A method and computing device for distributed photovoltaic power generation prediction

Abstract

The present invention provides a method and computing device for distributed photovoltaic power generation forecasting, applicable to the field of new energy forecasting technology. The method comprises obtaining basic data for distributed power stations, including information for each substation within the distributed power station; forecasting the power generation data using a selected power generation data forecasting model, aggregating the forecast results to obtain a summary forecast result; and splitting the summary forecast result to obtain a forecast result for each substation. By splitting the aggregated forecast result into each substation, the method not only improves the accuracy and reliability of distributed photovoltaic power station forecasts but also allows for the rational optimization of resource allocation.

Description

Technical Field

[0001] The present invention relates to the field of new energy prediction technology, and in particular to a method and computing equipment for distributed photovoltaic power generation prediction. Background Art

[0002] In recent years, the installed capacity of distributed photovoltaic power generation has increased year by year. The installed capacity of distributed photovoltaic power generation in many provinces has reached the upper limit of regional power grid dispatching. In view of the small capacity of individual distributed photovoltaic power stations, investment cost restrictions and many other conditions, the prediction system and meteorological and modeling optimization solutions used in centralized photovoltaic power stations are not suitable for distributed photovoltaic power station prediction in terms of cost or basic data requirements.

[0003] In order to ensure that distributed photovoltaics are objective, measurable, adjustable and controllable, it is necessary to predict the power generation of distributed photovoltaics to guide the grid's dispatch of distributed photovoltaic power station power generation plans. Power station operation and maintenance manufacturers also need to understand future power generation capacity to manage power station power generation and maintenance plans.

[0004] Therefore, a method for distributed photovoltaic power generation prediction is needed, which can effectively realize the power prediction of distributed photovoltaic power stations and help grid dispatchers and power station operation and maintenance personnel to better manage and optimize the operation of distributed photovoltaic power stations. Summary of the Invention

[0005] The present invention aims to provide a method and computing device for distributed photovoltaic power generation prediction, which can reasonably distribute the aggregated prediction results to each substation, helping grid dispatchers and power station operation and maintenance personnel to better manage and optimize the operation of distributed photovoltaic power stations.

[0006] According to one aspect of the present invention, a method for distributed photovoltaic power generation prediction is provided, comprising:

[0007] Obtain basic data of distributed power stations, including information of each substation in the distributed power station;

[0008] Predicting the power generation data using the selected power generation data prediction model, summarizing the prediction results to obtain a summary prediction result;

[0009] The aggregated prediction results are split to obtain prediction results for each substation.

[0010] According to some embodiments, the basic data of the distributed power station includes:

[0011] The latitude and longitude coordinates and rated capacity of each substation;

[0012] The operating data parameters of each substation include power generation or power generation.

[0013] According to some embodiments, the rationality of the splitting results is further judged:

[0014] The predicted result of each moment of the area allocation is not greater than the rated capacity of the area;

[0015] The prediction results allocated to each substation at a certain prediction moment are summed up, and the sum value is equal to the summary prediction result at the prediction moment.

[0016] According to some embodiments, power generation data is predicted using a selected power generation data prediction model, wherein the selected power generation data prediction model includes a single data source model:

[0017] The input of the single data source model includes the aggregated historical power generation data of distributed power stations;

[0018] The output of the single data source model includes an aggregated prediction result, and the aggregated prediction result is no greater than the aggregated power plant rated capacity.

[0019] According to some embodiments, power generation data is predicted using a selected power generation data prediction model, wherein the selected power generation data prediction model includes a data inverse model:

[0020] The input of the data inverse model includes the aggregated historical power generation data of distributed power stations, the aggregated rated capacity of power stations and the historical numerical weather forecast values ​​of the power station area;

[0021] The output summary prediction target value of the data inversion model is reversely calculated, and the future summary power prediction value is reversed based on the historical data including the summary capacity and the power of electricity.

[0022] According to some embodiments, power generation data is predicted using a selected power generation data prediction model, wherein the selected power generation data prediction model includes a non-numerical meteorological source model:

[0023] The input of the meteorological forecast model in the non-numerical meteorological source model is the historical meteorological station data collected by the power station;

[0024] Use the power plant's historical power data and the power plant's historical weather station data as modeling data for the power prediction model;

[0025] The input of the power prediction model includes future weather forecast data predicted based on historical weather station data of the power station;

[0026] The non-numerical meteorological source model is applied to the situation where only real-time ground meteorological station data is available and there is no external numerical meteorological forecast source.

