Power generation data generation method and apparatus

Abstract

The disclosure provides a power generation data generation method and device, and particularly relates to the technical field of distributed photovoltaic power generation prediction. The specific implementation scheme is: obtaining target prediction weather data; obtaining first power generation data of distributed photovoltaic; obtaining second power generation data of distributed photovoltaic according to the ratio of the first power generation data of distributed photovoltaic and a control parameter; inputting the target prediction weather data and the second power generation data of distributed photovoltaic into a preset power prediction model to obtain third power generation data of distributed photovoltaic. This way improves the accuracy of distributed photovoltaic power generation prediction.

Description

Technical Field

[0001] This disclosure relates to the field of new energy technology, specifically to the field of distributed photovoltaic power generation prediction technology, and particularly to a method and apparatus for generating power generation data. Background Technology

[0002] With the establishment of a green, low-carbon, and circular economic system and a clean, low-carbon, safe, and efficient energy system in China, the demand for new energy power generation is increasing. Currently, many prefecture-level cities in China have begun large-scale construction of distributed photovoltaic (PV) installations, with the installed capacity growing rapidly.

[0003] In existing technologies, since distributed photovoltaic devices generally rarely record specific power generation data, the prediction of future power generation is mainly achieved through manual estimation. Summary of the Invention

[0004] This disclosure provides a method, apparatus, device, and storage medium for generating power generation data.

[0005] In a first aspect, embodiments of this disclosure provide a method for generating power generation data, the method comprising: acquiring target predicted weather data; acquiring first power generation data of distributed photovoltaic (PV); obtaining second power generation data of distributed PV based on the ratio of the first power generation data of distributed PV to a reference parameter; and inputting the target predicted weather data and the second power generation data of distributed PV into a preset power prediction model to obtain third power generation data of distributed PV.

[0006] Secondly, embodiments of this disclosure provide a power generation data generation apparatus, which includes: a first acquisition module configured to acquire target predicted weather data; a second acquisition module configured to acquire first power generation data of distributed photovoltaic (PV) power generation; a data calculation module configured to obtain second power generation data of distributed PV power generation based on the ratio of the first power generation data of distributed PV power generation to a reference parameter; and a data prediction module configured to input the target predicted weather data and the second power generation data of distributed PV power generation into a preset power prediction model to obtain third power generation data of distributed PV power generation.

[0007] Thirdly, embodiments of this disclosure provide an electronic device including one or more processors; and a storage device storing one or more programs thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the power generation data generation method as described in any embodiment of the first aspect.

[0008] Fourthly, embodiments of this disclosure provide a computer-readable medium having a computer program stored thereon that, when executed by a processor, implements a power generation data generation method as described in any embodiment of the first aspect.

[0009] Fifthly, embodiments of this disclosure provide a computer program product, including a computer program that, when executed by a processor, implements a power generation data generation method as described in any embodiment of the first aspect.

[0010] This disclosure improves the accuracy of distributed photovoltaic power generation forecasting.

[0011] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0012] Figure 1 This is an exemplary system architecture diagram to which this disclosure can be applied;

[0013] Figure 2 This is a flowchart of one embodiment of the power generation data generation method according to the present disclosure;

[0014] Figure 3 This is a schematic diagram of an application scenario of the power generation data generation method according to this disclosure;

[0015] Figure 4 This is a flowchart of yet another embodiment of the power generation data generation method according to the present disclosure;

[0016] Figure 5 This is a schematic diagram of one embodiment of the power generation data generation apparatus according to the present disclosure;

[0017] Figure 6 This is a schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present disclosure. Detailed Implementation

[0018] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0019] It should be noted that, unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other. This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0020] Figure 1 An exemplary system architecture 100 is shown, in which embodiments of the power generation data generation method of this disclosure can be applied.

[0021] like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, and 103, a network 104, and a server 105. Network 104 serves as the medium for providing communication links between terminal devices 101, 102, and 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0022] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Applications such as power generation prediction and communication applications can be installed on terminal devices 101, 102, and 103.

