Solar radiation amount assessment method and apparatus, and electronic device and storage medium

Abstract

Provided in the present disclosure are a solar radiation amount assessment method and apparatus, and an electronic device and a storage medium. The method comprises: acquiring historical meteorological data of the place where a photovoltaic project is located, and performing data cleaning on the historical meteorological data, wherein the historical meteorological data is from various meteorological databases; on the basis of geographic location information of the photovoltaic project, performing data fusion processing on the cleaned historical meteorological data, so as to obtain target meteorological data of the place where the photovoltaic project is located; and performing data analysis on the target meteorological data, so as to determine radiation amount data of the place where the photovoltaic project is located. Compared with the related art, the historical meteorological data is acquired from various meteorological databases, such that information from different sources can be covered to the greatest extent, thereby reducing any bias or omissions that may be present in a single data source; and on the basis of accurate target meteorological data, the radiation amount data of the place where a photovoltaic project is located can be more accurately calculated, thereby improving the power generation efficiency and economic benefits of a photovoltaic system.

Description

Methods, apparatus, electronic devices and storage media for assessing solar radiation Technical Field

[0001] This disclosure relates to the field of data processing technology, and in particular to a method and apparatus, electronic device and storage medium for assessing solar radiation. Background Technology

[0002] Meteorological data plays a crucial role in the site selection process for photovoltaic (PV) power plants, particularly in assessing the solar radiation of the target plant. Solar radiation is a key indicator for measuring the power generation potential of a PV power plant, directly impacting its power generation efficiency and economic benefits. However, current methods for analyzing and calculating solar radiation are relatively simple, relying primarily on basic meteorological data such as sunshine duration and average irradiance. The accuracy and completeness of this data have certain limitations.

[0003] Specifically, existing methods for analyzing and calculating solar radiation often fail to adequately consider the influence of various complex factors, such as geographical location, topography, climate conditions, atmospheric transparency, cloud cover variations, and solar altitude angle. Changes in these factors can significantly impact the accurate calculation of solar radiation. Therefore, relying solely on simple meteorological data for analysis often fails to accurately reflect the actual solar radiation levels, thus preventing the maximization of power generation efficiency in photovoltaic power plants.

[0004] To improve the accuracy of photovoltaic power plant site selection and power generation efficiency, it is urgent to develop a more accurate method for analyzing and calculating solar radiation to achieve precise calculation of solar radiation. Summary of the Invention

[0005] This disclosure provides a method, apparatus, electronic device, and storage medium for assessing solar radiation. Its main purpose is to address the problem of low accuracy in calculating and assessing solar radiation at the site of photovoltaic projects during site selection.

[0006] According to a first aspect of this disclosure, a method for assessing solar radiation is provided, comprising:

[0007] Historical meteorological data of the photovoltaic project site is obtained and the historical meteorological data is cleaned. The historical meteorological data comes from multiple meteorological databases.

[0008] Based on the geographical location information of the photovoltaic project, the cleaned historical meteorological data is fused to obtain the target meteorological data for the location of the photovoltaic project.

[0009] Data analysis is performed on the target meteorological data to determine the radiation data of the location of the photovoltaic project.

[0010] In some embodiments, the method further includes:

[0011] Based on the radiation data, the solar energy resources at the location of the photovoltaic project are graded and classified.

[0012] In some embodiments, classifying and rating the solar energy resources at the location of the photovoltaic project based on the radiation data includes:

[0013] Based on the radiation data, calculate the average annual radiation and monthly radiation at the location of the photovoltaic project.

[0014] Using the target meteorological data, analyze and calculate the solar radiation stability coefficient of the location of the photovoltaic project;

[0015] The solar radiation level of the location of the photovoltaic project is determined based on the solar radiation stability coefficient, the annual average radiation, and the monthly radiation.

[0016] In some embodiments, the step of performing data fusion processing on the cleaned historical meteorological data based on the geographical location information of the photovoltaic project to obtain target meteorological data for the location of the photovoltaic project includes:

[0017] Based on the geographical location information, nearby meteorological stations for the photovoltaic project are determined, and these nearby meteorological stations are screened to identify target meteorological stations with similar climate conditions to the photovoltaic project.

[0018] The coupling algorithm is invoked to couple the historical meteorological data of the target meteorological station to obtain the target meteorological data.