[0027] According to some embodiments, power generation data is predicted using a selected power generation data prediction model, wherein the selected power generation data prediction model includes a power plant information-free prediction model:

[0028] The input of the power station information-free prediction model is regional weather forecast data;

[0029] The summary theoretical prediction results are obtained through the theoretical prediction model;

[0030] The product of the power station's rated capacity and the theoretical prediction of a single megawatt is obtained by converting the summarized theoretical prediction results.

[0031] According to some embodiments, splitting the aggregated prediction results includes:

[0032] Calculate the power generation efficiency and use the formula to calculate the power generation efficiency of each substation:

[0033] Pr=Q / (Cap*T)

[0034] Among them, Pr is the power generation efficiency, Q is the actual power generation, Cap is the rated capacity of each substation, and T is the peak sunshine hours.

[0035] According to some embodiments, splitting the aggregated prediction results further includes:

[0036] Calculate the splitting coefficient of each area using the following formula:

[0037] Xn=0.5*(Cap n / sum(Cap n ))+0.5*(Pr n / sum(Pr n ))

[0038] Among them, Xn is the nth area split coefficient, Cap n is the rated capacity of the nth station, Pr n is the power generation efficiency of the nth substation;

[0039] The prediction results of each substation are formed into a prediction result set using the split coefficient calculation.

[0040] According to another aspect of the present invention, there is provided a computing device comprising:

[0041] processor; and

[0042] A memory stores a computer program, which, when executed by the processor, causes the processor to perform any of the aforementioned methods.

[0043] According to an example embodiment of the present invention, basic data of the distributed power station is obtained, the power generation data is predicted using a selected power generation data prediction model, the prediction results are summarized, and the most suitable prediction model is selected based on the characteristics of different substations in the distributed power station and the different historical data that can be obtained, which can significantly improve the accuracy of the prediction; the summarized prediction results are split, and the different conditions of each substation are combined in the splitting process to make the prediction results closer to the actual situation. The method of splitting the summarized prediction results into each substation not only improves the accuracy and reliability of the prediction, but also provides strong support for multiple aspects such as power station operation management and market transactions.

[0044] According to the example embodiment, based on the different characteristics of distributed power stations and the different historical data that can be obtained, multiple modeling methods are provided to choose from, including single data source model, data inversion model, non-numerical meteorological source model and power station information-free prediction model. By selecting different models in different situations, the model performance can be continuously evaluated and optimized, and the power generation of each substation can be predicted more accurately, thereby rationally allocating operation and maintenance resources.

[0045] It should be understood that the foregoing general description and the following detailed description are exemplary only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments.

[0047] Figure 1 A flow chart illustrating a method for distributed photovoltaic power generation prediction according to an example embodiment is shown.

[0048] Figure 2 A schematic block diagram of a single data source model for distributed photovoltaic power prediction according to an example embodiment is shown.

[0049] Figure 3 A schematic block diagram of a distributed photovoltaic power prediction data inverse model according to an example embodiment is shown.

[0050] Figure 4 A schematic block diagram of a numerical meteorological source-free model for distributed photovoltaic power prediction according to an example embodiment is shown.

[0051] Figure 5 A schematic block diagram of a power plant information-free prediction model for distributed photovoltaic power prediction according to an example embodiment is shown.

[0052] Figure 6 A block diagram of a computing device according to an example embodiment of the present invention is shown. DETAILED DESCRIPTION

[0053] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be embodied in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete and will fully convey the concepts of the example embodiments to those skilled in the art. Like reference numerals in the drawings represent like or similar parts, and thus repeated description thereof will be omitted.

[0054] In addition, the described features, structures or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present invention. However, it will be appreciated by those skilled in the art that the technical solutions of the present invention can be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, known methods, devices, implementations or operations are not shown or described in detail to avoid blurring various aspects of the present invention.

[0055] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0056] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, while others may be combined or partially combined. Therefore, the actual execution order may vary depending on the actual situation.

[0057] It should be understood that although the terms first, second, third, etc. may be used herein to describe various components, these components should not be limited by these terms. These terms are used to distinguish one component from another. Thus, the first component discussed below could be referred to as the second component without departing from the teachings of the present inventive concept. As used herein, the term "and / or" includes any one and all combinations of one or more of the associated listed items.

[0058] The user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in the present invention are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with the relevant laws, regulations and standards of relevant countries and regions, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0059] Those skilled in the art will understand that the drawings are merely schematic diagrams of example embodiments, and the modules or processes in the drawings are not necessarily necessary for implementing the present invention, and therefore cannot be used to limit the scope of protection of the present invention.