[0023] Terminal devices 101, 102, and 103 can be either hardware or software. When terminal devices 101, 102, and 103 are hardware, they can be various electronic devices with displays, including but not limited to mobile phones and laptops. When terminal devices 101, 102, and 103 are software, they can be installed in the electronic devices listed above. They can be implemented as multiple software programs or software modules (e.g., used to provide power generation data generation services) or as a single software program or software module. No specific limitations are made here.

[0024] Server 105 can be a server that provides various services, such as acquiring data to be processed, including: acquiring target forecast weather data; acquiring first power generation data of distributed photovoltaic power generation; obtaining second power generation data of distributed photovoltaic power generation based on the ratio of first power generation data of centralized photovoltaic power generation to second power generation data of centralized photovoltaic power generation and first power generation data of distributed photovoltaic power generation; and inputting the target forecast weather data and second power generation data of distributed photovoltaic power generation into a preset power prediction model to obtain third power generation data of distributed photovoltaic power generation.

[0025] It should be noted that server 105 can be either hardware or software. When server 105 is hardware, it can be implemented as a distributed server cluster consisting of multiple servers, or as a single server. When server 105 is software, it can be implemented as multiple software programs or software modules (e.g., used to provide power generation data generation services), or as a single software program or software module. No specific limitations are made here.

[0026] It should be noted that the power generation data generation method provided in the embodiments of this disclosure can be executed by server 105, by terminal devices 101, 102, and 103, or by server 105 and terminal devices 101, 102, and 103 in cooperation with each other. Accordingly, all parts (e.g., units, sub-units, modules, and sub-modules) of the download data generation device can be entirely located in server 105, entirely located in terminal devices 101, 102, and 103, or separately located in server 105 and terminal devices 101, 102, and 103.

[0027] It should be understood that Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0028] Figure 2 A flowchart 200 illustrating an embodiment of a power generation data generation method is shown. The power generation data generation method includes the following steps:

[0029] Step 201: Obtain the target forecast weather data.

[0030] In this embodiment, the execution entity (e.g., Figure 1 The server 105 or terminal device 101, 102, 103 can obtain the target forecast weather data locally or on a remote server that stores the target forecast weather data.

[0031] Among them, the target forecast weather data is used to indicate the forecast weather data for future time periods adjacent to the current time.

[0032] Here, the target predicted weather data can be weather data predicted by weather forecasts, or weather data after correction of weather forecasts; this application does not limit this.

[0033] The target forecast weather data can include multiple weather data that affect photovoltaic power generation, such as cloud cover, temperature, humidity, wind speed, and irradiance.

[0034] In addition, the target forecast weather data can also include the weights of each weather data point. Here, the weights of each weather data point can be determined based on the degree of impact of each weather data point on photovoltaic power generation.

[0035] In some optional ways, the target predicted weather data is obtained, including: obtaining real weather data of a second historical time period adjacent to the current time and first predicted weather data of a future time period adjacent to the current time; inputting the real weather data into a preset weather prediction model to obtain second predicted weather data; correcting the first predicted weather data according to the second predicted weather data to obtain corrected first predicted weather data; and determining the target predicted weather data based on the corrected first predicted weather data.

[0036] In this implementation, the executing entity can first obtain the real weather data of the second historical time period adjacent to the current time and the first predicted weather data adjacent to the current time. The first predicted weather data is typically used to indicate the weather data predicted in the weather forecast.

[0037] Here, typically, the second historical time period and the future time period are of equal length. The length of the historical time period and the future time period can be set based on experience and actual needs, such as 1 day, 3 days, 1 month, etc., and this application does not limit this.

[0038] Specifically, the real weather data is the weather data of the historical 24 hours adjacent to the current time, and the first predicted weather data is the weather data of the next 24 hours adjacent to the current time.

[0039] Furthermore, the implementing entity can input real weather data into a preset weather prediction model to obtain second predicted weather data, wherein the preset weather prediction model is trained based on real weather data samples labeled with the second predicted weather.