[0019] In some embodiments, the method further includes:

[0020] If the target weather station cannot be determined based on the geographical location information, then satellite observation data should be searched and obtained;

[0021] A preset algorithm is invoked to interpolate the observation data at the location of the photovoltaic project to obtain the target meteorological data.

[0022] According to a second aspect of this disclosure, an apparatus for assessing solar radiation is provided, comprising:

[0023] The acquisition unit is used to acquire historical meteorological data of the location of the photovoltaic project and perform data cleaning on the historical meteorological data, wherein the historical meteorological data comes from multiple meteorological databases;

[0024] The fusion unit is used to perform data fusion processing on the cleaned historical meteorological data based on the geographical location information of the photovoltaic project to obtain the target meteorological data of the location of the photovoltaic project.

[0025] The determination unit is used to perform data analysis on the target meteorological data to determine the radiation data of the location of the photovoltaic project.

[0026] In some embodiments, the apparatus further includes:

[0027] The assessment unit is used to assess and classify the solar energy resources at the location of the photovoltaic project based on the radiation data.

[0028] In some embodiments, the evaluation unit includes:

[0029] The first calculation module is used to calculate the average annual radiation and monthly radiation of the location of the photovoltaic project based on the radiation data.

[0030] The second calculation module is used to analyze and calculate the solar radiation stability coefficient of the location of the photovoltaic project using the target meteorological data;

[0031] The first determining module is used to determine the solar radiation level of the location of the photovoltaic project based on the solar radiation stability coefficient, the annual average radiation, and the monthly radiation.

[0032] In some embodiments, the fusion unit includes:

[0033] The second determining module is used to determine the nearby meteorological stations of the photovoltaic project based on the geographical location information, and to screen the nearby meteorological stations to determine the target meteorological station with similar climate conditions to the photovoltaic project.

[0034] The processing module is used to call the coupling algorithm to perform coupling processing on the historical meteorological data of the target meteorological station to obtain the target meteorological data.

[0035] In some embodiments, the apparatus further includes:

[0036] The search unit is used to search for and obtain satellite observation data if the target meteorological station cannot be determined based on the geographical location information.

[0037] The interpolation unit is used to call a preset algorithm to interpolate the observation data at the location of the photovoltaic project to obtain the target meteorological data.

[0038] According to a third aspect of this disclosure, an electronic device is provided, comprising:

[0039] At least one processor; and

[0040] A memory communicatively connected to the at least one processor; wherein,

[0041] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method described in the first aspect above.

[0042] According to a fourth aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions, wherein the computer instructions are configured to cause the computer to perform the method described in the first aspect above.

[0043] According to a fifth aspect of this disclosure, a computer program product is provided, comprising a computer program that, when executed by a processor, implements the method described in the first aspect above.

[0044] This disclosure provides a method, apparatus, electronic device, and storage medium for assessing solar radiation. The method involves acquiring historical meteorological data of a photovoltaic (PV) project location and cleaning the historical meteorological data, which is sourced from multiple meteorological databases. Based on the geographical location information of the PV project, the cleaned historical meteorological data is fused to obtain target meteorological data for the PV project location. Data analysis is then performed on the target meteorological data to determine the radiation data for the PV project location. Compared to related technologies, acquiring historical meteorological data from multiple meteorological databases maximizes the coverage of information from different sources, reducing potential biases or omissions from single data sources. Based on accurate target meteorological data, the radiation data for the PV project location can be calculated more precisely, helping to optimize design parameters such as the layout, orientation, and tilt angle of the PV project, thereby improving the power generation efficiency and economic benefits of the PV system.

[0045] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0046] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0047] Figure 1 is a flowchart illustrating a method for assessing solar radiation provided in an embodiment of this disclosure;

[0048] Figure 2 is a schematic diagram of the structure of a device for assessing solar radiation provided in an embodiment of this disclosure;

[0049] Figure 3 is a schematic diagram of another device for evaluating solar radiation provided in an embodiment of this disclosure;

[0050] Figure 4 is a schematic block diagram of an example electronic device provided in an embodiment of this disclosure. Detailed Implementation

[0051] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0052] The following description, with reference to the accompanying drawings, outlines a method, apparatus, electronic device, and storage medium for assessing solar radiation according to embodiments of this disclosure.