[0060] In order to adapt to distributed photovoltaic power stations, the present invention provides a method for distributed photovoltaic power generation prediction, which aggregates and predicts distributed photovoltaic power stations and reasonably divides the prediction results into individual independent small-capacity power stations (hereinafter referred to as stations) to address the situations such as small capacity of individual power stations, wide distribution of aggregated power stations, incomplete collection of basic data of power stations, and scattered user demands for prediction results and prediction ranges.

[0061] Before describing the embodiments of the present application, some terms or concepts involved in the embodiments of the present application are explained.

[0062] STC (Standard Test Conditions) are standard test conditions used to evaluate the performance of photovoltaic modules. These conditions define a set of specific environmental parameters to enable the performance of different photovoltaic modules to be compared under the same conditions.

[0063] Exemplary embodiments of the present invention are described below with reference to the accompanying drawings.

[0064] Figure 1 A flow chart illustrating a method for distributed photovoltaic power generation prediction according to an example embodiment is shown.

[0065] According to an example embodiment, see Figure 1 , showing the method steps for distributed photovoltaic power generation prediction, through which the prediction accuracy can be improved and resource allocation can be optimized.

[0066] In S101 , basic data of a distributed power station is obtained.

[0067] According to the example embodiment, the basic data of the distributed power station is obtained, and the basic data of the distributed power station that needs to be predicted is determined, including the information of each substation in the distributed power station, the latitude and longitude coordinates of each substation and the rated capacity Cap; the operating data parameters of each substation, including the generated power Pn or the generated power Qn, where the time resolution is less than or equal to 15 minutes, and the collected irradiance meteorological data of the model area is obtained.

[0068] In S103, the power generation data is predicted using the selected power generation data prediction model, and the prediction results are summarized to obtain a summary prediction result.

[0069] According to the example embodiment, a variety of modeling methods are used to achieve power aggregation prediction, and the aggregated power prediction value PI of the aggregated power station at each moment is obtained. The modeling methods described in this example embodiment include a single data source model, a data inversion model, a non-numerical meteorological source model, and a power station information-free prediction model. The power generation data is predicted by the selected power generation data prediction model, and the prediction results are summarized to obtain a summary prediction result. According to different data collection conditions, a suitable model can be selected to perform power prediction of distributed photovoltaic power stations. Each modeling method has its applicable scenarios and advantages. Through reasonable selection and application, the accuracy and reliability of the prediction can be improved. The final summary prediction result can be used to guide grid scheduling and power station operation and maintenance.

[0070] In S105, the aggregated prediction result is split to obtain a prediction result for each substation.

[0071] According to an example embodiment, the aggregate power forecast value is split, including calculating power generation efficiency, and the power generation efficiency of each substation is calculated by a formula. The formula for calculating the power generation efficiency Pr is:

[0072] Pr=Q / (Cap*T)

[0073] in,

[0074] Q is the actual power generation (unit: MWh);

[0075] Cap is the rated capacity of each substation (unit: MW);

[0076] T is the peak sunshine hours (unit: h).

[0077] The peak sunshine hours T is the total daily radiation (kWh / m 2 ) and STC corresponding to 1000W / m 2 ratio.

[0078] According to an example embodiment, splitting the aggregate power prediction value further includes calculating a splitting coefficient for each substation. The substation splitting coefficient is formed by combining two parameters: the substation rated capacity Cap and the power generation efficiency Pr. The combination of the two parameters adopts a normalized calculation method. The calculation formula of the substation splitting coefficient is:

[0079] Xn=0.5*(Cap n / sum(Cap n ))+0.5*(Pr n / sum(Pr n ))

[0080] in,

[0081] Xn is the splitting coefficient of the nth station;

[0082] Cap n is the rated capacity of the nth substation;

[0083] Pr n is the power generation efficiency of the nth substation.

[0084] Then, the prediction results of each area are calculated using the split coefficient to form a prediction result set. The calculation formula for the prediction result Pn of each area is:

[0085] Pn=PI*Xn

[0086] in,

[0087] Pn is the prediction result set of the nth station;

[0088] PI summarizes the power forecast value;

[0089] Xn is the splitting coefficient of the nth station.

[0090] According to some embodiments, Pn is a set of prediction results, with one prediction value every 15 minutes, and the prediction value at each time point is 96. The power prediction result of the nth station area in the next 24 hours is composed of 96 prediction values, Pn = [P n1 ,P n2 ,......P n96 ].

