, average daily horizontal radiation, maximum horizontal radiation, average daily diffuse horizontal radiation, maximum diffuse horizontal radiation, average daily inclined radiation, maximum inclined radiation, average daily diffuse inclined radiation, and maximum diffuse inclined radiation of the ith day, i∈[1,365], and select photovoltaic-related meteorological factors according to the historical meteorological data of the location of the photovoltaic power plant, and the photovoltaic-related meteorological factors include the first photovoltaic-related meteorological factor, the second photovoltaic-related meteorological factor, the third photovoltaic-related meteorological factor, the fourth photovoltaic-related meteorological factor, the fifth photovoltaic-related meteorological factor, and the sixth photovoltaic-related meteorological factor; Based on the correlation between the historical meteorological data of the photovoltaic location and the historical photovoltaic power data of the location, six meteorological factors corresponding to the historical meteorological data of the photovoltaic location with the highest correlation are selected as the photovoltaic-related meteorological factors.

[0026] S302: Clustering the dates of the previous year based on the photovoltaic-related meteorological factors to obtain date types and corresponding historical meteorological data, the date types include a first date type, a second date type, and a third date type, and the corresponding historical meteorological data are the photovoltaic power history data of the location corresponding to the photovoltaic-related meteorological factors of the date type, such as the daily average and maximum values ​​of the relevant meteorological factors on the day corresponding to all dates within the first date type; S303: Decomposing the corresponding historical meteorological data by using the improved secondary decomposition algorithm, wherein the improved secondary decomposition algorithm includes primary decomposition and secondary decomposition; S304: constructing a photovoltaic power prediction model by using a particle swarm algorithm, and inputting L1+L2-1 vectors obtained by processing the improved quadratic decomposition algorithm into the photovoltaic power prediction model to obtain the predicted power of the photovoltaic system.

[0027] The particle swarm algorithm initializes the position and velocity of the particle swarm, continuously iterates and updates the position and velocity of the particles, and finally finds the optimal solution. In each iteration, the particles adjust their speed and direction according to their own historical best position and global best position, thus moving towards the direction of the optimal solution.

[0028] In this embodiment, based on the photovoltaic-related meteorological factors, the dates of the previous year are clustered to obtain date types and corresponding historical meteorological data, which can be specifically implemented by the following steps: S302-1: Constructing a photovoltaic characteristic vector based on the photovoltaic-related meteorological factors, the expression of the photovoltaic characteristic vector is: , where X i is the photovoltaic eigenvector, is the daily average value of the jth photovoltaic-related meteorological factor on the i-th day, is the maximum value of the jth photovoltaic-related meteorological factor on the i-th day, j∈[1,6]; S302-2: Clustering is performed based on the CLARANS algorithm and the photovoltaic feature vector to obtain the date type. The basic steps of the CLARANS algorithm are: (1) Set the maximum number of local optimal solutions and the maximum number of neighbors for the parameter, initialize the minimum cost, and initialize y to 1, where y is the number of local optimal solutions for the parameter; (2) Selecting k elements from the photovoltaic feature vector to form a feature set Current; (3) Let j = 1, where j is the number of neighbors; (4) randomly selecting an element from the remaining 12-k elements of the photovoltaic feature vector to replace any element in the feature set Current, obtaining a new feature set Current, and obtaining the cost difference between the feature set Current and the new feature set Current; (5) Setting a cost difference threshold. When the cost difference is less than the cost difference threshold, using the new feature set Current to replace the feature set Current, and returning to step (3). (6) When the cost difference is greater than or equal to the cost difference threshold, set j = j + 1; when j is less than or equal to the maximum number of neighbors, return to step (4); (7) When j is greater than the maximum number of neighbors, compare the cost of the current feature set Current with the minimum cost; when the cost of the current feature set Current is less than the minimum cost, set the minimum cost as the cost of the current feature set Current; (8) Let y = y + 1. When y is greater than the maximum number of local optimal solutions for the parameter, output the current feature set Current. When y is less than or equal to the maximum number of local optimal solutions for the parameter, return to step (2).

