Method and device for calculating generating capacity of photovoltaic power station, electronic equipment and storage medium

Abstract

The invention discloses a photovoltaic power station generating capacity calculation method and device, electronic equipment and a storage medium, and relates to the field of artificial intelligence and big data, and the method comprises the steps: obtaining the geographic position data and historical power generation data of a target power station; determining a geographic area to which the target power station belongs, iteratively selecting a reference power station in the geographic area, calculating a spatial correlation weight of the reference power station, and calculating a first theoretical power generation amount of the target power station in the target time period based on the spatial correlation weight of the reference power station and the real-time power generation data; inputting the historical power generation data into the exponential weighted moving average model, and outputting a second theoretical power generation amount of the target power station in the target time period; and calculating the theoretical generating capacity of the target power station in the target time period based on the first theoretical generating capacity and the second theoretical generating capacity. According to the invention, the technical problem of low calculation result accuracy of a mode of calculating the theoretical generating capacity of the photovoltaic power station according to meteorological data in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the fields of artificial intelligence and big data, or other related technical fields. Specifically, it relates to a method and device for calculating the power generation of a photovoltaic power station, an electronic device, and a storage medium. Background Art

[0002] In today's environment of sustainable development, photovoltaic energy, as a clean and renewable power resource, is increasingly being emphasized and promoted by countries around the world. With the development of communication technology and the popularization of 5G networks, the establishment of a large number of communication base stations has put forward higher requirements for stable and reliable power supply. In this context, as one of the power supply solutions for communication base stations, the accurate calculation of the theoretical power generation of a photovoltaic power station is particularly important. The calculation of theoretical power generation can not only help base station operators conduct precise energy management and scheduling, but also play a key role in fault diagnosis and system optimization, thus ensuring the stability and efficiency of the communication network.

[0003] The accurate calculation of theoretical power generation is crucial for the operation and maintenance and energy management of a photovoltaic power station. On the one hand, it can be used as a reference for actual power generation to help analyze the operation status of the power station and ensure that it achieves the expected power generation efficiency under various weather conditions. On the other hand, the theoretical power generation data can assist in fault prediction and diagnosis. For example, when the actual power generation is much lower than the theoretical value, the operation and maintenance personnel can quickly locate the problem and check whether there is shading, equipment failure, or system abnormality, and then perform maintenance and repair in a timely manner.

[0004] In related technologies, the calculation of the theoretical power generation of a photovoltaic power station mainly relies on two methods: one is a prediction model based on real-time meteorological data, which obtains key parameters such as solar radiation, atmospheric temperature, and humidity by accessing a third-party meteorological service (such as an API interface), and then calculates the power generation in combination with the characteristics of the power station equipment; the other is based on the measurement data of local on-site meteorological instruments. By installing a special meteorological station, the meteorological conditions at the location of the photovoltaic power station are monitored in real time, and this is used as the basis for calculating the theoretical power generation.

[0005] In related technologies, when estimating the theoretical power generation of a photovoltaic power station based on meteorological condition data, the step-by-step cost is relatively high, and the dependence on weather data is relatively strong, resulting in relatively low accuracy of the calculation results.

[0006] In view of the above problems, no effective solution has been proposed yet. Summary of the Invention

[0007] Embodiments of the present invention provide a method and device for calculating the power generation of a photovoltaic power station, an electronic device, and a storage medium, so as to at least solve the technical problem of relatively low accuracy of the calculation results in the related technology when calculating the theoretical power generation of a photovoltaic power station according to meteorological data.

[0008] According to one aspect of the embodiments of the present invention, a method for calculating the power generation of a photovoltaic power station is provided, including: obtaining the geographical location data and historical power generation data of a target power station; determining the geographical region to which the target power station belongs based on the geographical location data, iteratively selecting reference power stations within the geographical region, and calculating the spatial correlation weights of the reference power stations, calculating the first theoretical power generation of the target power station within a target time period based on the spatial correlation weights of the reference power stations and real-time power generation data, wherein the first theoretical power generation is the theoretically generated power based on spatial weighting; inputting the historical power generation data into an exponentially weighted moving average model, and outputting the second theoretical power generation of the target power station within the target time period, wherein the second theoretical power generation is the theoretically generated power based on historical weighting; calculating the theoretical power generation of the target power station within the target time period based on the first theoretical power generation and the second theoretical power generation.

[0009] Further, the steps of iteratively selecting reference power stations within the geographical region, calculating the spatial correlation weights of the reference power stations, and calculating the first theoretical power generation of the target power station within a target time period based on the spatial correlation weights of the reference power stations and real-time power generation data include: Step 1, selecting reference power stations within the geographical region according to a preset time interval, and obtaining the real-time power generation data of the reference power stations, wherein the real-time power generation data at least includes: real-time power generation power; Step 2, calculating the distance value between the target power station and the reference power stations, and calculating the spatial correlation weights of the reference power stations based on the distance value; Step 3, calculating the product of the real-time power generation power and the spatial correlation weights to obtain the first theoretical power generation of the target power station within a preset time interval; repeating the above Steps 1 to 3, and accumulating the first theoretical power generation within each preset time interval to obtain the first theoretical power generation of the target power station within the target time period.

[0010] Further, the step of selecting reference power stations within the geographical region according to a preset time interval includes: for each preset time interval, obtaining the power generation power values of all adjacent power stations within the geographical region at the same moment; comparing the power generation power values of all adjacent power stations at the same moment, and selecting the adjacent power station with the largest power generation power value as the reference power station of the target power station within the preset time interval.

[0011] Further, the steps of calculating the distance value between the target power station and the reference power station and calculating the spatial correlation weight of the reference power station based on the distance value include: calculating the distance value between the target power station and the reference power station based on the latitude and longitude data of the target power station and the latitude and longitude data of the reference power station; setting a distance attenuation factor, and calculating the spatial correlation weight of the reference power station according to the distance value and the distance attenuation factor, wherein the spatial correlation weight is inversely proportional to the distance value.

[0012] Further, the steps of calculating the distance value between the target power station and the reference power station based on the latitude and longitude data of the target power station and the latitude and longitude data of the reference power station include: extracting the latitude value of the target power station and the latitude value of the reference power station based on the latitude and longitude data of the target power station and the latitude and longitude data of the reference power station; calculating the longitude difference and latitude difference between the target power station and the reference power station based on the latitude and longitude data of the target power station and the latitude and longitude data of the reference power station; substituting the latitude value of the target power station, the latitude value of the reference power station, the longitude difference and latitude difference between the target power station and the reference power station into a distance calculation formula, and calculating the distance value between the target power station and the reference power station.

[0013] Further, the steps of inputting the historical power generation data into an exponentially weighted moving average model and outputting the second theoretical power generation of the target power station in a target time period include: the exponentially weighted moving average model divides the historical power generation data based on the timestamps of the historical power generation data to obtain historical power generation data in multiple historical time periods; the exponentially weighted moving average model configures historical correlation weights for the historical power generation data in each historical time period based on a preset smoothing coefficient; the exponentially weighted moving average model performs exponential weighting calculation on the historical power generation data in each historical time period based on the historical correlation weights to obtain the second theoretical power generation in the target time period, and takes the second theoretical power generation as output data.