[0091] In order to avoid incorrect allocation of prediction results to each substation after splitting, the rationality of the splitting results is judged, and two items of rationality verification are retained. The first item is that the prediction result of the substation allocation at each moment is not greater than the rated capacity of the substation; the second item is to sum up the prediction results allocated to each substation at a certain prediction moment, and the sum value is equal to the prediction result summarized at the prediction moment.

[0092] Figure 2 A schematic block diagram of a single data source model for distributed photovoltaic power prediction according to an example embodiment is shown.

[0093] See also Figure 2 According to an example embodiment, the input of the single data source model is the aggregated historical power generation data of distributed power stations and the aggregated power station rated capacity, and the output of the single data source model is the aggregated prediction result, which is not greater than the aggregated power station rated capacity.

[0094] The single-data source model uses aggregated historical power generation data from distributed power plants, or the historical power generation output of distributed aggregated power plants, for modeling and forecasting. The aggregated power plant rated capacity serves as fundamental information and does not require real-time collection. The historical power generation data is preprocessed, including outlier removal, missing value filling, and format normalization, to ensure data quality. An appropriate forecasting model, such as linear regression, ARIMA, or random forest, is selected, and optimal parameter settings are determined to enable the model to simulate the historical power generation data as accurately as possible. The trained single-data model is then used to forecast power generation for future time periods, yielding a summarized forecast result.

[0095] Each modeling process is designed based on whether certain data can be collected and what types of data can be collected. The single-data model is suitable for modeling and prediction under the condition that only power data can be collected. The single-data source model is relatively simple and has fewer data requirements.

[0096] Figure 3 A schematic block diagram of a distributed photovoltaic power prediction data inverse model according to an example embodiment is shown.

[0097] See also Figure 3 According to an example embodiment, the data inversion model inputs include the aggregated historical power generation data of distributed power plants, the aggregated rated capacity of the power plants, and historical numerical weather forecasts for the power plant region. The model's output aggregated forecast target value is reversely calculated to infer the aggregated total power forecast value based on historical data including aggregated capacity and power. The data collection and preprocessing process is not detailed here.

[0098] The data inversion model is based on historical data and uses modeling principles to infer future power. Although the prediction accuracy of this model may be low, by using methods such as time series analysis, the summary prediction results can be reasonably distributed to each substation, generating a prediction result set for each substation, and generating prediction results with certain reference significance.

[0099] Figure 4 A schematic block diagram of a numerical meteorological source-free model for distributed photovoltaic power prediction according to an example embodiment is shown.

[0100] See also Figure 4 According to an example embodiment, the numerical source-free meteorological model is applied when only real-time ground-based meteorological station data is available, without an external numerical weather forecast source. The meteorological forecast model in the numerical source-free model uses historical meteorological station data collected by the power plant as input; the power plant's historical power data and historical meteorological station data are used as modeling data for the power forecast model; the power forecast model uses future weather forecast data predicted based on the power plant's historical meteorological station data as input, and the power forecast model generates a summary forecast result.

[0101] The first step is to predict future meteorological data. The meteorological prediction model is built based on the historical meteorological station data of the power station. The prediction of future meteorological data can be achieved using methods such as time series analysis or sliding average filtering.

[0102] The second step is to build a power prediction model. This model uses the power plant's historical power data and weather station data as the modeling data. The model is trained using the weather station data as the training input and the power plant's historical power data as the target output. Modeling can be performed using convolutional neural networks (CNNs), support vector machines (SVMs), or long short-term memory networks (LSTMs). Multiple modeling methods can be tried, and prediction performance can be compared to select the model that best suits a specific application scenario. Furthermore, ensemble learning methods can be considered to combine the results of multiple models to improve prediction accuracy.

[0103] A power prediction model between weather and power is established. In the third step, weather forecast data is input into the trained power prediction model, and the weather forecast data is used as input to obtain the prediction result of future power.

[0104] This exemplary embodiment can obtain a relatively accurate future power prediction result by using a model trained using historical weather station data and historical power data, and predicting future weather data.

[0105] Figure 5 A schematic block diagram of a power plant information-free prediction model for distributed photovoltaic power prediction according to an example embodiment is shown.

[0106] See also Figure 5 According to an example embodiment, the prediction model without power plant information uses regional weather forecast data as input. A theoretical prediction model is used to generate a theoretical aggregated prediction result, which is then converted to the product of the power plant's rated capacity and the theoretical single-megawatt prediction. This conversion provides robust data support for power plant operations and management, helping to predict the plant's future power generation capacity.