[0029] In this embodiment, the corresponding historical meteorological data is decomposed by the improved secondary decomposition algorithm, which can be specifically implemented by the following steps: S303-1: Perform the first decomposition using a variational mode decomposition algorithm to obtain L1 first decomposition vectors. Variational mode decomposition is a signal decomposition method that can adaptively decompose an input signal into a set of intrinsic mode functions. It has good noise immunity and effectively solves the problem of modal component aliasing. The process of the variational mode decomposition algorithm is as follows: Taking the corresponding historical meteorological data as an input signal and decomposing it into an intrinsic mode function; Calculating the analytical signal of the intrinsic mode function by Hilbert transform to obtain a single-sided spectrum; Modulating the spectrum of the intrinsic mode function to a corresponding baseband; Demodulating the input signal through Gaussian smoothing to obtain the bandwidth of the intrinsic mode function and obtain the corresponding constrained variational problem; The corresponding constrained variational problem is transformed into an unconstrained problem by using quadratic penalty and Lagrange multipliers, which is mathematically described as , where L is the augmented Lagrangian function, u is the set of intrinsic mode functions, is the set of central frequencies of the intrinsic mode functions, λ is the Lagrange multiplier, α is the quadratic penalty factor, T is the number of intrinsic mode functions, To find the derivative with respect to time t, is the Dirac function, j is the imaginary part, * is the convolution, f (t) is the constraint condition; The final modal function and the final center frequency are obtained through iterative updating based on the alternating direction multiplier method.

[0030] S303-2: Perform the secondary decomposition by using the ensemble empirical mode decomposition algorithm to obtain L2 secondary decomposition vectors. Gaussian white noise is added to the ensemble empirical mode decomposition, thereby solving the problem of residual white noise increasing the reconstruction error, making the reconstruction error almost 0. The process of the ensemble empirical mode decomposition algorithm is as follows: Initialize the white noise standard deviation and the white noise average times; Adding white noise to the corresponding historical meteorological data to obtain a superimposed signal; Decomposing the superimposed signal to obtain a secondary decomposition intrinsic mode function and a first-order intrinsic mode component thereof; Obtaining the residual of the intrinsic modal component and obtaining the second-order intrinsic modal component; adding white noise to the residual; The residual is updated to obtain a k-order residual.

[0031] In this embodiment, the photovoltaic system predicted cost is calculated and a photovoltaic system cost prediction model is constructed, which can be implemented by the following steps: A photovoltaic system power threshold is preset, and when the photovoltaic system predicted power is greater than the photovoltaic system power threshold, the photovoltaic system material statistical data is reduced to a preset minimum material cost threshold, and the photovoltaic system predicted cost is updated and calculated; When the photovoltaic system predicted power is less than or equal to the photovoltaic system power threshold, the photovoltaic system predicted cost is directly calculated.

[0032] Example 2 like Figure 2 As shown, the present invention provides an intelligent computing system for photovoltaic system material statistics and cost prediction, including a data statistics module, a power prediction module and a cost prediction module; The data statistics module is used to obtain photovoltaic system material statistics, which are cost statistics of component materials required to support the normal operation of the photovoltaic system; obtain photovoltaic system construction statistics, which are all construction cost statistics required for photovoltaic system construction except component materials, such as land occupation costs, labor costs, etc.; The power prediction module is used to obtain the predicted power of the photovoltaic system by improving the quadratic decomposition algorithm; The cost prediction module is used to calculate the predicted cost of the photovoltaic system through the statistical data of photovoltaic system materials, the statistical data of photovoltaic system construction, and the predicted power of the photovoltaic system, and to construct a cost prediction model for the photovoltaic system. The predicted cost of the photovoltaic system is the sum of the statistical data of photovoltaic system materials, the statistical data of photovoltaic system construction, and other costs. The other costs include but are not limited to the maintenance costs of the photovoltaic system and other costs generated during the photovoltaic operation process.

[0033] Example 3 An electronic device provided by the present invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the steps of the intelligent computing method are implemented when the processor executes the program.

[0034] The electronic device may also include one or more of a multimedia component, an input / output (I / O) interface, and a communication component.

[0035] The processor is used to control the overall operation of the electronic device to complete all or part of the steps in the storage medium sharing method. The memory is used to store various types of data to support the operation of the electronic device, which may include instructions for any application or method used to operate on the electronic device, as well as application-related data, such as contact data, messages sent and received, pictures, audio, video, etc. The memory can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Read-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, magnetic disk or optical disk. The multimedia component may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving an external audio signal. The received audio signal may be further stored in a memory or sent through a communication component. The audio component also includes at least one speaker for outputting an audio signal. The I / O interface provides an interface between the processor and other interface modules, and the above-mentioned other interface modules may be a keyboard, a mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component is used for wired or wireless communication between the electronic device and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G or 4G, or a combination of one or more of them, so the corresponding communication component may include: Wi-Fi module, Bluetooth module, NFC module.

[0036] In an exemplary embodiment, the electronic device can be implemented by one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the storage medium sharing method.

[0037] Example 4 The present invention provides a storage medium containing computer executable instructions, and the computer executable instructions are used to execute the steps of the intelligent computing method when executed by a computer processor.