[0040] The preset weather forecast model can be a model used for processing sequence modeling tasks in existing or future technologies, such as a model based on LSTM (Long Short-Term Memory) or a model based on TCN (Temporal Convolutional Network), etc. This application does not limit it.

[0041] After obtaining the second forecast weather data, the implementing entity can correct the first forecast weather data based on the second forecast weather data to obtain the corrected first forecast weather data, and determine the target forecast weather data based on the corrected first forecast weather data.

[0042] This implementation method acquires real weather data for a second historical time period adjacent to the current time and first predicted weather data for a future time period adjacent to the current time; inputs the real weather data into a preset weather prediction model to obtain second predicted weather data; corrects the first predicted weather data based on the second predicted weather data to obtain corrected first predicted weather data; and determines the target predicted weather data based on the corrected first predicted weather data, effectively improving the accuracy of the determined target predicted weather data.

[0043] In some alternative approaches, the target forecast weather data is determined based on the corrected first forecast weather data, including: determining the target forecast weather data according to at least two weather data and the weights of each weather data in the at least two weather data.

[0044] In this implementation, the first predicted weather data includes at least two weather data, such as temperature and humidity. After obtaining at least two weather data, the executing entity can further obtain the weights corresponding to each weather data, and determine the target predicted weather data based on the at least two weather data and the weights of each weather data in the at least two weather data.

[0045] The weights of each weather data point can be determined based on the degree of impact of each weather data point on photovoltaic power generation.

[0046] Specifically, at least two weather data points include: temperature, irradiance, cloud cover, and humidity, with corresponding weights of 0.8, 0.7, 0.5, and 0.4, respectively. The implementing entity can determine the target forecast weather data based on the at least two weather data points and the weights of each weather data point within those at least two weather data points.

[0047] This implementation method determines the target predicted weather data based on at least two weather data points and the weights of each weather data point within those two data points. The target predicted weather data and the second power generation data are then input into a preset power prediction model to obtain the third power generation data. This fully considers the proportion of each weather data point in the target predicted weather data, thereby further improving the accuracy of the determined second wind power generation data.

[0048] In some alternative approaches, the weather forecasting model is a TCN network-based model.

[0049] In this implementation, the executing entity can input real weather data into the TCN network-based model to obtain the second predicted weather data.

[0050] The TCN network can accept input sequences of arbitrary length as input and map them to output sequences of equal length. The TCN network can achieve large-scale parallel processing and has the advantages of fast training speed and low memory consumption.

[0051] This application improves the efficiency of generating second predicted weather data by inputting real weather data into a TCN network-based model.

[0052] Step 202: Obtain the first power generation data of the distributed photovoltaic system.

[0053] In this embodiment, the executing entity can obtain the first power generation data of the distributed photovoltaic system locally or on a remote server that stores the first power generation data of the distributed photovoltaic system.

[0054] The first power generation data is used to indicate the maximum power generation data in the first historical time period adjacent to the current time, which is the installed capacity.

[0055] Step 203: Obtain the second power generation data of the distributed photovoltaic system based on the ratio of the first power generation data to the reference parameters.

[0056] In this embodiment, the executing entity can further obtain the first power generation data and the second power generation data of the centralized photovoltaic system, and obtain the second power generation data of the distributed photovoltaic system based on the ratio of the first power generation data and the second power generation data of the centralized photovoltaic system, i.e., the comparison parameter and the first power generation data of the distributed photovoltaic system.

[0057] Specifically, it can be expressed by the following formula:

[0058] Comparison parameter = First power generation data of centralized photovoltaic power generation / Second power generation data of centralized photovoltaic power generation = First power generation data of distributed photovoltaic power generation / Second power generation data of distributed photovoltaic power generation.

[0059] The second power generation data is used to indicate the power generation data at each point in time within the second historical time period adjacent to the current time.

[0060] Specifically, if the first power generation data of centralized photovoltaic is 2000W, the second power generation data of centralized photovoltaic is 9:00-800W, 12:00-800W, 15:00-600W, 18:00-600W, 21:00-800W, and 24:00-1000W, and the first power generation data of distributed photovoltaic is 1000W, then the second power generation data of distributed photovoltaic is 9:00-400W, 12:00-400W, 15:00-300W, 18:00-300W, 21:00-400W, and 24:00-500W.