[0053] Figure 1 is a flowchart illustrating a method for assessing solar radiation provided in an embodiment of this disclosure.

[0054] As shown in Figure 1, the method includes the following steps:

[0055] Step 101: Obtain historical meteorological data of the location of the photovoltaic project and perform data cleaning on the historical meteorological data, wherein the historical meteorological data comes from multiple meteorological databases.

[0056] In some embodiments of this disclosure, to accurately assess the feasibility of a photovoltaic project and optimize its design parameters, it is first necessary to obtain historical meteorological data of the project's location. Historical meteorological data can provide fundamental input information for subsequent project analysis. This historical meteorological data is not from a single source but comes from multiple meteorological databases, including but not limited to official databases of national meteorological bureaus, databases built by research institutions, and databases of commercial meteorological service providers. Each of these databases has different advantages; some are known for their comprehensiveness and accuracy, some provide high-resolution spatiotemporal data, and others can update data in real time, ensuring that we obtain the most representative meteorological information.

[0057] After collecting historical meteorological data from various sources, data cleaning is necessary. This process aims to identify and correct errors, omissions, or inconsistencies in the data, ensuring its accuracy and usability. This step includes checking data integrity, confirming that all necessary fields are filled and there are no missing values; verifying data accuracy, such as by comparing data from different sources to identify and correct potential errors; and handling outliers, i.e., values ​​that significantly deviate from the normal range, which may be due to measurement errors or equipment malfunctions. Furthermore, data cleaning also includes standardizing data formats to ensure seamless integration of data from different sources. This involves standardizing time formats (e.g., unifying date and time to ISO 8601 format), converting data units (e.g., converting temperature from Fahrenheit to Celsius), and ensuring data type consistency (e.g., converting text-formatted dates to date types).

[0058] Through this series of cleaning steps, high-quality, standardized historical meteorological data can be extracted from various meteorological databases.

[0059] Step 102: Based on the geographical location information of the photovoltaic project, the cleaned historical meteorological data is fused to obtain the target meteorological data of the location of the photovoltaic project.

[0060] In some embodiments of this disclosure, based on the specific geographical location information of the photovoltaic project, the cleaned and preprocessed historical meteorological data undergoes further data fusion processing to obtain more accurate, comprehensive, and applicable target meteorological data for the photovoltaic project location. By integrating information from different meteorological data sources and fully utilizing their respective advantages, a comprehensive meteorological dataset that reflects regional characteristics and possesses high spatiotemporal resolution is generated.

[0061] During the data fusion process, multiple meteorological data sources that most closely resemble the climate characteristics of the photovoltaic project's location are identified and selected based on the project's geographical location. These data sources may include official data from the National Meteorological Administration, precise monitoring data from research institutions, and real-time data from commercial meteorological service providers. By comparing the data quality, coverage, and update frequency of different data sources, it can be ensured that the selected data sources comprehensively reflect the meteorological conditions of the project location.

[0062] Next, data fusion algorithms are used to integrate these historical meteorological data from different data sources. Data fusion algorithms typically include weighted averaging, Kalman filtering, and Bayesian network methods, which can intelligently allocate weights, filter noise, and fuse information according to different application scenarios and data characteristics. During this process, we pay special attention to maintaining data consistency and continuity to avoid data distortion or loss during the fusion process.

[0063] In addition, the specific meteorological conditions of the photovoltaic project site must be considered, such as topography, altitude, and ocean influence, which can significantly affect meteorological data. Therefore, during data fusion, these specific conditions are taken into account, and the fusion algorithm is appropriately adjusted to ensure that the obtained target meteorological data accurately reflects the actual meteorological conditions of the project site.

[0064] Step 103: Perform data analysis on the target meteorological data to determine the radiation data of the location of the photovoltaic project.

[0065] In some embodiments of this disclosure, in-depth data analysis of the target meteorological data obtained through data fusion processing aims to extract key data directly related to solar radiation from complex and ever-changing meteorological information, providing a scientific basis for the planning, design, and performance evaluation of photovoltaic projects.

[0066] During data analysis, parameters directly related to the target meteorological data, such as total radiation, direct radiation, diffuse radiation, and sunshine duration, are used. These parameters comprehensively reflect the abundance and distribution of solar energy resources at the location of the photovoltaic project and form the basis for assessing the power generation potential of the photovoltaic system.