[0014] Further, the steps of determining the geographical region to which the target power station belongs based on the geographical location data include: setting a geographical region delineation range, and determining the position coordinates of the target power station based on the geographical location data; taking the position coordinates of the target power station as the regional center, and determining the geographical region to which the target power station belongs based on the geographical region delineation range, wherein the geographical region includes M adjacent power stations related to the target power station, and M is a positive integer.

[0015] According to another aspect of the embodiments of the present invention, there is also provided a calculation device for the power generation amount of a photovoltaic power station, including: an acquisition unit for acquiring the geographical location data and historical power generation data of a target power station; a selection unit for determining the geographical region to which the target power station belongs based on the geographical location data, iteratively selecting reference power stations within the geographical region, calculating the spatial correlation weights of the reference power stations, and calculating the first theoretical power generation amount of the target power station within a target time period based on the spatial correlation weights of the reference power stations and real-time power generation data, wherein the first theoretical power generation amount is the theoretically generated power based on spatial weighting; an output unit for inputting the historical power generation data into an exponentially weighted moving average model and outputting the second theoretical power generation amount of the target power station within the target time period, wherein the second theoretical power generation amount is the theoretically generated power based on historical weighting; and a calculation unit for calculating the theoretical power generation amount of the target power station within the target time period based on the first theoretical power generation amount and the second theoretical power generation amount.

[0016] Further, the selection unit includes: a first selection subunit for, in step one, selecting reference power stations within the geographical region according to a preset time interval and acquiring the real-time power generation data of the reference power stations, wherein the real-time power generation data at least includes: real-time power generation power; a first calculation subunit for, in step two, calculating the distance value between the target power station and the reference power station and calculating the spatial correlation weight of the reference power station based on the distance value; a second calculation subunit for, in step three, calculating the product of the real-time power generation power and the spatial correlation weight to obtain the first theoretical power generation amount of the target power station within a preset time interval; and a first repetition subunit for repeating the above steps one to three and accumulating the first theoretical power generation amounts within each preset time interval to obtain the first theoretical power generation amount of the target power station within the target time period.

[0017] Further, the first selection subunit includes: a first acquisition module for, for each preset time interval, acquiring the power generation power values of all adjacent power stations within the geographical region at the same moment; and a first selection module for comparing the power generation power values of all adjacent power stations at the same moment and selecting the adjacent power station with the largest power generation power value as the reference power station of the target power station within the preset time interval.

[0018] Further, the first calculation subunit includes: a first calculation module for calculating the distance value between the target power station and the reference power station based on the longitude and latitude data of the target power station and the longitude and latitude data of the reference power station; and a second calculation module for setting a distance attenuation factor and calculating the spatial correlation weight of the reference power station according to the distance value and the distance attenuation factor, wherein the spatial correlation weight is inversely proportional to the distance value.

[0019] Further, the first calculation module includes: a first extraction sub-module, configured to extract the latitude value of the target power station and the latitude value of the reference power station based on the longitude and latitude data of the target power station and the longitude and latitude data of the reference power station; a first calculation sub-module, configured to calculate the longitude difference and latitude difference between the target power station and the reference power station based on the longitude and latitude data of the target power station and the longitude and latitude data of the reference power station; a second calculation sub-module, configured to substitute the latitude value of the target power station, the latitude value of the reference power station, the longitude difference and latitude difference between the target power station and the reference power station into a distance calculation formula to calculate the distance value between the target power station and the reference power station.

[0020] Further, the output unit includes: a first division sub-unit, configured to divide the historical power generation data by the exponential weighted moving average model based on the timestamps of the historical power generation data to obtain the historical power generation data within multiple historical periods; a first configuration sub-unit, configured to configure historical correlation weights for the historical power generation data within each historical period by the exponential weighted moving average model based on a preset smoothing coefficient; a third calculation sub-unit, configured to perform exponential weighted calculation on the historical power generation data within each historical period by the exponential weighted moving average model based on the historical correlation weights to obtain the second theoretical power generation amount within the target time period, and use the second theoretical power generation amount as output data.

[0021] Further, the selection unit further includes: a first setting sub-unit, configured to set the geographical area delineation range and determine the position coordinates of the target power station based on the geographical position data; a first determination sub-unit, configured to use the position coordinates of the target power station as the regional center and determine the geographical area to which the target power station belongs based on the geographical area delineation range, where the geographical area includes M adjacent power stations related to the target power station, and M is a positive integer.

[0022] According to another aspect of the embodiments of the present invention, there is also provided a computer-readable storage medium, where the computer-readable storage medium includes a stored computer program, and when the computer program runs, it controls the device where the computer-readable storage medium is located to execute any one of the above photovoltaic power station power generation calculation methods.

[0023] According to another aspect of the embodiments of the present invention, there is also provided an electronic device, including one or more processors and a memory, where the memory is used to store one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement any one of the above photovoltaic power station power generation calculation methods.

[0024] In this application, through the following steps: obtaining the geographical location data and historical power generation data of the target power station, determining the geographical region to which the target power station belongs based on the geographical location data, iteratively selecting reference power stations within the geographical region, calculating the spatial correlation weights of the reference power stations, calculating the first theoretical power generation amount of the target power station within the target time period based on the spatial correlation weights of the reference power stations and the real-time power generation data, where the first theoretical power generation amount is the theoretically generated power based on spatial weighting, then inputting the historical power generation data into an exponentially weighted moving average model to output the second theoretical power generation amount of the target power station within the target time period, where the second theoretical power generation amount is the theoretically generated power based on historical weighting, and finally calculating the theoretical power generation amount of the target power station within the target time period based on the first theoretical power generation amount and the second theoretical power generation amount.

[0025] In this application, a representative reference power station is selected through a dynamic optimization strategy, and the theoretical power generation amount is calculated based on the relative spatial positions of the reference power station and the target power station and the historical power generation trend information of the target power station, making full use of the spatial correlation between power stations and the historical power generation trend, providing a more accurate, economical and applicable solution for calculating the theoretical power generation amount of a photovoltaic power station, achieving the technical effect of improving the accuracy of the calculation result of the photovoltaic power station, and further solving the technical problem of the low accuracy of the calculation result in the related technology when calculating the theoretical power generation amount of a photovoltaic power station according to meteorological data. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The drawings described herein are used to provide a further understanding of the present invention, form a part of this application, and the schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation to the present invention. In the drawings:

[0027] Figure 1 The hardware structure block diagram of a computer terminal (or mobile device) for implementing a method for calculating the power generation amount of a photovoltaic power station is shown;

[0028] Figure 2 is a flowchart of an optional method for calculating the power generation amount of a photovoltaic power station according to an embodiment of the present invention;

[0029] Figure 3 is a schematic diagram of an optional calculation process for the theoretical power generation amount of a photovoltaic power station according to an embodiment of the present invention;

[0030] Figure 4 is a schematic diagram of an optional calculation device for the power generation amount of a photovoltaic power station according to an embodiment of the present invention;

[0031] Figure 5 is a hardware structure block diagram of an optional electronic device (or mobile device) for executing a method for calculating the power generation amount of a photovoltaic power station according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0032] To enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0033] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described here can be implemented in an order different from those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0034] It should be noted that the method and device for calculating the power generation of a photovoltaic power station in this application can be used in the field of artificial intelligence or big data when calculating the theoretical power generation of a photovoltaic power station based on spatial weighting and historical data weighting, and can also be used in any field other than the field of artificial intelligence and big data when calculating the theoretical power generation of a photovoltaic power station based on spatial weighting and historical data weighting. The application field of the method and device for calculating the power generation of a photovoltaic power station in this application is not limited.