[0061] Step 204: Input the target predicted weather data and the second power generation data of the distributed photovoltaic system into the preset power prediction model to obtain the third power generation data of the distributed photovoltaic system.

[0062] In this embodiment, after obtaining the target forecast weather data and the second power generation data of the distributed photovoltaic system, the executing entity can input the target forecast weather data and the second power generation data into a preset power prediction model to obtain the third power generation data.

[0063] The third power generation data is used to indicate the power generation data for the aforementioned future time period, that is, the power generation data at each point in time within the future time period.

[0064] Here, the power forecasting model is trained based on predicted weather data labeled with third power generation data and second power generation data samples.

[0065] Specifically, the target predicted weather data is the weather data for the next 24 hours adjacent to the current time, and the second power generation data is the power generation data for each time point within the historical 24 hours adjacent to the current time, for example, 9:00-800W, 12:00-800W, 15:00-600W, 18:00-600W, 21:00-800W, 24-1000W. The executing entity inputs the target predicted weather data and the second power generation data into a preset power prediction model to obtain the third power generation data, which is the power generation data for each time point within the next 24 hours adjacent to the current time, for example, 9:00-600W, 12:00-800W, 15:00-600W, 18:00-600W, 21:00-600W, 24-1000W.

[0066] The power prediction model can be a machine learning model from existing or future technologies, such as the XGBoost model or the lightGBM model, and this application does not limit it.

[0067] In some alternative approaches, the power forecasting model is the lightGBM model.

[0068] In this implementation, the executing entity can input the target predicted weather data and the second power generation data into the lightGBM model to obtain the third power generation data.

[0069] The lightGBM model is an implementation of GBDT (Gradient Boosting Decision Tree). Its core principle is to train an ensemble of base classifiers (decision trees) to obtain the optimal model. It has the advantages of fast training speed and low computational cost.

[0070] This implementation improves the efficiency of generating third-generation power generation data by inputting target forecast weather data and second-generation power generation data into the lightGBM model.

[0071] In some alternative approaches, the method further includes: correcting the third power generation data according to the standard power curve of centralized photovoltaic power generation to obtain the target power generation data.

[0072] In this implementation, after obtaining the third power generation data of distributed photovoltaic (PV), the executing entity can further obtain the standard power curve of centralized PV. The standard power curve indicates the maximum and minimum output power that centralized PV can output. Therefore, the executing entity can correct the third power generation data based on the maximum and minimum output power indicated in the standard power curve to obtain corrected third power generation data, and then determine the corrected third power generation data as the target power generation data.

[0073] Specifically, the third power generation data is: 9:00 -500W, 12:00 -600W, 15:00 -800W, 18:00 -1000W, 21:00 -1000W, 24:00 -1200W. According to the standard power curve, the maximum output power is 1000W. Therefore, the output power of 1200W at 24:00 is an abnormal value. According to the standard power curve, the output power at 24:00 can be corrected to 1000W.

[0074] This approach corrects the third power generation data based on the standard power curve of centralized photovoltaic power generation to obtain the target power generation data, effectively avoiding the generation data being too large or too small, and further improving the accuracy of the generated power generation data.