[0067] To accurately obtain this radiation data, analyzing the relationship between radiation and time series data can reveal trends in radiation variation across different time scales, such as daily, monthly, and interannual variations. Spatial distribution analysis can reveal differences in radiation across different geographical locations, helping to optimize the layout and site selection of photovoltaic projects.

[0068] This disclosure also utilizes mathematical models and algorithms, such as radiative transfer models and neural network prediction models, to further process and predict the target meteorological data. These models can comprehensively consider the influence of multiple meteorological factors (such as cloud cover, atmospheric transparency, and surface reflectivity) on solar radiation, thereby providing more accurate and reliable radiation prediction results.

[0069] This disclosure provides a method for assessing solar radiation. The method involves acquiring historical meteorological data of the photovoltaic (PV) project location and cleaning the historical meteorological data, which is sourced from multiple meteorological databases. Based on the geographical location information of the PV project, the cleaned historical meteorological data is fused to obtain target meteorological data for the PV project location. Data analysis is then performed on the target meteorological data to determine the radiation data for the PV project location. Compared to related technologies, acquiring historical meteorological data from multiple meteorological databases maximizes the coverage of information from different sources, reducing potential biases or omissions from single data sources. Accurate target meteorological data allows for more precise calculation of radiation data for the PV project location, helping to optimize design parameters such as the PV project's layout, orientation, and tilt angle, thereby improving the power generation efficiency and economic benefits of the PV system.

[0070] As one possible implementation of this disclosure, after obtaining the radiation data of the location of the photovoltaic project, the following methods can be used for grade assessment: the solar energy resources of the location of the photovoltaic project are graded and classified according to the radiation data.

[0071] Furthermore, the step of classifying and rating the solar energy resources at the location of the photovoltaic project based on the radiation data includes: calculating the annual average radiation and monthly radiation at the location of the photovoltaic project based on the radiation data; analyzing and calculating the solar radiation stability coefficient at the location of the photovoltaic project using the target meteorological data; and determining the solar radiation level at the location of the photovoltaic project based on the solar radiation stability coefficient, the annual average radiation, and the monthly radiation.

[0072] Specifically, based on the acquired radiation data, further in-depth analysis and calculations can be conducted to determine the solar energy resource status of the photovoltaic project site. First, this radiation data will be used to calculate the annual average radiation for the photovoltaic project site, a key indicator for assessing the total solar energy resources of the region. Simultaneously, by calculating the monthly radiation, the variation patterns of solar energy resources across different seasons can be revealed, which is crucial for optimizing the design and operation strategies of the photovoltaic system.

[0073] In addition to calculating the annual average and monthly radiation values, the solar radiation stability coefficient of the photovoltaic project site is analyzed and calculated using cleaned and fused target meteorological data. The solar radiation stability coefficient is a crucial parameter reflecting the stability and reliability of solar radiation. It helps us understand the fluctuations in solar radiation in a region and its variations under different weather conditions. By calculating the solar radiation stability coefficient, the quality of solar resources at the photovoltaic project site can be more comprehensively assessed, providing a more scientific basis for the design and operation and maintenance of photovoltaic systems.

[0074] After obtaining the annual average solar radiation, monthly solar radiation, and solar radiation stability coefficient, the solar radiation level of the photovoltaic project site will be determined based on these key indicators. The solar radiation level is a comprehensive indicator reflecting the abundance and utilization potential of solar energy resources in a region, and it plays a crucial guiding role in the site selection, design, and operation strategy formulation of photovoltaic projects. By scientifically and rationally classifying solar radiation levels, we can more accurately assess the solar energy resource status of photovoltaic project sites, providing stronger support for the development of the photovoltaic industry.

[0075] As a further feasible method of the above embodiment, when fusing meteorological data, the step of performing data fusion processing on the cleaned historical meteorological data based on the geographical location information of the photovoltaic project to obtain the target meteorological data of the location of the photovoltaic project includes: determining the nearby meteorological stations of the photovoltaic project according to the geographical location information, filtering the nearby meteorological stations, and determining the target meteorological station with similar climate conditions to the photovoltaic project; and calling a coupling algorithm to perform coupling processing on the historical meteorological data of the target meteorological station to obtain the target meteorological data.