[0107] The prediction model without power station information can provide a preliminary prediction reference in the absence of historical data of power stations, and combines large-scale regional numerical meteorological information and standard prediction models to predict the power and daily power generation of unit installed capacity in the region.

[0108] The method for distributed photovoltaic power generation prediction proposed in the present invention summarizes and predicts the distributed photovoltaic power and splits the prediction results into each substation. The method can realize the summary prediction by large-capacity power station, and comprehensively distribute the summary prediction results according to the capacity and power generation efficiency of each substation. Compared with the method of only distributing the summary prediction results according to the rated capacity of each substation, this method can more accurately distribute the prediction value. The prediction value allocated to the substation with low power generation efficiency is lower, and the prediction value allocated to the substation with high power generation efficiency is higher, thereby better ensuring the prediction accuracy of each substation.

[0109] Figure 6 A block diagram of a computing device according to an example embodiment of the present invention is shown.

[0110] like Figure 6 As shown, computing device 30 includes processor 12 and memory 14. Computing device 30 may also include bus 22, network interface 16, and I / O interface 18. Processor 12, memory 14, network interface 16, and I / O interface 18 may communicate with each other via bus 22.

[0111] The processor 12 may include one or more general-purpose CPUs (Central Processing Units, processors), microprocessors, or application-specific integrated circuits, etc., for executing relevant program instructions.

[0112] The memory 14 may include machine-readable media in the form of volatile memory, such as random access memory (RAM), read-only memory (ROM), and / or cache memory. The memory 14 is used to store one or more programs including instructions and data. The processor 12 may read the instructions stored in the memory 14 to execute the method according to the embodiment of the present invention described above.

[0113] The computing device 30 may also communicate with one or more networks through the network interface 16. The network interface 16 may be a wireless network interface.

[0114] The bus 22 may include an address bus, a data bus, a control bus, etc. The bus 22 provides a path for exchanging information between various components.

[0115] It should be noted that, in the specific implementation process, the computing device 30 may also include other components necessary for normal operation. In addition, those skilled in the art will understand that the above-mentioned device may also only include components necessary to implement the embodiments of this specification, and does not necessarily include all components shown in the figure.

[0116] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above method. The computer-readable storage medium may include, but is not limited to, any type of disk, including a floppy disk, an optical disk, a DVD, a CD-ROM, a microdrive and a magneto-optical disk, a ROM, a RAM, an EPROM, an EEPROM, a DRAM, a VRAM, a flash memory device, a magnetic or optical card, a nanosystem (including a molecular memory IC), a network storage device, a cloud storage device, or any type of medium or device suitable for storing instructions and / or data.

[0117] An embodiment of the present invention further provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute part or all of the steps of any method described in the above method embodiments.

[0118] Those skilled in the art will readily appreciate that the technical solutions of the present invention can be implemented using software and / or hardware. "Unit" and "module" in this specification refer to software and / or hardware that can independently perform specific functions or work in conjunction with other components. Examples of hardware include field programmable gate arrays and integrated circuits.

[0119] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations. However, those skilled in the art should be aware that the present invention is not limited by the order of the actions described, because according to the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0120] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0121] In the several embodiments provided herein, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is merely a logical functional division. In actual implementation, other division methods may be used. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not implemented. In addition, the coupling or direct coupling or communication connection shown or discussed may be through some service interface. The indirect coupling or communication connection of devices or units may be electrical or other forms.

[0122] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0123] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0124] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present invention, 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. This computer software product is stored in a memory and includes a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of the present invention.

[0125] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0126] The exemplary embodiments of the present invention are specifically shown and described above. It should be understood that the present invention is not limited to the detailed structure, configuration or implementation described herein; on the contrary, the present invention is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended clauses.

[0127] Those skilled in the art will readily appreciate that the technical solutions of the present invention can be implemented using software and / or hardware. "Unit" and "module" in this specification refer to software and / or hardware that can independently perform specific functions or work in conjunction with other components. Examples of hardware include field programmable gate arrays and integrated circuits.

[0128] It should be noted that for the aforementioned method embodiments, for simplicity of description, they are all expressed as a series of action combinations. However, those skilled in the art should be aware that the present invention is not limited by the order of the actions described, because according to the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in this specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0129] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0130] In the several embodiments provided herein, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of units is merely a logical functional division. In actual implementation, other division methods may be used. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not implemented. In addition, the coupling or direct coupling or communication connection shown or discussed may be through some service interface. The indirect coupling or communication connection of devices or units may be electrical or other forms.