[0035] It should be noted that the information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for display, data for analysis, etc.) collected in this application are information and data authorized by the user or fully authorized by all parties. And the processing of relevant data, such as collection, storage, use, processing, transmission, provision, disclosure and application, etc., all comply with relevant laws, regulations and standards, take necessary confidentiality measures, do not violate public order and good customs, and provide corresponding operation entrances for users to choose to authorize or refuse. For example, an interface is set between this system and relevant users or institutions to provide corresponding operation entrances for users to choose to agree or refuse the automated decision-making results; if the user chooses to refuse, the expert decision-making process will be entered.

[0036] The following embodiments of the present invention can be applied to various calculation systems / applications / devices for the theoretical power generation of photovoltaic power stations. The present invention does not rely on single meteorological data, but dynamically adjusts and derives the theoretical power generation according to the historical power generation data of the power station and the method of spatial weighting. This method avoids the timeliness and accuracy problems that may be brought by weather data, and can still ensure accurate power generation assessment especially when the meteorological data is lagging or inaccurate.

[0037] The present invention will be described in detail below in conjunction with each embodiment.

[0038] Embodiment 1

[0039] According to an embodiment of the present invention, an embodiment of a method for calculating the power generation of a photovoltaic power station is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order than here.

[0040] The method embodiment provided by the first embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 A hardware structure block diagram of a computer terminal (or mobile device) for implementing the method for calculating the power generation of a photovoltaic power station is shown. As Figure 1 shown, the computer terminal 10 (or mobile device) may include one or more (shown as 102a, 102b,..., 102n in the figure) processors 102 (the processor 102 may include, but is not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may further include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which can be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. Those of ordinary skill in the art can understand that Figure 1 the structure shown is only schematic and does not limit the structure of the above-mentioned electronic device. For example, the computer terminal 10 may further include more or fewer components than Figure 1 shown, or have a different configuration from Figure 1 shown.

[0041] It should be noted that one or more of the above-mentioned processors 102 and / or other data processing circuits can generally be referred to as "data processing circuits" herein. The data processing circuit can be embodied in software, hardware, firmware, or any combination thereof, in whole or in part. In addition, the data processing circuit can be a single independent processing module, or be incorporated in whole or in part into any one of other elements in the computer terminal 10 (or mobile device). As involved in the embodiments of the present application, the data processing circuit is a kind of processor control (such as the selection of a variable resistor terminal path connected to an interface).

[0042] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage devices corresponding to the calculation method of the power generation amount of the photovoltaic power station in the embodiments of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, realizes the above-mentioned calculation method of the power generation amount of the photovoltaic power station. The memory 104 can include a high-speed random access memory, and can also include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some instances, the memory 104 can further include a memory remotely set relative to the processor 102, and these remote memories can be connected to the computer terminal 10 through a network. Examples of the above-mentioned network include but are not limited to the Internet, intranet, local area network, mobile communication network, and combinations thereof.

[0043] The transmission device 106 is used to receive or send data via a network. Specific examples of the above-mentioned network can include a wireless network provided by the communication provider of the computer terminal 10. In one instance, the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station and thus can communicate with the Internet. In one instance, the transmission device 106 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0044] The display can be, for example, a touch-screen liquid crystal display (LCD), which enables a user to interact with the user interface of the computer terminal 10 (or mobile device).

[0045] Under the above operating environment, the present application provides a Figure 2 calculation method of the power generation amount of the photovoltaic power station as shown, and the implementation subject of this method is the calculation system of the power generation amount of the photovoltaic power station.

[0046] Figure 2 is a flowchart of an optional calculation method of the power generation amount of the photovoltaic power station according to an embodiment of the present invention, as Figure 2As shown, the method includes the following steps:

[0047] Step S201, obtain the geographical location data and historical power generation data of the target power station.

[0048] It should be noted that calculating the theoretical power generation of a photovoltaic power station is crucial for optimizing the operation and maintenance management, fault detection and diagnosis, and energy scheduling of the photovoltaic power station. The theoretical power generation reflects the power generation level of the photovoltaic power station under ideal conditions. Based on this value, the operation and maintenance team can evaluate the actual power generation efficiency of the power station and determine whether the expected power generation performance has been achieved. By comparing the theoretical power generation with the actual power generation, the degree of performance deviation of the photovoltaic power station can be quantified, providing a data basis for system optimization. The calculation of theoretical power generation can be used as part of the health monitoring of the photovoltaic power station. By continuously monitoring the gap between the theoretical and actual power generation, system anomalies such as equipment failures, poor connections, and system configuration problems can be detected early, so as to intervene in a timely manner, prevent the occurrence of faults, and reduce downtime and maintenance costs.

[0049] In the embodiment of the present invention, the spatial correlation and historical data trend are combined to improve the accuracy and stability of the calculation of the theoretical power generation of the photovoltaic power station. In the above step S201, first, it is necessary to obtain the geographical location data and historical power generation data of the target power station to be calculated. The geographical location data can be obtained through an accurate global positioning system or other geolocation technologies. The geographical location data includes at least the longitude and latitude coordinates of the target power station for subsequent spatial division and weighted calculation. The historical power generation data covers the actual power generation and power generation power of the target power station at a series of historical time points for subsequent weighted calculation based on the historical power generation trend to estimate the final theoretical power generation.

[0050] Step S202, determine the geographical region to which the target power station belongs based on the geographical location data, iteratively select reference power stations within the geographical region, calculate the spatial correlation weights of the reference power stations, and calculate the first theoretical power generation of the target power station within the target time period based on the spatial correlation weights of the reference power stations and the real-time power generation data.

[0051] In the above step S202, by dynamically selecting the reference power station with the best performance among adjacent power stations and performing spatial weighting for the target power station based on the reference power station, the correlation with adjacent power stations under the same conditions within the spatial region can be fully utilized to calculate the theoretical power generation. Specifically, first, the specific geographical area to which the target power station belongs is determined based on its accurate longitude and latitude coordinates. This area division can be refined to cities, counties, or specific geographical grids, aiming to delimit a reasonable range to facilitate the search for reference power stations that are spatially adjacent or have similar climate conditions. After defining the geographical area of the target power station, several reference power stations are dynamically and iteratively selected within this area. The criterion for selecting reference power stations is based on their real-time power generation data. The system preferentially selects the power station with the best power generation capacity as the reference, which reflects the maximum power generation potential of photovoltaic power stations under the same conditions. By iteratively selecting multiple reference power stations and performing spatial weighting calculations for the target power station based on the distance correlation of the reference power stations, the first theoretical power generation is obtained, which is the theoretical power generation based on spatial weighting.

[0052] It should be noted that the selection of reference power stations is repeated at each preset time interval, which means that the selection of reference power stations is dynamic and will change according to the changes in real-time power generation data. This method ensures that the reference power station can always reflect the current optimal power generation performance. Even when the power generation capacity of the power station fluctuates or the weather conditions change, the reference power station can be updated in a timely manner to improve the accuracy and reliability of power generation prediction.

[0053] Furthermore, the steps for determining the geographical area to which the target power station belongs based on geographical location data include: setting the scope of geographical area delimitation and determining the position coordinates of the target power station based on geographical location data; using the position coordinates of the target power station as the regional center and determining the geographical area to which the target power station belongs based on the scope of geographical area delimitation, where the geographical area includes M adjacent power stations related to the target power station, and M is a positive integer.