[0076] Specifically, when fusing and processing cleaned historical meteorological data based on the geographical location information of photovoltaic projects, Geographic Information System (GIS) technology is used to accurately identify and locate all meteorological stations geographically adjacent to the photovoltaic project, according to the specific geographical location information of the photovoltaic project. These meteorological stations provide rich historical meteorological data resources, which form the basis for subsequent data fusion processing.

[0077] Not all data from nearby weather stations are suitable for analyzing photovoltaic (PV) projects. Therefore, further screening of these stations is necessary to determine which stations have climate conditions most similar to the PV project site. This screening process typically involves comparative analysis of historical data from the weather stations, including comparisons of multiple key meteorological elements such as temperature, humidity, wind speed, wind direction, and solar radiation, to ensure that the data from the selected stations accurately reflects the climate conditions of the PV project site.

[0078] After identifying target meteorological stations with similar climate conditions to the photovoltaic project, a coupling algorithm is used to couple the historical meteorological data from these stations. This coupling algorithm maximizes data accuracy and completeness while maintaining data consistency. During the coupling process, the spatiotemporal distribution characteristics of data from different meteorological stations and the interactions between various meteorological elements must be considered to ensure that the obtained target meteorological data comprehensively and accurately reflects the meteorological conditions of the photovoltaic project location.

[0079] Furthermore, when screening target meteorological stations, if it is determined that there is no meteorological station with similar climate conditions to the location of the photovoltaic project, the following methods can be used, but are not limited to: if the target meteorological station cannot be determined based on the geographical location information, then satellite observation data is searched and obtained; a preset algorithm is called to interpolate the observation data of the location of the photovoltaic project to obtain the target meteorological data.

[0080] Specifically, when a target meteorological station with similar climate conditions cannot be directly identified based on the geographical location information of a photovoltaic project, an alternative solution is adopted to ensure that accurate and reliable meteorological data of the target can still be obtained. The core of this alternative solution is to use satellite observation data to fill the gaps in ground-based meteorological station data.

[0081] Satellite remote sensing technology is used to locate and acquire satellite observation data related to the location of photovoltaic projects. This satellite observation data typically includes information on several key meteorological elements such as cloud cover, surface temperature, and solar radiation, which can provide important clues about the atmospheric and surface conditions of the photovoltaic project site.

[0082] However, satellite observation data often has limitations in spatial and temporal resolution, and therefore cannot directly replace data from ground-based weather stations. A pre-defined interpolation algorithm is needed to process the satellite observation data. When using the interpolation algorithm, the spatiotemporal distribution characteristics of the satellite observation data, as well as factors such as the topography and climate type of the photovoltaic project site, must be fully considered to ensure the accuracy and rationality of the interpolation results. Through interpolation processing, a more complete, continuous, and accurate set of meteorological data can be obtained. This data will be used as target meteorological data for subsequent photovoltaic project analysis and decision-making.

[0083] It should be noted that the embodiments of this disclosure may include multiple steps. For ease of description, these steps are numbered, but these numbers are not a limitation on the execution time slots or execution order between the steps; these steps can be implemented in any order, and the embodiments of this disclosure do not limit this.

[0084] Corresponding to the above-described method for assessing solar radiation, this invention also proposes an apparatus for assessing solar radiation. Since the apparatus embodiments of this invention correspond to the method embodiments described above, details not disclosed in the apparatus embodiments can be referred to in the method embodiments, and will not be repeated here.

[0085] Figure 2 is a schematic diagram of a solar radiation assessment device provided in an embodiment of this disclosure. As shown in Figure 2, it includes:

[0086] The acquisition unit 21 is used to acquire historical meteorological data of the location of the photovoltaic project and perform data cleaning on the historical meteorological data, wherein the historical meteorological data comes from multiple meteorological databases;

[0087] The fusion unit 22 is used to perform data fusion processing on the cleaned historical meteorological data based on the geographical location information of the photovoltaic project to obtain the target meteorological data of the location of the photovoltaic project.

[0088] The determining unit 23 is used to perform data analysis on the target meteorological data to determine the radiation data of the location of the photovoltaic project.