[0038] The computer storage medium of the present embodiment can adopt any combination of one or more computer-readable media. Computer-readable media can be computer-readable signal media or computer-readable storage media. Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or devices, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: electrical connections with one or more wires, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this document, computer-readable storage media can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0039] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0040] The program code included on the computer readable medium can be transmitted with any appropriate medium, including but not limited to wireless, electric wire, optical cable, RF, etc., or any suitable combination of the above. The computer program code for performing the operation of the present invention can be written in one or more programming languages ​​or their combinations, and the programming language includes object-oriented programming languages-such as Java, Smalltalk, C++, and also includes conventional procedural programming languages-such as "C" language or similar programming languages. The program code can be executed completely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on the remote computer, or completely on the remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0041] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and the present invention can be implemented in other specific forms without departing from the spirit or basic features of the present invention. Therefore, no matter from which point of view, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the attached claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any figure mark in the claims should not be regarded as limiting the claims involved.

[0042] In addition, it should be understood that although this specification is described in accordance with the implementation modes, not every implementation mode contains only one independent technical solution. This description of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation modes that can be understood by those skilled in the art. The above content is only to illustrate the technical idea of ​​the present invention, and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution according to the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. An intelligent calculation method for photovoltaic system material statistics and cost prediction, characterized in that: The following steps are involved: Obtaining statistical data on materials of a photovoltaic system, wherein the statistical data on materials of a photovoltaic system is statistical data on the cost of component materials required to support normal operation of the photovoltaic system; Obtaining photovoltaic system construction statistical data, wherein the photovoltaic system construction statistical data is all construction cost statistical data required for photovoltaic system construction except for component materials; The predicted power of photovoltaic system is obtained by improving the quadratic decomposition algorithm; The predicted cost of the photovoltaic system is calculated through the statistical data of photovoltaic system materials, the statistical data of photovoltaic system construction and the predicted power of the photovoltaic system, and a photovoltaic system cost prediction model is constructed. The predicted cost of the photovoltaic system is the sum of the statistical data of photovoltaic system materials, the statistical data of photovoltaic system construction and other costs.

2. The intelligent computing method according to claim 1, characterized in that: The method of obtaining the predicted power of the photovoltaic system by improving the secondary decomposition algorithm includes: Obtain historical meteorological data of the location of the photovoltaic power plant and historical photovoltaic power data of the location, wherein the historical meteorological data of the location of the photovoltaic power plant include the average daily wind speed, maximum wind speed, average daily rainfall, maximum rainfall, average daily humidity, maximum humidity, average daily temperature, maximum temperature, average daily horizontal radiation, maximum horizontal radiation, average daily diffuse horizontal radiation, maximum diffuse horizontal radiation, average daily inclined radiation, maximum inclined radiation, average daily diffuse inclined radiation, and maximum diffuse inclined radiation of the ith day, i∈[1,365], and select photovoltaic-related meteorological factors according to the historical meteorological data of the location of the photovoltaic power plant, wherein the photovoltaic-related meteorological factors include the first photovoltaic-related meteorological factor, the second photovoltaic-related meteorological factor, the third photovoltaic-related meteorological factor, the fourth photovoltaic-related meteorological factor, the fifth photovoltaic-related meteorological factor, and the sixth photovoltaic-related meteorological factor; Clustering the dates of the previous year based on the photovoltaic-related meteorological factors to obtain date types and corresponding historical meteorological data, wherein the date types include a first date type, a second date type, and a third date type; Decomposing the corresponding historical meteorological data by using the improved secondary decomposition algorithm, wherein the improved secondary decomposition algorithm includes primary decomposition and secondary decomposition; A photovoltaic power prediction model is constructed by using a particle swarm algorithm, and L1+L2-1 vectors obtained by processing the improved quadratic decomposition algorithm are input into the photovoltaic power prediction model to obtain the predicted power of the photovoltaic system.