[0054] Specifically, when dividing the area of the target power station, first, the size of the scope of geographical area delimitation needs to be clarified, which is usually preset by the system and determined based on the distribution characteristics of power stations to screen out power stations that are geographically close and have similar climate conditions. The setting of the scope is directly related to the geographical coverage of the subsequent selected reference power stations and the contribution degree of these power stations to the power generation estimation of the target power station, providing a more accurate reference for the calculation of theoretical power generation. For example, if the power stations within the area are densely distributed and the geographical environment changes little, the delimitation scope can be relatively small; on the contrary, if the power stations are sparsely distributed or the geographical environment varies greatly, a larger scope needs to be set to ensure that sufficient diverse power station data is covered. Through this step, a geographically coherent and representative reference framework can be constructed, providing a stable geographical basis for subsequent calculations.

[0055] Subsequently, based on the specific geographic location data provided by the target power station, its latitude and longitude coordinates are obtained and used as the center point for the regional division. Using the latitude and longitude coordinates of the target power station as the center of the geographic region, the system selects multiple adjacent power stations related to the target power station based on the preset demarcation range. This process not only takes into account the geographical distance between power stations, but also may make weighted selections based on factors such as the power station's historical power generation data and similarity of meteorological conditions to ensure that the selected power station can most accurately reflect the average power generation potential of the target area. By connecting the target power station with M neighboring power stations, a dynamic power generation estimation model that covers regional characteristics can be constructed, improving the accuracy and reliability of the prediction.

[0056] Furthermore, a reference power station is iteratively selected within a geographical area, and the spatial correlation weight of the reference power station is calculated. The step of calculating the first theoretical power generation of the target power station within a target time period based on the spatial correlation weight of the reference power station and the real-time power generation data includes: step one, selecting a reference power station within a geographical area according to a preset time interval, and obtaining the real-time power generation data of the reference power station, wherein the real-time power generation data at least includes: real-time power generation power; step two, calculating the distance value between the target power station and the reference power station, and calculating the spatial correlation weight of the reference power station based on the distance value; step three, calculating the product of the real-time power generation power and the spatial correlation weight to obtain the first theoretical power generation of the target power station within a preset time interval; repeating the above steps one to three, accumulating the first theoretical power generation within each preset time interval, and obtaining the first theoretical power generation of the target power station within the target time period.

[0057] Specifically, within a preset geographical area, the system will dynamically select the photovoltaic power station with the best power generation capacity in the geographical area as the reference power station at fixed time intervals (for example, every 15 minutes). The selection of this time interval is based on the frequency of power station data updates and the fluctuation characteristics of power station power generation, aiming to ensure that the selected reference power station can provide the latest and representative real-time power generation data. Real-time power generation data includes at least real-time power generation, which directly reflects the power generation capacity of the power station at the current moment and is the key data for selecting the reference power station. Through this step, the system can capture the real-time changes in the optimal power generation of the power station in the environment where the target power station is located, providing the most realistic data basis for subsequent theoretical power generation calculations.

[0058] The actual spatial separation between the target power station and each selected reference power station is quantified by calculating the geospatial distance between them. The distance value is calculated based on the longitude and latitude coordinates of the two power stations and is accurately calculated using the distance value calculation formula. Subsequently, based on the calculated distance value, the spatial correlation weight corresponding to each reference power station is determined. The calculation of this weight uses a distance decay factor to ensure that the correlation weight value corresponding to the reference power station closer to the target power station is larger, and conversely, the correlation weight value corresponding to the reference power station farther from the target power station is smaller.

[0059] Finally, the theoretical power generation of the target power station within the preset time period is calculated based on the product of the real-time power generation of the reference power station and the corresponding spatial correlation weight, and the total first theoretical power generation within the target time period is obtained based on the theoretical power generation calculated from multiple selected reference power stations.

[0060] In the embodiment of the present invention, by dynamically selecting reference power stations and based on the real-time power generation data and spatial correlation weights of the reference power stations, the first theoretical power generation of the target power station within the target time period is accurately calculated. It avoids the dependence on accurate weather data, reduces system costs, and at the same time improves the accuracy of theoretical power generation calculation. It is applicable to the scenario of a photovoltaic power station cluster, can make full use of the spatial correlation between power stations, and can provide stable and reliable power generation predictions even in the absence of real-time weather information, providing strong support for the operation and maintenance management and fault diagnosis of photovoltaic power stations. In addition, through continuous iterative calculation, this method can reflect the changes in the power generation trend of the power station in real time, further optimize the power generation prediction model, and improve the adaptability and flexibility of the prediction.

[0061] Further, the step of selecting reference power stations in the geographical area according to a preset time interval includes: for each preset time interval, obtaining the power generation values of all adjacent power stations in the geographical area at the same moment; comparing the power generation values of all adjacent power stations at the same moment, and selecting the adjacent power station with the largest power generation value as the reference power station of the target power station within this preset time interval.

[0062] Specifically, when selecting reference power stations, for each preset time interval (for example, every half hour, every hour, etc.), the system automatically collects the power generation values of all adjacent power stations in the geographical area at this time point. The power generation value reflects the actual power generation capacity of each power station at a specific moment and is an important indicator for evaluating the power generation performance of the power station. The purpose of collecting this data is to ensure that the selected reference power station can represent the real-time state of the maximum power generation potential in the region. After obtaining the power generation values of all adjacent power stations at the same moment, a comparative analysis is carried out. Through comparison, the system identifies the adjacent power station with the largest power generation value within this time interval, that is, the power station with the highest power generation efficiency at this moment, and uses it as the reference power station of the target power station.

[0063] Further, the steps of calculating the distance value between the target power station and the reference power station and calculating the spatial correlation weight of the reference power station based on the distance value include: calculating the distance value between the target power station and the reference power station based on the latitude and longitude data of the target power station and the reference power station; setting a distance attenuation factor, and calculating the spatial correlation weight of the reference power station according to the distance value and the distance attenuation factor, wherein the spatial correlation weight is inversely proportional to the distance value.

[0064] Specifically, the distance value between the reference power station and the target power station determines the magnitude of spatial weighting for the target power station. The distance value is calculated based on the latitude and longitude data of the target power station and the reference power station. The result of the distance calculation reflects the proximity of the two power stations in space and is the basis for subsequent weight calculation. Set a distance attenuation factor, which is used to control the influence degree of the distance on the spatial correlation weight. Based on the calculated distance value and the set distance attenuation factor, use a specific formula to calculate the spatial correlation weight of the reference power station. The calculated spatial correlation weight is inversely proportional to the distance value between the power stations.

[0065] The spatial weighting calculation formula can be expressed as follows: where ω ij is the spatial weight of power station i for power station j, d ij is the distance between power station i and power station j, and α is the distance attenuation factor with a value of 2, which is used to control the influence of the distance on the weighting.

[0066] Further, the steps of calculating the distance value between the target power station and the reference power station based on the latitude and longitude data of the target power station and the reference power station include: extracting the latitude value of the target power station and the latitude value of the reference power station based on the latitude and longitude data of the target power station and the reference power station; calculating the longitude difference and latitude difference between the target power station and the reference power station based on the latitude and longitude data of the target power station and the reference power station; substituting the latitude value of the target power station, the latitude value of the reference power station, the longitude difference and the latitude difference between the target power station and the reference power station into the distance calculation formula to calculate the distance value between the target power station and the reference power station.