[0089] This disclosure provides an apparatus for assessing solar radiation. It acquires historical meteorological data of the photovoltaic (PV) project location and cleans the historical meteorological data, which comes from multiple meteorological databases. Based on the geographical location information of the PV project, the cleaned historical meteorological data is fused to obtain target meteorological data for the PV project location. Data analysis is then performed on the target meteorological data to determine the radiation data for the PV project location. Compared with related technologies, acquiring historical meteorological data from multiple meteorological databases maximizes the coverage of information from different sources, reducing potential biases or omissions from a single data source. Based on accurate target meteorological data, the radiation data for the PV project location can be calculated more precisely, helping to optimize design parameters such as the layout, orientation, and tilt angle of the PV project, thereby improving the power generation efficiency and economic benefits of the PV system.

[0090] Furthermore, in one possible implementation of this embodiment, as shown in FIG3, the device further includes:

[0091] The assessment unit 24 is used to assess and classify the solar energy resources at the location of the photovoltaic project based on the radiation data.

[0092] Furthermore, in one possible implementation of this embodiment, as shown in FIG3, the evaluation unit 24 includes:

[0093] The first calculation module 241 is used to calculate the annual average radiation and monthly radiation of the location of the photovoltaic project based on the radiation data.

[0094] The second calculation module 242 is used to analyze and calculate the solar radiation stability coefficient of the location of the photovoltaic project using the target meteorological data;

[0095] The first determining module 243 is used to determine the solar radiation level of the location of the photovoltaic project based on the solar radiation stability coefficient, the annual average radiation amount, and the monthly radiation amount.

[0096] Furthermore, in one possible implementation of this embodiment, as shown in FIG3, the fusion unit 22 includes:

[0097] The second determining module 221 is used to determine the nearby meteorological stations of the photovoltaic project based on the geographical location information, and to screen the nearby meteorological stations to determine the target meteorological station with similar climate conditions to the photovoltaic project.

[0098] The processing module 222 is used to call the coupling algorithm to perform coupling processing on the historical meteorological data of the target meteorological station to obtain the target meteorological data.

[0099] Furthermore, in one possible implementation of this embodiment, as shown in FIG3, the device further includes:

[0100] The search unit 25 is used to search for and obtain satellite observation data if the target meteorological station cannot be determined based on the geographical location information.

[0101] The interpolation unit 26 is used to call a preset algorithm to interpolate the observation data at the location of the photovoltaic project to obtain the target meteorological data.

[0102] It should be noted that the foregoing explanation of the method embodiments also applies to the apparatus of this embodiment, and the principle is the same, so it is not limited in this embodiment.

[0103] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0104] Figure 4 illustrates a schematic block diagram of an example electronic device 300 that can be used to implement embodiments of the present disclosure. The electronic device 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 may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0105] As shown in Figure 4, device 300 includes a computing unit 301, which can perform various appropriate actions and processes based on a computer program stored in ROM (Read-Only Memory) 302 or a computer program loaded from storage unit 308 into RAM (Random Access Memory) 303. RAM 303 can also store various programs and data required for the operation of device 300. The computing unit 301, ROM 302, and RAM 303 are interconnected via bus 304. I / O (Input / Output) interface 305 is also connected to bus 304.

[0106] Multiple components in device 300 are connected to I / O interface 305, including: input unit 306, such as keyboard, mouse, etc.; output unit 307, such as various types of monitors, speakers, etc.; storage unit 308, such as disk, optical disk, etc.; and communication unit 309, such as network card, modem, wireless transceiver, etc. Communication unit 309 allows device 300 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0107] The computing unit 301 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, CPUs (Central Processing Units), GPUs (Graphics Processing Units), various special-purpose AI (Artificial Intelligence) computing chips, various computing units running machine learning model algorithms, DSPs (Digital Signal Processors), and any suitable processor, controller, microcontroller, etc. The computing unit 301 performs the various methods and processes described above, such as methods for assessing solar radiation. For example, in some embodiments, the method for assessing solar radiation may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 308. In some embodiments, part or all of the computer program may be loaded and / or installed on device 300 via ROM 302 and / or communication unit 309. When the computer program is loaded into RAM 303 and executed by the computing unit 301, one or more steps of the methods described above may be performed. Alternatively, in other embodiments, the computing unit 301 may be configured to perform the aforementioned method of assessing solar radiation by any other suitable means (e.g., by means of firmware).