[0075] See also Figure 3 , Figure 3This is a schematic diagram of an application scenario of the power generation data generation method according to this embodiment. The execution entity 301 acquires target predicted weather data 302, wherein the target predicted weather data 302 is used to indicate the predicted weather data for a future time period adjacent to the current time, such as the weather data for the next day predicted by the weather forecast; acquires first power generation data 303 of distributed photovoltaic, wherein the first power generation data is used to indicate the maximum power generation data within a historical first time period adjacent to the current time; based on the ratio of the first power generation data of distributed photovoltaic to a reference parameter, the reference parameter indicates the ratio of the first power generation data of centralized photovoltaic to the second power generation data of centralized photovoltaic, to obtain second power generation data 304 of distributed photovoltaic, wherein the second power generation data is used to indicate the power generation data at each time point within a historical second time period adjacent to the current time, the historical second time period being of the same length as the future time period, such as the power generation data of distributed photovoltaic at each time point of the current day; inputs the target predicted weather data 302 and the second power generation data 304 of distributed photovoltaic into a preset power prediction model 305 to obtain third power generation data 306 of distributed photovoltaic, wherein the third power generation data is used to indicate the power generation data for a future time period, such as the power generation data of distributed photovoltaic for the next day, and the power prediction model is trained based on the predicted weather data and second power generation data samples labeled with the third power generation data.

[0076] The power generation data generation method provided in the embodiments of this disclosure obtains target predicted weather data; obtains first power generation data of distributed photovoltaic power generation; obtains second power generation data of distributed photovoltaic power generation based on the ratio of the first power generation data of distributed photovoltaic power generation to a reference parameter; inputs the target predicted weather data and the second power generation data of distributed photovoltaic power generation into a preset power prediction model to obtain third power generation data of distributed photovoltaic power generation, thereby improving the accuracy of distributed photovoltaic power generation prediction.

[0077] Further reference Figure 4 It shows Figure 2 The flowchart 400 shows another embodiment of the power generation data generation method. In this embodiment, the power generation data generation method flowchart 400 may include the following steps:

[0078] Step 401: Obtain the target forecast weather data.

[0079] In this embodiment, the implementation details and technical effects of step 401 can be found in the description of step 201, and will not be repeated here.

[0080] Step 402: Based on the daily performance index data of distributed photovoltaic power generation in the first historical time period adjacent to the current time, determine the first average of a preset number of minimum power generation values ​​on sunny days and the second average of a preset number of maximum power generation values ​​on non-sunny days.

[0081] In this embodiment, the executing entity can first obtain the daily performance index data of distributed photovoltaic power generation within the first historical time period adjacent to the current time, and then remove abnormal days based on the performance index data to obtain the data after removing abnormal days.

[0082] The performance index data may include: the mean, variance, maximum, and minimum values ​​of power generation data.

[0083] Furthermore, the implementing entity can determine a preset number of sunny days, i.e. days when distributed photovoltaic power generation can be normal, from the data after removing abnormal days, and determine the first average of the preset number of sunny days' minimum power generation based on the minimum power generation of each of the preset number of sunny days.

[0084] Here, the minimum power generation for each sunny day can be determined based on the total power generation for the entire sunny day, or based on the power generation for a preset time period on each sunny day. This application does not limit this.

[0085] The preset time period can be the time period for centralized distributed photovoltaic power generation, such as 11:00 to 14:00, 12:00 to 14:00, etc., and can be set according to actual needs.

[0086] Specifically, the implementing entity can first obtain the daily performance index data of distributed photovoltaic (PV) power generation within a historical time period, for example, 20 days. Then, it removes abnormal days from the daily performance index data to obtain the data after removing abnormal days, for example, 16 days of distributed PV performance index data. Further, the implementing entity determines a preset number of sunny days from the remaining 16 days of distributed PV performance data, for example, 5 sunny days, and the minimum power generation between 11:00 and 14:00 each day, such as 500W, 600W, 400W, 300W, and 200W respectively. Then, based on the minimum power generation of the 5 days, the average value, i.e., the first average, is calculated, which is 400W.

[0087] Furthermore, the implementing entity can identify a preset number of non-sunny days from the data after removing abnormal days, i.e., days when distributed photovoltaic power cannot generate electricity normally, such as cloudy days, rainy days, etc., and determine the second average of the preset number of non-sunny days' maximum daily power generation based on the maximum daily power generation of the preset number of non-sunny days.

[0088] Specifically, the implementing entity can first obtain the daily performance index data of distributed photovoltaic power within a historical first time period, for example, 20 days, and then remove abnormal days based on the daily performance index data to obtain the data after removing abnormal days, for example, 16 days of distributed photovoltaic performance index data.

