Photovoltaic equipment fault prediction method based on big data analysis and related products
Through big data analysis and model training, the problems of accuracy and timeliness in photovoltaic equipment fault prediction have been solved, realizing intelligent operation and maintenance of photovoltaic equipment and reducing operation and maintenance costs.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- 华能(嘉峪关)新能源有限公司
- Filing Date
- 2024-11-22
- Publication Date
- 2026-05-22
AI Technical Summary
Existing methods for predicting photovoltaic equipment failures mainly rely on periodic inspections and single characteristic indicators. They lack large-scale data mining, making it difficult to cope with complex and ever-changing real-world application scenarios. Furthermore, they depend on expert knowledge, making it difficult to achieve timely failure detection.
By employing a big data analytics approach, we collect operational data from photovoltaic (PV) equipment, train and predict using SVM or time series forecasting models, and combine data preprocessing and correlation analysis to achieve real-time fault prediction for PV equipment.
It improves the accuracy and timeliness of fault prediction, reduces the risk of equipment failure and operation and maintenance costs, and promotes the intelligent and automated operation and maintenance of photovoltaic equipment.
Smart Images
Figure CN122072853A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of intelligent photovoltaic technology, and relates to a method for predicting photovoltaic equipment faults based on big data analysis and related products. Background Technology
[0002] With the continuous growth of global demand for renewable energy, photovoltaic (PV) power generation, as a clean and renewable energy source, is becoming increasingly important. PV equipment, especially PV inverters, is the core component of PV power generation systems, and its stability and reliability directly affect the power generation efficiency and operational safety of the entire system. However, PV equipment is often affected by various factors during operation, such as environmental factors like solar radiation intensity, temperature, and humidity, as well as the aging and wear of the equipment itself. These factors can all lead to equipment failure, thereby affecting the overall performance of the PV power generation system.
[0003] Traditional photovoltaic (PV) equipment maintenance strategies primarily rely on periodic inspections and repairs. This approach is not only costly but also struggles to detect potential faults in a timely manner. While some research has been conducted on PV equipment fault diagnosis, most methods are based on single characteristic indicators or simple statistical analyses, lacking in-depth mining and utilization of large-scale data. Furthermore, these methods often depend on expert knowledge and experience, making them ill-suited for complex and ever-changing real-world application scenarios. Summary of the Invention
[0004] The purpose of this invention is to address the technical problem that most existing fault prediction methods rely on a single indicator or simple statistical analysis, lack the mining and utilization of large-scale data, and are unable to cope with complex and ever-changing real-world application scenarios. This invention provides a photovoltaic equipment fault prediction method and related products based on big data analysis.
[0005] To achieve the above objectives, the present invention employs the following technical solution: The first aspect of this invention provides a method for predicting photovoltaic equipment faults based on big data analysis, comprising the following steps: Collect operational data from photovoltaic equipment; The collected operating data of the photovoltaic equipment is input into a pre-trained fault prediction model to obtain the predicted photovoltaic equipment data; Based on predicted photovoltaic equipment data, photovoltaic equipment failure prediction is performed. The training method for the pre-trained fault prediction model is as follows: The historical operating data of the collected photovoltaic equipment is input into the prediction model for training, resulting in a pre-trained fault prediction model.
[0006] Furthermore, the operating data of the photovoltaic equipment includes data on parameters that affect equipment failure.
[0007] Furthermore, the prediction model is an SVM model or a time series prediction model.
[0008] Furthermore, the step of inputting the collected photovoltaic equipment operating data into a pre-trained fault prediction model to obtain predicted photovoltaic equipment data specifically involves: Preprocess the collected operating data of the photovoltaic equipment; The pre-processed operating data of the photovoltaic equipment is input into a pre-trained fault prediction model to obtain the predicted photovoltaic equipment data.
[0009] Furthermore, the preprocessing of the collected photovoltaic equipment operating data specifically includes: The collected photovoltaic equipment data is processed using a smoothing filter method to obtain smoothed data. Redundant data is removed by performing correlation analysis on the smoothed data.
[0010] Furthermore, the photovoltaic equipment fault prediction based on predicted photovoltaic equipment data specifically includes: Issue fault warnings for photovoltaic equipment whose data is outside the health threshold range.
[0011] A second aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described photovoltaic equipment fault prediction method based on big data analysis.
[0012] A third aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described photovoltaic equipment fault prediction method based on big data analysis.
[0013] A fourth aspect of the present invention provides a computer program product, the computer program product including computer instructions, characterized in that the computer instructions instruct a computer to execute the above-mentioned photovoltaic equipment fault prediction method based on big data analysis.