3. The intelligent computing method according to claim 2, characterized in that: The clustering of the dates of the previous year based on the photovoltaic-related meteorological factors to obtain date types and corresponding historical meteorological data includes: A photovoltaic characteristic vector is constructed based on the photovoltaic related meteorological factors. The expression of the photovoltaic characteristic vector is: , where X i is the photovoltaic eigenvector, is the daily average value of the jth photovoltaic-related meteorological factor on the i-th day, is the maximum value of the jth photovoltaic-related meteorological factor on the i-th day, j∈[1,6]; The date type is obtained by clustering based on the CLARANS algorithm and the photovoltaic feature vector. The basic steps of the CLARANS algorithm are: (1) Set the maximum number of local optimal solutions and the maximum number of neighbors for the parameter, initialize the minimum cost, and initialize y to 1, where y is the number of local optimal solutions for the parameter; (2) Selecting k elements from the photovoltaic feature vector to form a feature set Current; (3) Let j = 1, where j is the number of neighbors; (4) randomly selecting an element from the remaining 12-k elements of the photovoltaic feature vector to replace any element in the feature set Current, obtaining a new feature set Current, and obtaining the cost difference between the feature set Current and the new feature set Current; (5) Setting a cost difference threshold. When the cost difference is less than the cost difference threshold, using the new feature set Current to replace the feature set Current, and returning to step (3). (6) When the cost difference is greater than or equal to the cost difference threshold, set j = j + 1; when j is less than or equal to the maximum number of neighbors, return to step (4); (7) When j is greater than the maximum number of neighbors, compare the cost of the current feature set Current with the minimum cost; when the cost of the current feature set Current is less than the minimum cost, set the minimum cost as the cost of the current feature set Current; (8) Let y = y + 1. When y is greater than the maximum number of local optimal solutions for the parameter, output the current feature set Current. When y is less than or equal to the maximum number of local optimal solutions for the parameter, return to step (2).

4. The intelligent computing method according to claim 2, characterized in that: Decomposing the corresponding historical meteorological data by using the improved secondary decomposition algorithm includes: Performing the first decomposition by a variational mode decomposition algorithm to obtain L1 first decomposition vectors; The secondary decomposition is performed using a set empirical mode decomposition algorithm to obtain L2 secondary decomposition vectors.

5. The intelligent computing method according to claim 4, characterized in that: The process of the variational mode decomposition algorithm is: Taking the corresponding historical meteorological data as an input signal and decomposing it into an intrinsic mode function; Calculating the analytical signal of the intrinsic mode function by Hilbert transform to obtain a single-sided spectrum; Modulating the spectrum of the intrinsic mode function to a corresponding baseband; Demodulating the input signal through Gaussian smoothing to obtain the bandwidth of the intrinsic mode function and obtain the corresponding constrained variational problem; The corresponding constrained variational problem is transformed into an unconstrained problem by using quadratic penalty and Lagrange multipliers, which is mathematically described as , where L is the augmented Lagrangian function, u is the set of intrinsic mode functions, is the set of central frequencies of the intrinsic mode functions, λ is the Lagrange multiplier, α is the quadratic penalty factor, T is the number of intrinsic mode functions, To find the derivative with respect to time t, is the Dirac function, j is the imaginary part, * is the convolution, f (t) is the constraint condition; The final modal function and the final center frequency are obtained through iterative updating based on the alternating direction multiplier method.

6. The intelligent computing method according to claim 4, characterized in that: The process of the ensemble empirical mode decomposition algorithm is as follows: Initialize the white noise standard deviation and the white noise average times; Adding white noise to the corresponding historical meteorological data to obtain a superimposed signal; Decomposing the superimposed signal to obtain a secondary decomposition intrinsic mode function and a first-order intrinsic mode component thereof; Obtaining the residual of the intrinsic modal component and obtaining the second-order intrinsic modal component; adding white noise to the residual; The residual is updated to obtain a k-order residual.

7. The intelligent computing method according to claim 1, characterized in that: The calculating of the predicted cost of the photovoltaic system and constructing the photovoltaic system cost prediction model comprises: A photovoltaic system power threshold is preset, and when the photovoltaic system predicted power is greater than the photovoltaic system power threshold, the photovoltaic system material statistical data is reduced to a preset minimum material cost threshold, and the photovoltaic system predicted cost is updated and calculated; When the photovoltaic system predicted power is less than or equal to the photovoltaic system power threshold, the photovoltaic system predicted cost is directly calculated.

8. An intelligent computing system for photovoltaic system material statistics and cost prediction, characterized in that: Including data statistics module, power prediction module and cost prediction module; The data statistics module is used to obtain statistical data on photovoltaic system materials, which are statistical data on the costs of component materials required to support the normal operation of the photovoltaic system; and obtain statistical data on photovoltaic system construction, which are statistical data on all construction costs other than component materials required for the construction of the photovoltaic system; The power prediction module is used to obtain the predicted power of the photovoltaic system by improving the quadratic decomposition algorithm; The cost prediction module is used to calculate the predicted cost of the photovoltaic system through the statistical data of photovoltaic system materials, the statistical data of photovoltaic system construction, and the predicted power of the photovoltaic system, and to construct a cost prediction model of the photovoltaic system. The predicted cost of the photovoltaic system is the sum of the statistical data of photovoltaic system materials, the statistical data of photovoltaic system construction, and other costs.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the intelligent computing method as described in any one of claims 1 to 7 are implemented.

10. A storage medium containing computer executable instructions, characterized in that: When the computer executable instructions are executed by a computer processor, they are used to perform the steps of the intelligent computing method as described in any one of claims 1 to 7.

Images