[0089] Furthermore, the implementing entity determines a preset number of non-sunny days from the performance data of the distributed photovoltaic system for the remaining 16 days, for example, 5 non-sunny days, and the maximum power generation during the period from 11:00 to 14:00 each day, such as 100W, 50W, 40W, 60W, and 50W. Then, based on the maximum power generation of the 5 days, the average value, i.e., the second average value, is calculated, which is 60W.

[0090] Step 403: The difference between the first mean and the second mean is determined as the first power generation data of the distributed photovoltaic system.

[0091] In this embodiment, after obtaining the first mean and the second mean, the executing entity can directly determine the difference between the first mean and the second mean as the first power generation data of the distributed photovoltaic system.

[0092] Specifically, the first average is 400W, the second average is 60W, and the difference between the first and second averages is 340W, meaning the first power generation data is 340W.

[0093] Step 404: Obtain the second power generation data of the distributed photovoltaic system based on the ratio of the first power generation data to the reference parameters.

[0094] In this embodiment, the implementation details and technical effects of step 404 can be found in the description of step 203, and will not be repeated here.

[0095] Step 405: Input the target predicted weather data and the second power generation data of the distributed photovoltaic system into the preset power prediction model to obtain the third power generation data of the distributed photovoltaic system.

[0096] In this embodiment, the implementation details and technical effects of step 405 can be found in the description of step 204, and will not be repeated here.

[0097] The above embodiments of this disclosure, and Figure 2 Compared to the previous embodiment, this embodiment emphasizes determining a first average of the minimum power generation on a preset number of sunny days and a second average of the maximum power generation on a preset number of non-sunny days based on the daily performance index data of distributed photovoltaics within a historical first time period. The difference between the first average and the second average is determined as the first power generation data of distributed photovoltaics. Then, based on the ratio of the first power generation data of distributed photovoltaics to the reference parameters, the second power generation data of distributed photovoltaics is obtained. The target predicted weather data and the second power generation data of distributed photovoltaics are input into a preset power prediction model to obtain the third power generation data of distributed photovoltaics. This improves the accuracy of the determined second power generation data of distributed photovoltaics, thereby improving the accuracy of the determined predicted power generation data of distributed photovoltaics.

[0098] Further reference Figure 5As an implementation of the methods shown in the above figures, this disclosure provides an embodiment of a power generation data generation device, which is similar to... Figure 1 Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices.

[0099] like Figure 5 As shown, the power generation data generation device 500 of this embodiment includes: a first acquisition module 501, a second acquisition module 502, a data calculation module 503, and a data prediction module 504.

[0100] The first acquisition module 501 can be configured to acquire target predicted weather data.

[0101] The second acquisition module 502 can be configured to acquire the first power generation data of the distributed photovoltaic system.

[0102] The data calculation module 503 can be configured to obtain the second power generation data of the distributed photovoltaic system based on the ratio of the first power generation data of the distributed photovoltaic system to the reference parameter.

[0103] The data prediction module 504 can be configured to input target predicted weather data and second power generation data of distributed photovoltaic power generation into a preset power prediction model to obtain third power generation data of distributed photovoltaic power generation.

[0104] In some optional embodiments of this example, the second acquisition module is further configured to: determine a first average of a preset number of minimum power generation values ​​on sunny days and a second average of a preset number of maximum power generation values ​​on non-sunny days based on the daily performance index data of distributed photovoltaic power generation within a historical first time period adjacent to the current time; and determine the difference between the first average and the second average as the first power generation data of distributed photovoltaic power generation.

[0105] In some optional embodiments of this example, the first acquisition module is further configured to acquire real weather data of a historical second time period adjacent to the current time and first predicted weather data of a future time period adjacent to the current time; input the real weather data into a preset weather prediction model to obtain second predicted weather data; correct the first predicted weather data according to the second predicted weather data to obtain corrected first predicted weather data; and determine the target predicted weather data based on the corrected first predicted weather data.