[0014] The fifth aspect of this invention provides a photovoltaic equipment fault prediction system based on big data analysis, comprising: The data acquisition module is used to collect operating data from photovoltaic equipment. The data prediction module is used to input the collected operating data of photovoltaic equipment into a pre-trained fault prediction model to obtain predicted photovoltaic equipment data. The fault prediction module is used to predict photovoltaic equipment faults based on predicted photovoltaic equipment data.
[0015] Compared with the prior art, the present invention has the following beneficial effects: This invention discloses a photovoltaic (PV) equipment fault prediction method based on big data analysis. By collecting comprehensive and accurate PV equipment operating data, the quality of the input data is ensured, thereby improving the prediction accuracy of the fault prediction model. Using historical operating data for model training enables the model to learn the fault modes and patterns of PV equipment under different operating conditions, further enhancing the model's predictive performance. Applying the trained fault prediction model to real-time data streams enables real-time monitoring of the PV equipment's operating status. Through real-time analysis and prediction, the system can issue timely fault warnings, helping maintenance personnel take proactive measures to reduce the risk and losses of faults. The model can process and analyze large amounts of data, thus possessing stronger generalization capabilities and enabling effective fault prediction under different PV equipment and operating environments.
[0016] Furthermore, by focusing on parameter data that directly affects photovoltaic equipment failures, the method of this invention can more accurately capture the precursors and key factors of failures, thereby improving the accuracy and timeliness of failure prediction. Selecting parameter data closely related to the failure helps reduce interference from irrelevant data, allowing the model to focus more on key information and enhancing its predictive capabilities. Attached Figure Description
[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.
[0018] Figure 1 This is a block diagram of the photovoltaic equipment fault prediction method based on big data analysis of the present invention; Figure 2 This is a block diagram of the photovoltaic equipment fault prediction system based on big data analysis of the present invention; Figure 3 This is a schematic diagram of a computer device provided according to an embodiment of the present invention.
[0019] Wherein: 60-Computer equipment; 61-Processor; 62-Memory; 63-Computer program; 201-Data acquisition module; 202-Data prediction module; 203-Fault prediction module. Detailed Implementation
[0020] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and marked in the accompanying drawings can generally be arranged and designed in various different configurations.
[0021] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.
[0022] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.
[0023] In the description of the embodiments of the present invention, it should be noted that if terms such as "upper," "lower," "horizontal," or "inner" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product of the invention is in use, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the present invention. Furthermore, terms such as "first" and "second" are only used to distinguish descriptions and should not be construed as indicating or implying relative importance.
[0024] Furthermore, the use of the term "horizontal" does not imply that the component must be absolutely horizontal, but rather that it can be slightly tilted. For example, "horizontal" simply means that its direction is more horizontal than "vertical," and does not mean that the structure must be completely horizontal, but can be slightly tilted.
[0025] In the description of the embodiments of the present invention, it should also be noted that, unless otherwise explicitly specified and limited, the terms "set," "install," "connect," and "link" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention according to the specific circumstances.
[0026] The present invention will now be described in further detail with reference to the accompanying drawings: See Figure 1This invention discloses a method for predicting photovoltaic equipment failures based on big data analysis, comprising the following steps: S1 collects operational data from photovoltaic equipment; First, operational data of the photovoltaic (PV) equipment is collected using various sensors installed on the equipment (such as current sensors, voltage sensors, and temperature sensors). This data includes, but is not limited to, the voltage, current, and temperature of the PV modules, the operating status of the inverter, and environmental factors (such as sunlight intensity and wind speed). The frequency and accuracy of data acquisition must meet the requirements of fault prediction, ensuring the authenticity and completeness of the data.
[0027] S2, input the collected photovoltaic equipment operation data into the pre-trained fault prediction model to obtain the predicted photovoltaic equipment data; input the real-time collected photovoltaic equipment operation data into the pre-trained fault prediction model to obtain the predicted photovoltaic equipment data.
[0028] The training method for the pre-trained fault prediction model is as follows: The historical operating data of the collected photovoltaic equipment is input into the prediction model for training, resulting in a pre-trained fault prediction model.
[0029] S3 predicts photovoltaic equipment failures based on predicted photovoltaic equipment data; it analyzes the prediction results output by the model, and once an anomaly or potential failure is detected, the system will automatically issue an early warning signal to help maintenance personnel take timely measures.
[0030] One embodiment of the present invention discloses a method for predicting photovoltaic equipment faults based on big data analysis, comprising the following steps: S1. Collect operational data from the photovoltaic (PV) equipment. This operational data comprises parameters that affect equipment failure. Various high-precision sensors (such as current sensors, voltage sensors, and temperature sensors) installed on the PV equipment are used to collect operational data in real time. This data covers all key parameters that may affect the performance and lifespan of the PV equipment during operation, such as the output current, output voltage, and surface temperature of the PV modules, as well as the operating status of the inverter. The frequency and accuracy of data acquisition must meet the requirements of fault prediction, ensuring the completeness and accuracy of the data.