[0067] Specifically, when calculating the distance value, extract the latitude values from the latitude and longitude data of the target power station and the reference power station respectively. At the same time, based on the latitude and longitude data of the target power station and the reference power station, calculate the longitude difference and latitude difference between the target power station and the reference power station. Finally, substitute the latitude values of the target power station and the reference power station and the longitude difference and latitude difference between the two into the distance calculation formula to quantify the spatial distance between the two stations.

[0068] In the embodiment of the present invention, the distance calculation formula is expressed as:

[0069] Among them, d is the distance value between power stations, and r is the radius of the earth. are the latitude value of the target power station and the latitude value of the reference power station respectively. is the latitude difference between the target power station and the reference power station, and Δλ is the longitude difference between the target power station and the reference power station.

[0070] Step S203: Input the historical power generation data into the exponentially weighted moving average model, and output the second theoretical power generation of the target power station within the target time period.

[0071] It should be noted that the collection of historical power generation data is to capture the trends and laws of the power generation capacity of power stations changing over time, providing a basis for predicting the theoretical power generation. The historical power generation data is weighted. The weighted historical power generation data can better reflect the changes in the power generation capacity of power stations, especially the power generation characteristics under different climate or load conditions. Input the historical power generation data into the exponentially weighted moving average model. The exponentially weighted moving average model can smooth the historical data series by assigning greater weights to recent data and smaller weights to distant data, so as to eliminate the influence of data fluctuations and obtain a more stable power generation prediction value reflecting the recent trend, and then obtain the second theoretical power generation of the target power station within the target time period. The second theoretical power generation is the theoretical power generation based on historical weighting, that is, the theoretical power generation predicted based on historical trends.

[0072] Furthermore, the steps of inputting the historical power generation data into the exponentially weighted moving average model and outputting the second theoretical power generation of the target power station within the target time period include: The exponentially weighted moving average model divides the historical power generation data based on the timestamps of the historical power generation data to obtain the historical power generation data within multiple historical time periods; The exponentially weighted moving average model configures historical correlation weights for the historical power generation data within each historical time period based on a preset smoothing coefficient; The exponentially weighted moving average model performs exponential weighting calculations on the historical power generation data within each historical time period based on the historical correlation weights to obtain the second theoretical power generation within the target time period, and uses the second theoretical power generation as the output data.

[0073] Specifically, the exponentially weighted moving average model first divides the data based on the timestamps of historical power generation data, dividing the entire historical dataset into multiple historical periods. The data within each period represents the power generation performance within a specific time window. The purpose of this time division is to be able to process and analyze different historical periods separately, enabling the model to assign specific weight values to the historical power generation data of different periods. Next, the exponentially weighted moving average model configures historical correlation weights for the historical power generation data within each historical period based on a pre-set smoothing coefficient. The smoothing coefficient determines the relative importance of recent data and long-term data in the model. The principle of weight configuration is that recent data is given a higher weight, while the weight of long-term data gradually decreases, and different weights are assigned to the data within each historical period in an exponentially decaying manner. This step ensures that the model can pay more attention to recent power generation data during prediction, while not completely ignoring the long-term trend of historical data, thus providing a balanced and accurate prediction result. Finally, the model performs an exponentially weighted calculation on the historical power generation data within each historical period using the configured historical correlation weights to obtain the second theoretical power generation for the target time period. This theoretical power generation is predicted based on the historical data trend. By weighted averaging the data from different time periods, short-term abnormal fluctuations are eliminated, resulting in a more stable prediction value closer to the actual power generation trend. The second theoretical power generation is then used as the output data of the model to guide the evaluation of the power generation performance of the target power station within a specific time period.

[0074] Step S204: Calculate the theoretical power generation of the target power station within the target time period based on the first theoretical power generation and the second theoretical power generation.

[0075] It should be noted that both the first theoretical power generation and the second theoretical power generation are obtained through calculations. The first theoretical power generation and the second theoretical power generation are weighted and added together to obtain the theoretical power generation of the target power station within the target time period. This theoretical power generation takes into account the best power generation performance within the spatial region and the historical power generation trend, improving the accuracy of the calculated result of the theoretical power generation.

[0076] Through the above steps, obtain the geographical location data and historical power generation data of the target power station, determine the geographical region to which the target power station belongs based on the geographical location data, iteratively select reference power stations within the geographical region, calculate the spatial correlation weights of the reference power stations, and calculate the first theoretical power generation of the target power station within the target time period based on the spatial correlation weights of the reference power stations and the real-time power generation data. The first theoretical power generation is the theoretically generated power based on spatial weighting. Then, input the historical power generation data into the exponentially weighted moving average model to output the second theoretical power generation of the target power station within the target time period. The second theoretical power generation is the theoretically generated power based on historical weighting. Finally, calculate the theoretical power generation of the target power station within the target time period based on the first theoretical power generation and the second theoretical power generation.

[0077] In this embodiment, representative reference power stations are selected through a dynamic optimization strategy, and the theoretical power generation is calculated based on the relative spatial positions of the reference power stations and the target power station and the historical power generation trend information of the target power station, making full use of the spatial correlation between power stations and the historical power generation trend, providing a more accurate, economical and applicable solution for calculating the theoretical power generation of photovoltaic power stations, achieving the technical effect of improving the accuracy of the calculation results of photovoltaic power stations, and thus solving the technical problem of low accuracy of the calculation results in the related art when calculating the theoretical power generation of photovoltaic power stations according to meteorological data.

[0078] The following will be described in detail in combination with another optional specific implementation manner.

[0079] Figure 3 is a schematic diagram of an optional calculation process of the theoretical power generation of a photovoltaic power station according to an embodiment of the present invention. As Figure 3 shown, when calculating the theoretical power generation of the target power station, the specific process includes:

[0080] Step 1, start;

[0081] Step 2, calculate the theoretical power generation based on spatial weighting;

[0082] When calculating the theoretical power generation based on spatial weighting, the area where the power station is located can be divided by province, city, district or geographical grid. Within the same area, the spatial distance between power stations is calculated using longitude and latitude, and weighted according to this distance. The spatial weighting calculation uses a distance formula to quantify the actual distance between power stations and converts the calculation result into a weight coefficient. Specifically, the closer the spatial distance between two power stations, the higher the weight assigned to it, and vice versa, the lower the weight.

[0083] After dividing the target power station into regions, taking the target power station as the center, find all adjacent power stations within a preset range, and dynamically find the power station with the optimal power generation capacity as the reference power station within a preset time interval. Calculate the spatial distance between power stations, and perform weighting based on the spatial distance to obtain the weight value corresponding to the reference power station. Finally, calculate the theoretically generated power after spatial weighting based on the weight value corresponding to the reference power station and its real-time power generation data (including power generation power).

[0084] The formula for calculating the distance value between the target power station and the reference power station is expressed as:

[0085]

[0086] where d is the distance value between power stations, r is the radius of the earth, are the latitude values of the target power station and the reference power station respectively, is the latitude difference between the target power station and the reference power station, and Δλ is the longitude difference between the target power station and the reference power station.

[0087] Spatial weighting can be calculated through the following expression:

[0088]

[0089] where ω ij is the spatial weight of power station i to power station j, d ij is the distance between power station i and power station j, and α is the distance attenuation factor, with a value of 2, used to control the influence of distance on weighting.