[0106] In some optional embodiments of this example, determining target forecast weather data based on the corrected first forecast weather data includes: determining target forecast weather data according to at least two weather data and the weights of each weather data in the at least two weather data.

[0107] In some alternative embodiments of this invention, the device further includes a correction module configured to correct the third power generation data according to a standard power curve of a centralized photovoltaic system to obtain target power generation data.

[0108] The acquisition, storage, and application of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0109] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0110] like Figure 6 The diagram shown is a block diagram of an electronic device for a power generation data generation method according to an embodiment of the present disclosure.

[0111] 600 is a block diagram of an electronic device for a power generation data generation method according to embodiments of the present disclosure. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0112] like Figure 6 As shown, the electronic device includes one or more processors 601, a memory 602, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components are interconnected via different buses and can be mounted on a common motherboard or otherwise as required. The processors can process instructions executed within the electronic device, including instructions stored in or on memory to display graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In other embodiments, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple electronic devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 6 Take the 601 processor as an example.

[0113] The memory 602 is the non-transitory computer-readable storage medium provided in this disclosure. The memory stores instructions executable by at least one processor to cause the at least one processor to perform the power generation data generation method provided in this disclosure. The non-transitory computer-readable storage medium of this disclosure stores computer instructions for causing a computer to perform the power generation data generation method provided in this disclosure.

[0114] Memory 602, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as the program instructions / modules corresponding to the power generation data generation method in this embodiment of the present disclosure (e.g., attached...). Figure 5 The first acquisition module 501, the second acquisition module 502, the data calculation module 503, and the data prediction module 504 are shown. The processor 601 executes various functional applications and data processing of the server by running non-transient software programs, instructions, and modules stored in the memory 602, thereby realizing the power generation data generation method in the above method embodiments.

[0115] Memory 602 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created by the use of the face-tracking electronic device, etc. Furthermore, memory 602 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory 602 may optionally include memory remotely located relative to processor 601, and this remote memory can be connected to the lane-line detection electronic device via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0116] The electronic device for the power generation data generation method may further include an input device 603 and an output device 604. The processor 601, memory 602, input device 603, and output device 604 can be connected via a bus or other means. Figure 6 Taking the example of a connection between China and Israel via a bus.

[0117] Input device 603 can receive input digital or character information, as well as key signal inputs related to user settings and function control of the electronic device for lane detection, such as touch screens, keypads, mice, trackpads, touchpads, pointers, one or more mouse buttons, trackballs, joysticks, etc. Output device 604 may include display devices, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors). The display device may include, but is not limited to, liquid crystal displays (LCDs), light-emitting diode (LED) displays, and plasma displays. In some embodiments, the display device may be a touch screen.

[0118] Various implementations of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, application-specific integrated circuits (ASICs), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include: implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transferring data and instructions to the storage system, the at least one input device, and the at least one output device.

[0119] These computational programs (also referred to as programs, software, software applications, or code) include machine instructions for a programmable processor and can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. As used herein, the terms “machine-readable medium” and “computer-readable medium” refer to any computer program product, device, and / or apparatus (e.g., disk, optical disk, memory, programmable logic device (PLD)) used to provide machine instructions and / or data to a programmable processor, including machine-readable media that receive machine instructions as machine-readable signals. The term “machine-readable signal” refers to any signal used to provide machine instructions and / or data to a programmable processor.

[0120] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0121] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0122] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other.

[0123] The technical solution according to the embodiments of this disclosure improves the accuracy of distributed photovoltaic power generation prediction.

[0124] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0125] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for generating power generation data, comprising: Acquire target forecast weather data, wherein the target forecast weather data is used to indicate forecast weather data for a future time period adjacent to the current time; Acquire the first power generation data of distributed photovoltaic power, wherein the first power generation data is used to indicate the maximum power generation data in a first historical time period adjacent to the current time; The second power generation data of the distributed photovoltaic system is obtained based on the ratio of the first power generation data of the distributed photovoltaic system to the reference parameter. The reference parameter is used to indicate the ratio of the first power generation data of the centralized photovoltaic system to the second power generation data of the centralized photovoltaic system. The second power generation data is used to indicate the power generation data at each time point in the historical second time period adjacent to the current time. The length of the historical second time period is the same as that of the future time period. The target predicted weather data and the second power generation data of the distributed photovoltaic system are input into a preset power prediction model to obtain the third power generation data of the distributed photovoltaic system. The third power generation data is used to indicate the power generation data for the future time period. The power prediction model is trained based on the predicted weather data and the second power generation data samples labeled with the third power generation data.