[0031] S2, input the collected photovoltaic equipment operation data into the pre-trained fault prediction model to obtain the predicted photovoltaic equipment data; the prediction model is an SVM model or a time series prediction model.
[0032] S201. The collected photovoltaic equipment data is processed using a smoothing filter method to obtain smoothed data. Smoothing filter methods (such as moving average filtering, median filtering, Gaussian filtering, etc.) are used to process the collected raw data to eliminate random noise and fluctuations, resulting in smoothed data. This step helps improve the accuracy and reliability of subsequent data analysis.
[0033] S202 removes redundant data by performing correlation analysis on the smoothed data.
[0034] Correlation analysis is performed on the smoothed data to identify the correlation strength between various parameters. By analyzing the correlation between different parameters, the operating status and changing patterns of photovoltaic equipment can be further understood. Based on the correlation analysis results, redundant data that are highly correlated with other parameters but contribute little to fault prediction are removed. This embodiment uses the Pearson correlation coefficient to measure the correlation between variables; variables with an absolute correlation coefficient between 0.7 and 0.8 are considered to be strongly correlated. Based on the correlation analysis results, combined with expert knowledge or experimental verification, it is determined which parameters are truly redundant, and which redundant parameters contribute little to fault prediction.
[0035] S203: Input the pre-processed photovoltaic equipment operation data into the pre-trained fault prediction model to obtain the predicted photovoltaic equipment data.
[0036] The training method for the pre-trained fault prediction model is as follows: Historical operating data of the collected photovoltaic equipment is input into the prediction model for training, resulting in a pre-trained fault prediction model. The fault prediction model is trained in advance using historical operating data of the photovoltaic equipment. During training, the historical data is divided into a training set and a validation set (or test set), and the model parameters are adjusted using iterative optimization algorithms (such as gradient descent) until the model achieves satisfactory predictive performance on the validation set.
[0037] The pre-processed photovoltaic equipment operating data is used as input and fed into a pre-trained fault prediction model. The model will calculate and output predicted photovoltaic equipment data based on the input data.
[0038] For model training, this embodiment provides two options: Support Vector Machine (SVM) model and time series prediction model. In practice, the appropriate model can be selected for training based on specific application requirements and data characteristics; this embodiment selects the SVM model.
[0039] S3, based on predicted photovoltaic (PV) equipment data, predicts PV equipment faults and issues fault warnings for PV equipment whose data is outside the health threshold range. The prediction results output by the model are analyzed to determine whether the PV equipment is in a healthy state or has potential fault risks. When the prediction result indicates that the PV equipment data is outside the health threshold range, the system automatically issues a fault warning.
[0040] The method of this invention, through meticulous data preprocessing and model selection, can more accurately predict photovoltaic equipment failures, effectively reducing the probability of failures and losses. Through real-time monitoring and fault warning mechanisms, this method can promptly detect and address potential failure risks, enhancing system stability and reliability. By providing early warnings and preventative maintenance, this method can effectively reduce unnecessary maintenance work and spare parts inventory, lowering the operation and maintenance costs of photovoltaic equipment. Furthermore, this method is closely integrated with modern information technologies (such as the Internet of Things, cloud computing, and artificial intelligence), promoting the intelligent and automated operation and maintenance management of photovoltaic equipment, and improving operation and maintenance efficiency and decision-making quality.
[0041] One embodiment of the present invention provides a photovoltaic equipment fault prediction system based on big data analysis, comprising: Data acquisition module 201 is used to collect operating data of photovoltaic equipment; The data prediction module 202 is used to input the collected operating data of the photovoltaic equipment into the pre-trained fault prediction model to obtain the predicted photovoltaic equipment data. The fault prediction module 203 is used to predict photovoltaic equipment faults based on the predicted photovoltaic equipment data.
[0042] In one embodiment of the present invention, a terminal device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve a corresponding method flow or corresponding function. The processor described in this embodiment can be used for the operation of a photovoltaic equipment fault prediction method based on big data analysis, including: Collect operational data from photovoltaic equipment; The collected operating data of the photovoltaic equipment is input into a pre-trained fault prediction model to obtain the predicted photovoltaic equipment data; Based on predicted photovoltaic equipment data, photovoltaic equipment failure prediction is performed. The training method for the pre-trained fault prediction model is as follows: The historical operating data of the collected photovoltaic equipment is input into the prediction model for training, resulting in a pre-trained fault prediction model.
[0043] In one embodiment of the present invention, a storage medium is also provided, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and extended storage media supported by the terminal device; it can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor, which can be one or more computer programs (including program code). It should be noted that more specific examples (a non-exhaustive list) of the computer-readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.
[0044] Computer-readable storage media also include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium can also be any readable medium other than a readable storage medium that can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium can be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.