[0131] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0132] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0133] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present invention, 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. This computer software product is stored in a memory and includes a number of instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of the present invention.

[0134] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0135] The exemplary embodiments of the present invention are specifically shown and described above. It should be understood that the present invention is not limited to the detailed structure, configuration or implementation described herein; on the contrary, the present invention is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended clauses.

Claims

1. A method for distributed photovoltaic power generation prediction, characterized in that: include: Obtain basic data of distributed power stations, including information of each substation in the distributed power station; Predicting the power generation data using the selected power generation data prediction model, summarizing the prediction results to obtain a summary prediction result; Splitting the aggregated prediction results to obtain prediction results for each substation; Wherein, splitting the aggregated prediction results includes: Calculate power generation efficiency; Calculating a splitting coefficient for each substation, wherein the substation splitting coefficient is formed by combining two parameters: the substation rated capacity and the power generation efficiency, wherein the combination of the two parameters, the substation rated capacity and the power generation efficiency, is calculated using a normalized calculation method; The prediction results of each area are calculated using the split coefficients of each area to form a prediction result set. The calculation formula for the prediction results of each area is: Pn=PI*Xn in, Pn is the prediction result of the nth station; PI is the summary power prediction value; Xn is the splitting coefficient of the nth station.

2. The method according to claim 1, characterized in that The basic data of the distributed power station includes: The latitude and longitude coordinates and rated capacity of each substation; The operating data parameters of each substation include power generation or power generation.

3. The method according to claim 1, characterized in that It also includes a reasonable judgment on the split results: The predicted result of each moment of the area allocation is not greater than the rated capacity of the area; The prediction results allocated to each substation at a certain prediction moment are summed up, and the sum value is equal to the summary prediction result at the prediction moment.

4. The method according to claim 1, wherein The power generation data is predicted using a selected power generation data prediction model, wherein the selected power generation data prediction model includes a single data source model: The input of the single data source model includes the aggregated historical power generation data of distributed power stations; The output of the single data source model includes an aggregated prediction result, and the aggregated prediction result is no greater than the aggregated power plant rated capacity.

5. The method according to claim 1, wherein The power generation data is predicted using a selected power generation data prediction model, wherein the selected power generation data prediction model includes a data inverse model: The input of the data inverse model includes the aggregated historical power generation data of distributed power stations, the aggregated rated capacity of power stations and the historical numerical weather forecast values ​​of the power station area; The output summary prediction target value of the data inversion model is reversely calculated, and the future summary power prediction value is reversed based on the historical data including the summary capacity and the power of electricity.

6. The method according to claim 1, characterized in that The power generation data is predicted using a selected power generation data prediction model, wherein the selected power generation data prediction model includes a non-numerical meteorological source model: The input of the meteorological forecast model in the non-numerical meteorological source model is the historical meteorological station data collected by the power station; Use the power plant's historical power data and the power plant's historical weather station data as modeling data for the power prediction model; The input of the power prediction model includes future weather forecast data predicted based on historical weather station data of the power station; The non-numerical meteorological source model is applied to the situation where only real-time ground meteorological station data is available and there is no external numerical meteorological forecast source.

7. The method according to claim 1, characterized in that The power generation data is predicted using a selected power generation data prediction model, wherein the selected power generation data prediction model includes a prediction model without power plant information: The input of the power station information-free prediction model is regional weather forecast data; The summary theoretical prediction results are obtained through the theoretical prediction model; The product of the power station's rated capacity and the theoretical prediction of a single megawatt is obtained by converting the summarized theoretical prediction results.

8. The method according to claim 1, characterized in that The power generation efficiency is calculated by the following formula: The formula for calculating the power generation efficiency of each substation is: Pr=Q / (Cap*T) Among them, Pr is the power generation efficiency, Q is the actual power generation, Cap is the rated capacity of each substation, and T is the peak sunshine hours.

9. The method according to claim 1, characterized in that The splitting coefficient of each area is calculated using the following formula: Xn=0.5*(Cap n / sum(Cap n ))+0.5*(Pr n / sum(Pr n , Among them, Xn is the nth area split coefficient, Cap n is the rated capacity of the nth station, Pr n is the power generation efficiency of the nth substation.

10. A computing device, characterized in that include: processor; as well as A memory storing a computer program, which, when executed by the processor, causes the processor to perform the method according to any one of claims 1 to 9.

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