[0090] Step 3: Calculate the theoretically generated power based on historical weighting;

[0091] When calculating the theoretically generated power based on historical weighting, collect the historical power generation data of the target power station, and input the historical power generation data into the exponentially weighted moving average model. Configure the weight value for the data of each historical period through the exponentially weighted moving average model, and perform calculations based on the historical power generation data to output the theoretically generated power after historical weighting.

[0092] By performing weighted processing on the historical power generation data through the exponentially weighted moving average model, the most recent power generation data has a greater impact on the calculation result, while the older historical data has a smaller impact. The weighted historical power generation data can better reflect the change in the power generation capacity of the power station, especially the power generation characteristics under different climate or load conditions.

[0093] Step 4: Combine the two parts of weighted generated power;

[0094] Step 5: Calculate the theoretically generated power of the photovoltaic power station;

[0095] P final =α·Pspace + β·P history ;

[0096] wherein, P final is the final theoretical power generation, P space is the power generation after spatial weighting, and P history is the power generation after historical weighting. α and β are weighting coefficients, which usually satisfy α + β = 1 and can be set and adjusted according to actual situations.

[0097] Step six, end.

[0098] The embodiment of the present invention does not rely on single meteorological data, but dynamically adjusts and derives the theoretical power generation according to the historical power generation data of the power station and the method of spatial weighting. This method avoids the timeliness and accuracy problems that may be brought by weather data. Especially in the case of lagging or inaccurate meteorological data, it can still ensure accurate power generation assessment.

[0099] The following will be described in detail with another embodiment.

[0100] Embodiment 2

[0101] A calculation device for the power generation of a photovoltaic power station provided in this embodiment includes multiple implementation units. Each implementation unit corresponds to each implementation step in Embodiment 1 above. The specific implementation manners and beneficial effects can refer to the foregoing method embodiment and will not be elaborated here.

[0102] Figure 4 is a schematic diagram of an optional calculation device for the power generation of a photovoltaic power station according to an embodiment of the present invention. As Figure 4 shown, the calculation device for the power generation of the photovoltaic power station may include: an acquisition unit 41, a selection unit 42, an output unit 43, and a calculation unit 44, wherein,

[0103] The acquisition unit 41 is configured to acquire the geographical location data and historical power generation data of the target power station;

[0104] The selection unit 42 is configured to determine the geographical area to which the target power station belongs based on the geographical location data, iteratively select reference power stations within the geographical area, calculate the spatial correlation weights of the reference power stations, and calculate the first theoretical power generation of the target power station within the target time period based on the spatial correlation weights of the reference power stations and the real-time power generation data. The first theoretical power generation is the theoretical power generation based on spatial weighting;

[0105] The output unit 43 is configured to input the historical power generation data into the exponentially weighted moving average model and output the second theoretical power generation of the target power station within the target time period. The second theoretical power generation is the theoretical power generation based on historical weighting;

[0106] A calculation unit 44 for calculating the theoretical power generation of the target power station within the target time period based on the first theoretical power generation amount and the second theoretical power generation amount.

[0107] The above-mentioned calculation device for the power generation amount of the photovoltaic power station obtains the geographical location data and historical power generation data of the target power station through the acquisition unit 41; determines the geographical area to which the target power station belongs based on the geographical location data through the selection unit 42, iteratively selects reference power stations within the geographical area, calculates the spatial correlation weights of the reference power stations, and calculates the first theoretical power generation amount of the target power station within the target time period based on the spatial correlation weights of the reference power stations and the real-time power generation data, where the first theoretical power generation amount is the theoretically generated power based on spatial weighting; inputs the historical power generation data into the exponentially weighted moving average model through the output unit 43 and outputs the second theoretical power generation amount of the target power station within the target time period, where the second theoretical power generation amount is the theoretically generated power based on historical weighting; calculates the theoretical power generation amount of the target power station within the target time period based on the first theoretical power generation amount and the second theoretical power generation amount through the calculation unit 44.

[0108] In this embodiment, representative reference power stations are selected through a dynamic optimization strategy, and the theoretical power generation amount is calculated based on the relative spatial positions of the reference power stations and the target power station and the historical power generation trend information of the target power station, making full use of the spatial correlation between power stations and the historical power generation trend, providing a more accurate, economical and applicable solution for calculating the theoretical power generation amount of photovoltaic power stations, achieving the technical effect of improving the accuracy of the calculation results of photovoltaic power stations, and thus solving the technical problem of low accuracy of the calculation results in the related art when calculating the theoretical power generation amount of photovoltaic power stations according to meteorological data.

[0109] Further, the selection unit includes: a first selection subunit for, in step one, selecting reference power stations within the geographical area at a preset time interval and obtaining the real-time power generation data of the reference power stations, where the real-time power generation data at least includes: real-time power generation power; a first calculation subunit for, in step two, calculating the distance value between the target power station and the reference power stations and calculating the spatial correlation weights of the reference power stations based on the distance value; a second calculation subunit for, in step three, calculating the product of the real-time power generation power and the spatial correlation weights to obtain the first theoretical power generation amount of the target power station within a preset time interval; a first repetition subunit for repeating the above steps one to three to accumulate the first theoretical power generation amounts within each preset time interval to obtain the first theoretical power generation amount of the target power station within the target time period.

[0110] Further, the first selection subunit includes: a first acquisition module, configured to acquire, for each preset time interval, the power generation power values of all adjacent power stations in the geographical area at the same moment; a first selection module, configured to compare the power generation power values of all adjacent power stations at the same moment, and select the adjacent power station with the largest power generation power value as the reference power station of the target power station within the preset time interval.

[0111] Further, the first calculation subunit includes: a first calculation module, configured to calculate the distance value between the target power station and the reference power station based on the longitude and latitude data of the target power station and the longitude and latitude data of the reference power station; a second calculation module, configured to set a distance attenuation factor, and calculate the spatial correlation weight of the reference power station according to the distance value and the distance attenuation factor, wherein the spatial correlation weight is inversely proportional to the distance value.

[0112] Further, the first calculation module includes: a first extraction sub-module, configured to extract the latitude value of the target power station and the latitude value of the reference power station based on the longitude and latitude data of the target power station and the longitude and latitude data of the reference power station; a first calculation sub-module, configured to calculate the longitude difference and the latitude difference between the target power station and the reference power station based on the longitude and latitude data of the target power station and the longitude and latitude data of the reference power station; a second calculation sub-module, configured to substitute the latitude value of the target power station, the latitude value of the reference power station, the longitude difference and the latitude difference between the target power station and the reference power station into a distance calculation formula to calculate the distance value between the target power station and the reference power station.

[0113] Further, the output unit includes: a first division subunit, configured to divide the historical power generation data by the exponentially weighted moving average model based on the timestamps of the historical power generation data to obtain the historical power generation data within multiple historical periods; a first configuration subunit, configured to configure the historical correlation weight for the historical power generation data within each historical period by the exponentially weighted moving average model based on a preset smoothing coefficient; a third calculation subunit, configured to perform exponential weighting calculation on the historical power generation data within each historical period by the exponentially weighted moving average model based on the historical correlation weight to obtain the second theoretical power generation amount within the target time period, and use the second theoretical power generation amount as output data.