[0045] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages such as Java and C++, and conventional procedural programming languages such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).
[0046] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the photovoltaic equipment fault prediction method based on big data analysis in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor in the following steps: Collect operational data from photovoltaic equipment; The collected operating data of the photovoltaic equipment is input into a pre-trained fault prediction model to obtain the predicted photovoltaic equipment data; Based on predicted photovoltaic equipment data, photovoltaic equipment failure prediction is performed. The training method for the pre-trained fault prediction model is as follows: The historical operating data of the collected photovoltaic equipment is input into the prediction model for training, resulting in a pre-trained fault prediction model.
[0047] Figure 3 This is a schematic diagram of a computer device provided according to an embodiment of the present invention.
[0048] Please see Figure 3 The terminal device is a computer device. In this embodiment, the computer device 60 includes a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When executed by the processor 61, the computer program 63 implements the fluid composition calculation method in the reservoir stimulation wellbore of this embodiment. To avoid repetition, details are omitted here. Alternatively, when executed by the processor 61, the computer program 63 implements the functions of each model / unit in the photovoltaic equipment fault prediction system based on big data analysis. To avoid repetition, details are omitted here.
[0049] Computer device 60 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. Computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will understand that... Figure 3 This is merely an example of computer device 60 and does not constitute a limitation on computer device 60. It may include more or fewer components than shown, or combine certain components, or different components. For example, computer device may also include input / output devices, network access devices, buses, etc.
[0050] The processor 61 may be a central processing unit (CPU), or other general-purpose processors, CPUs, graphics processing units (GPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, quantum computing-based data processing logic units, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.
[0051] The memory 62 can be an internal storage unit of the computer device 60, such as a hard disk or RAM of the computer device 60. The memory 62 can also be an external storage device of the computer device 60, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on the computer device 60.
[0052] Furthermore, the memory 62 may include both internal storage units of the computer device 60 and external storage devices. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 can also be used to temporarily store data that has been output or will be output.
[0053] Any references to memory, databases, or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.
[0054] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.
[0055] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.
Claims
1. A method for predicting photovoltaic equipment faults based on big data analysis, characterized in that, Includes the following steps: Collect operational data from photovoltaic equipment; The collected operating data of the photovoltaic equipment is input into a pre-trained fault prediction model to obtain the predicted photovoltaic equipment data; Based on predicted photovoltaic equipment data, photovoltaic equipment failure prediction is performed. The training method for the pre-trained fault prediction model is as follows: The historical operating data of the collected photovoltaic equipment is input into the prediction model for training, resulting in a pre-trained fault prediction model.
2. The photovoltaic equipment fault prediction method based on big data analysis according to claim 1, characterized in that, The operating data of the photovoltaic equipment refers to the data of parameters that affect equipment failure.
3. The photovoltaic equipment fault prediction method based on big data analysis according to claim 1, characterized in that, The prediction model is an SVM model or a time series prediction model.
4. The photovoltaic equipment fault prediction method based on big data analysis according to claim 1, characterized in that, The process involves inputting the collected operating data of the photovoltaic equipment into a pre-trained fault prediction model to obtain predicted photovoltaic equipment data. Specifically: Preprocess the collected operating data of the photovoltaic equipment; The pre-processed operating data of the photovoltaic equipment is input into a pre-trained fault prediction model to obtain the predicted photovoltaic equipment data.
5. The photovoltaic equipment fault prediction method based on big data analysis according to claim 4, characterized in that, The preprocessing of the collected photovoltaic equipment operating data specifically includes: The collected photovoltaic equipment data is processed using a smoothing filter method to obtain smoothed data. Redundant data is removed by performing correlation analysis on the smoothed data.
6. The photovoltaic equipment fault prediction method based on big data analysis according to claim 1, characterized in that, The photovoltaic equipment fault prediction based on predicted photovoltaic equipment data specifically includes: Issue fault warnings for photovoltaic equipment whose data is outside the health threshold range.
7. An electronic device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the photovoltaic equipment fault prediction method based on big data analysis as described in any one of claims 1-6.
8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the photovoltaic equipment fault prediction method based on big data analysis as described in any one of claims 1-6.
9. A computer program product, the computer program product comprising computer instructions, characterized in that, The computer instructions instruct the computer to execute the photovoltaic equipment fault prediction method based on big data analysis as described in any one of claims 1-6.
10. A photovoltaic equipment fault prediction system based on big data analysis, based on the photovoltaic equipment fault prediction method based on big data analysis as described in claim 1, characterized in that, include: The data acquisition module is used to collect operating data from photovoltaic equipment. The data prediction module is used to input the collected operating data of photovoltaic equipment into a pre-trained fault prediction model to obtain predicted photovoltaic equipment data. The fault prediction module is used to predict photovoltaic equipment faults based on predicted photovoltaic equipment data.