[0108] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, FPGAs (Field Programmable Gate Arrays), ASICs (Application-Specific Integrated Circuits), ASSPs (Application-Specific Standard Products), SOCs (System-on-Chips), CPLDs (Complex Programmable Logic Devices), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0109] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0110] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, RAM, ROM, EPROM (Electrically Programmable Read-Only Memory) or flash memory, optical fiber, CD-ROM (Compact Disc Read-Only Memory), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0111] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (Cathode-Ray Tube) or LCD (Liquid Crystal Display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0112] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include LANs (Local Area Networks), WANs (Wide Area Networks), the Internet, and blockchain networks.

[0113] Computer systems can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. A server can be a cloud server, also known as a cloud computing server or cloud host, a hosting product within the cloud computing service ecosystem, addressing the shortcomings of traditional physical hosts and VPS (Virtual Private Server, or simply "VPS") services, such as high management difficulty and weak business scalability. Servers can also be servers for distributed systems or servers incorporating blockchain technology.

[0114] It's important to note that artificial intelligence (AI) is the study of enabling computers to simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, and planning). It encompasses both hardware and software technologies. AI hardware technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, and big data processing. AI software technologies primarily include computer vision, speech recognition, natural language processing, machine learning / deep learning, big data processing, and knowledge graph technologies.

[0115] The various numerical designations such as "first," "second," etc., used in this disclosure are merely for ease of description and are not intended to limit the scope of the embodiments of this disclosure, nor do they indicate a sequential order.

[0116] At least one of the features described in this disclosure can also be described as one or more, and multiple features can be two, three, four or more, and this disclosure does not impose any limitations. In the embodiments of this disclosure, for a technical feature, the technical features in that technical feature are distinguished by "first", "second", "third", "A", "B", "C" and "D", etc., and there is no sequential order or size order among the technical features described by "first", "second", "third", "A", "B", "C" and "D".

[0117] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0118] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand 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 disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for assessing solar radiation, characterized in that, include: Historical meteorological data of the photovoltaic project site is obtained and the historical meteorological data is cleaned. The historical meteorological data comes from multiple meteorological databases. Based on the geographical location information of the photovoltaic project, the cleaned historical meteorological data is fused to obtain the target meteorological data for the location of the photovoltaic project. Data analysis is performed on the target meteorological data to determine the radiation data of the location of the photovoltaic project.

2. The method according to claim 1, characterized in that, The method further includes: Based on the radiation data, the solar energy resources at the location of the photovoltaic project are graded and classified.

3. The method according to claim 2, characterized in that, The step of classifying and rating the solar energy resources at the location of the photovoltaic project based on the radiation data includes: Based on the radiation data, calculate the average annual radiation and monthly radiation at the location of the photovoltaic project. Using the target meteorological data, analyze and calculate the solar radiation stability coefficient of the location of the photovoltaic project; The solar radiation level of the location of the photovoltaic project is determined based on the solar radiation stability coefficient, the annual average radiation, and the monthly radiation.

4. The method according to claim 1, characterized in that, Based on the geographical location information of the photovoltaic project, the cleaned historical meteorological data is fused to obtain the target meteorological data for the location of the photovoltaic project, including: Based on the geographical location information, nearby meteorological stations for the photovoltaic project are determined, and these nearby meteorological stations are screened to identify target meteorological stations with similar climate conditions to the photovoltaic project. The coupling algorithm is invoked to couple the historical meteorological data of the target meteorological station to obtain the target meteorological data.

5. The method according to claim 4, characterized in that, The method further includes: If the target weather station cannot be determined based on the geographical location information, then satellite observation data should be searched and obtained; A preset algorithm is invoked to interpolate the observation data at the location of the photovoltaic project to obtain the target meteorological data.

6. A device for assessing solar radiation, characterized in that, include: The acquisition unit is used to acquire historical meteorological data of the location of the photovoltaic project and perform data cleaning on the historical meteorological data, wherein the historical meteorological data comes from multiple meteorological databases; The fusion unit is used to perform data fusion processing on the cleaned historical meteorological data based on the geographical location information of the photovoltaic project to obtain the target meteorological data of the location of the photovoltaic project. The determination unit is used to perform data analysis on the target meteorological data to determine the radiation data of the location of the photovoltaic project.

7. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.

8. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-5.

9. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method according to any one of claims 1-5.

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