[0114] Further, the selection unit further includes: a first setting subunit, configured to set the defined range of the geographical area, and determine the position coordinates of the target power station based on the geographical location data; a first determination subunit, configured to use the position coordinates of the target power station as the regional center, and determine the geographical area to which the target power station belongs based on the defined range of the geographical area, wherein the geographical area includes M adjacent power stations related to the target power station, and M is a positive integer.

[0115] It should be noted here that the above-mentioned acquisition unit 41, selection unit 42, output unit 43, and calculation unit 44 correspond to steps S201 to S204 in the first embodiment. The examples and application scenarios implemented by the above units and the corresponding steps are the same, but are not limited to the content disclosed in the first embodiment. It should be noted that the above modules or units may be hardware components or software components stored in a memory (for example, memory 104) and processed by one or more processors (for example, processors 102a, 102b,..., 102n), and the above modules or units may also be part of a device and can run in the computer terminal 10 provided in the first embodiment.

[0116] The present invention will be described below in conjunction with another optional embodiment.

[0117] Embodiment 3

[0118] The embodiment of the present invention may further provide an electronic device, Figure 5 which is a hardware structure block diagram of an optional electronic device (or mobile device) for executing the calculation method of the power generation of a photovoltaic power station according to the embodiment of the present invention, as Figure 5 shown. The electronic device may include: one or more ( Figure 5 only one is shown in the figure) processors 502, a memory 504, a storage controller, and a peripheral interface. Among them, the peripheral interface is connected to a radio frequency module, an audio module, and a display.

[0119] Among them, the memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the methods and devices in the embodiments of the present application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, the above-mentioned method is implemented. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some instances, the memory may further include a memory remotely disposed relative to the processor, and these remote memories may be connected to the terminal through a network. Examples of the above network include but are not limited to the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0120] The processor can call the information and application programs stored in the memory through the transmission device to execute the following steps: obtain the geographical location data and historical power generation data of the target power station; determine the geographical region to which the target power station belongs based on the geographical location data, iteratively select reference power stations within the geographical region, and calculate the spatial correlation weights of the reference power stations. Calculate the first theoretical power generation amount of the target power station within the target time period based on the spatial correlation weights of the reference power stations and the real-time power generation data, where the first theoretical power generation amount is the theoretically generated power based on spatial weighting; input the historical power generation data into the exponentially weighted moving average model, and output the second theoretical power generation amount of the target power station within the target time period, where the second theoretical power generation amount is the theoretically generated power based on historical weighting; calculate the theoretical power generation amount of the target power station within the target time period based on the first theoretical power generation amount and the second theoretical power generation amount.

[0121] The processor can also call the information and application programs stored in the memory through the transmission device to execute the following steps: the steps of iteratively selecting reference power stations within the geographical region and calculating the spatial correlation weights of the reference power stations, and calculating the first theoretical power generation amount of the target power station within the target time period based on the spatial correlation weights of the reference power stations and the real-time power generation data include: Step 1, select reference power stations within the geographical region according to a preset time interval, and obtain the real-time power generation data of the reference power stations, where the real-time power generation data at least includes: real-time power generation power; Step 2, calculate the distance value between the target power station and the reference power station, and calculate the spatial correlation weight of the reference power station based on the distance value; Step 3, calculate the product of the real-time power generation power and the spatial correlation weight to obtain the first theoretical power generation amount of the target power station within a preset time interval; repeat the above steps 1 to 3, and accumulate the first theoretical power generation amounts within each preset time interval to obtain the first theoretical power generation amount of the target power station within the target time period.

[0122] The processor can also call the information and application programs stored in the memory through the transmission device to execute the following steps: the step of selecting reference power stations within the geographical region according to a preset time interval includes: for each preset time interval, obtain the power generation power values of all adjacent power stations within the geographical region at the same moment; compare the power generation power values of all adjacent power stations at the same moment, and select the adjacent power station with the largest power generation power value as the reference power station of the target power station within the preset time interval.

[0123] The processor can also call the information and application programs stored in the memory through the transmission device to perform the following steps: The steps of calculating the distance value between the target power station and the reference power station and calculating the spatial correlation weight of the reference power station based on the distance value include: calculating the distance value between the target power station and the reference power station based on the longitude and latitude data of the target power station and the reference power station; setting a distance attenuation factor, and calculating the spatial correlation weight of the reference power station according to the distance value and the distance attenuation factor, wherein the spatial correlation weight is inversely proportional to the distance value.

[0124] The processor can also call the information and application programs stored in the memory through the transmission device to perform the following steps: The steps of calculating the distance value between the target power station and the reference power station based on the longitude and latitude data of the target power station and the reference power station include: extracting the latitude value of the target power station and the latitude value of the reference power station based on the longitude and latitude data of the target power station and the reference power station; calculating the longitude difference and latitude difference between the target power station and the reference power station based on the longitude and latitude data of the target power station and the reference power station; substituting the latitude value of the target power station, the latitude value of the reference power station, and the longitude difference and latitude difference between the target power station and the reference power station into the distance calculation formula to calculate the distance value between the target power station and the reference power station.

[0125] The processor can also call the information and application programs stored in the memory through the transmission device to perform the following steps: The steps of inputting the historical power generation data into the exponentially weighted moving average model and outputting the second theoretical power generation amount of the target power station in the target time period include: The exponentially weighted moving average model divides the historical power generation data based on the time stamps of the historical power generation data to obtain the historical power generation data in multiple historical time periods; The exponentially weighted moving average model configures historical correlation weights for the historical power generation data in each historical time period based on a preset smoothing coefficient; The exponentially weighted moving average model performs exponential weighting calculation on the historical power generation data in each historical time period based on the historical correlation weights to obtain the second theoretical power generation amount in the target time period, and takes the second theoretical power generation amount as the output data.

[0126] The processor can also call the information and application programs stored in the memory through the transmission device to perform the following steps: The steps of determining the geographical area to which the target power station belongs based on the geographical location data include: setting the geographical area delineation range, and determining the position coordinates of the target power station based on the geographical location data; taking the position coordinates of the target power station as the regional center, and determining the geographical area to which the target power station belongs based on the geographical area delineation range, wherein the geographical area includes M adjacent power stations related to the target power station, and M is a positive integer.

[0127] By adopting the embodiment of the present invention, a method for calculating the power generation of a photovoltaic power station is provided. A representative reference power station is selected through a dynamic optimization strategy. Based on the relative spatial positions of the reference power station and the target power station and the historical power generation trend information of the target power station, the theoretical power generation is calculated, making full use of the spatial correlation and historical power generation trend between power stations, providing a more accurate, economical and applicable solution for the calculation of the theoretical power generation of a photovoltaic power station, achieving the technical effect of improving the accuracy of the calculation result of a photovoltaic power station, and further solving the technical problem of the low accuracy of the calculation result in the related art when calculating the theoretical power generation of a photovoltaic power station according to meteorological data.

[0128] Those of ordinary skill in the art can understand that Figure 5 the structure shown is only for illustration, and the electronic device can also be a terminal device such as a smart phone, a tablet computer, a palm computer, and a Mobile Internet Device (MID), a PAD, etc. Figure 5 It does not limit the structure of the above-mentioned electronic device. For example, the electronic device may further include more or fewer components (such as a network interface, a display device, etc.) than those shown Figure 5 in the figure, or have a different configuration from that shown Figure 5 in the figure.

[0129] Those of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the relevant hardware of the terminal device through a program, and the program can be stored in a computer-readable storage medium. The storage medium may include: a flash drive, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disc, etc.

[0130] The present invention will be described below in conjunction with another optional embodiment.

[0131] Embodiment 4

[0132] The embodiment of the present invention further provides a computer-readable storage medium. Optionally, in the embodiment of the present invention, the above computer-readable storage medium can be used to save the program code executed by the method for calculating the power generation of a photovoltaic power station provided in the first embodiment above.

[0133] Optionally, in the embodiment of the present invention, the above storage medium can be located in any one of the computer terminals in the computer terminal group in the computer network, or in any one of the mobile terminals in the mobile terminal group.

[0134] An embodiment of the present invention also provides a computer program product. When executed on a data processing device, it is adapted to execute a program for calculating the power generation of a photovoltaic power station, including the steps of: obtaining the geographical location data and historical power generation data of a target power station; determining the geographical region to which the target power station belongs based on the geographical location data, iteratively selecting reference power stations within the geographical region, and calculating the spatial correlation weights of the reference power stations. Calculate the first theoretical power generation of the target power station within a target time period based on the spatial correlation weights of the reference power stations and real-time power generation data, where the first theoretical power generation is the theoretical power generation based on spatial weighting; input the historical power generation data into an exponentially weighted moving average model, and output the second theoretical power generation of the target power station within the target time period, where the second theoretical power generation is the theoretical power generation based on historical weighting; calculate the theoretical power generation of the target power station within the target time period based on the first theoretical power generation and the second theoretical power generation.

[0135] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0136] In the above embodiments of the present invention, the descriptions of each embodiment have their own focuses. For parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0137] In several embodiments provided in the present application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only illustrative. For example, the division of the units can be a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of units or modules can be in electrical or other forms.

[0138] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0139] In addition, the functional units in each embodiment of the present invention can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0140] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The foregoing storage medium includes: various media that can store program codes, such as USB flash drives, read-only memories (ROMs), random access memories (RAMs), mobile hard disks, magnetic disks, or optical discs.

[0141] The foregoing are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A method for calculating the power generation of a photovoltaic power station, characterized in that Including: Obtaining the geographical location data and historical power generation data of the target power station; Determining the geographical region to which the target power station belongs based on the geographical location data, iteratively selecting reference power stations within the geographical region, calculating the spatial correlation weights of the reference power stations, and calculating the first theoretical power generation of the target power station within the target time period based on the spatial correlation weights of the reference power stations and the real-time power generation data, wherein the first theoretical power generation is the theoretically generated power based on spatial weighting; Inputting the historical power generation data into an exponentially weighted moving average model and outputting the second theoretical power generation of the target power station within the target time period, wherein the second theoretical power generation is the theoretically generated power based on historical weighting; Calculating the theoretical power generation of the target power station within the target time period based on the first theoretical power generation and the second theoretical power generation.

2. The method according to claim 1, characterized in that The steps of iteratively selecting reference power stations within the geographical region, calculating the spatial correlation weights of the reference power stations, and calculating the first theoretical power generation of the target power station within the target time period based on the spatial correlation weights of the reference power stations and the real-time power generation data include: Step 1, selecting reference power stations within the geographical region according to a preset time interval and obtaining the real-time power generation data of the reference power stations, wherein the real-time power generation data at least includes: real-time power generation power; Step 2, calculating the distance value between the target power station and the reference power station, and calculating the spatial correlation weight of the reference power station based on the distance value; Step 3, calculating the product of the real-time power generation power and the spatial correlation weight to obtain the first theoretical power generation of the target power station within a preset time interval; Repeating the above steps 1 to 3, and accumulating the first theoretical power generation within each preset time interval to obtain the first theoretical power generation of the target power station within the target time period.

3. The method according to claim 2, wherein The steps of selecting reference power stations within the geographical region according to a preset time interval include: For each preset time interval, obtaining the power generation power values of all adjacent power stations within the geographical region at the same moment; Comparing the power generation power values of all adjacent power stations at the same moment, and selecting the adjacent power station with the largest power generation power value as the reference power station of the target power station within the preset time interval.

4. The method according to claim 2, wherein The steps of calculating the distance value between the target power station and the reference power station and calculating the spatial correlation weight of the reference power station based on the distance value include: Calculating the distance value between the target power station and the reference power station based on the latitude and longitude data of the target power station and the latitude and longitude data of the reference power station; Setting a distance attenuation factor, and calculating the spatial correlation weight of the reference power station according to the distance value and the distance attenuation factor, wherein the spatial correlation weight is inversely proportional to the distance value.

5. The method according to claim 4, characterized in that The steps of calculating the distance value between the target power station and the reference power station based on the latitude and longitude data of the target power station and the latitude and longitude data of the reference power station include: Extracting the latitude value of the target power station and the latitude value of the reference power station based on the latitude and longitude data of the target power station and the latitude and longitude data of the reference power station; Calculate the longitude difference and latitude difference between the target power station and the reference power station based on the longitude and latitude data of the target power station and the longitude and latitude data of the reference power station; Substitute the latitude value of the target power station, the latitude value of the reference power station, and the longitude difference and latitude difference between the target power station and the reference power station into the distance calculation formula to calculate the distance value between the target power station and the reference power station.

6. The method according to claim 1, wherein The step of inputting the historical power generation data into the exponentially weighted moving average model and outputting the second theoretical power generation amount of the target power station in the target time period includes: The exponentially weighted moving average model divides the historical power generation data based on the timestamps of the historical power generation data to obtain the historical power generation data in multiple historical time periods; The exponentially weighted moving average model configures historical correlation weights for the historical power generation data in each historical time period based on a preset smoothing coefficient; The exponentially weighted moving average model performs exponentially weighted calculation on the historical power generation data in each historical time period based on the historical correlation weights to obtain the second theoretical power generation amount in the target time period, and uses the second theoretical power generation amount as the output data.

7. The method according to claim 1, wherein The step of determining the geographical region to which the target power station belongs based on the geographical location data includes: Set the geographical region delineation range and determine the position coordinates of the target power station based on the geographical location data; Use the position coordinates of the target power station as the regional center, and determine the geographical region to which the target power station belongs based on the geographical region delineation range, where the geographical region includes M adjacent power stations related to the target power station, and M is a positive integer.

8. A calculation device for the power generation of a photovoltaic power station, characterized in that, Includes: An acquisition unit for acquiring the geographical location data and historical power generation data of the target power station; A selection unit for determining the geographical region to which the target power station belongs based on the geographical location data, iteratively selecting a reference power station within the geographical region, calculating the spatial correlation weight of the reference power station, and calculating the first theoretical power generation amount of the target power station in the target time period based on the spatial correlation weight of the reference power station and the real-time power generation data, where the first theoretical power generation amount is the theoretically generated power based on spatial weighting; An output unit for inputting the historical power generation data into the exponentially weighted moving average model and outputting the second theoretical power generation amount of the target power station in the target time period, where the second theoretical power generation amount is the theoretically generated power based on historical weighting; A calculation unit for calculating the theoretical power generation amount of the target power station in the target time period based on the first theoretical power generation amount and the second theoretical power generation amount.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored computer program, wherein when the computer program runs, it controls the device where the computer-readable storage medium is located to execute the photovoltaic power station power generation calculation method according to any one of claims 1 to 7.

10. An electronic device, characterized in that, Comprising one or more processors and a memory, the memory being used for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the method for calculating the power generation amount of a photovoltaic power station according to any one of claims 1 to 7.

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