2. The method according to claim 1, wherein, The acquisition of the first power generation data of distributed photovoltaic power includes: Based on the daily performance index data of distributed photovoltaic power generation in the first historical time period adjacent to the current time, a first average value of a preset number of minimum power generation on sunny days and a second average value of a preset number of maximum power generation on non-sunny days are determined. The difference between the first mean and the second mean is determined as the first power generation data of the distributed photovoltaic system.

3. The method according to claim 1, wherein, The acquisition of target forecast weather data includes: Obtain real weather data for the second historical time period adjacent to the current time and first predicted weather data for the future time period adjacent to the current time; The real weather data is input into a preset weather prediction model to obtain second predicted weather data. The preset weather prediction model is trained based on real weather data samples labeled with the second predicted weather data. The first forecast weather data is corrected based on the second forecast weather data to obtain the corrected first forecast weather data. Based on the corrected first forecast weather data, the target forecast weather data is determined.

4. The method according to claim 3, wherein, The corrected first forecast weather data includes at least two weather data points, and the determination of the target forecast weather data based on the corrected first forecast weather data includes: Target forecast weather data is determined based on the at least two weather data points and the weights of each weather data point within those at least two weather data points.

5. The method according to any one of claims 1-4, further comprising: The third power generation data is corrected based on the standard power curve of centralized photovoltaic power generation to obtain the target power generation data.

6. A power generation data generation device, comprising: The first acquisition module is configured to acquire target predicted weather data, wherein the target predicted weather data is used to indicate predicted weather data for a future time period adjacent to the current time. The second acquisition module is configured to acquire the first power generation data of the distributed photovoltaic system, wherein the first power generation data is used to indicate the maximum power generation data in a historical first time period adjacent to the current time. The data calculation module is configured to obtain the second power generation data of the distributed photovoltaic system based on the ratio of the first power generation data of the distributed photovoltaic system to the reference parameter. The reference parameter is used to indicate the ratio of the first power generation data of the centralized photovoltaic system to the second power generation data of the centralized photovoltaic system. The second power generation data is used to indicate the power generation data at each time point in the historical second time period adjacent to the current time. The length of the historical second time period is the same as that of the future time period. The data prediction module is configured to input the target predicted weather data and the second power generation data of the distributed photovoltaic system into a preset power prediction model to obtain the third power generation data of the distributed photovoltaic system. The third power generation data is used to indicate the power generation data for the future time period. The power prediction model is trained based on the predicted weather data and the second power generation data samples labeled with the third power generation data.

7. The apparatus according to claim 6, wherein, The second acquisition module is further configured to: Based on the daily performance index data of distributed photovoltaic power generation in the first historical time period adjacent to the current time, a first average value of a preset number of minimum power generation on sunny days and a second average value of a preset number of maximum power generation on non-sunny days are determined. The difference between the first mean and the second mean is determined as the first power generation data of the distributed photovoltaic system.

8. The apparatus according to claim 7, wherein, The first acquisition module is further configured to: Obtain real weather data for the second historical time period adjacent to the current time and first predicted weather data for the future time period adjacent to the current time; The real weather data is input into a preset weather prediction model to obtain second predicted weather data. The preset weather prediction model is trained based on real weather data samples labeled with the second predicted weather data. The first forecast weather data is corrected based on the second forecast weather data to obtain the corrected first forecast weather data. Based on the corrected first forecast weather data, the target forecast weather data is determined.

9. The apparatus according to claim 8, wherein, The corrected first forecast weather data includes at least two weather data points, and the determination of the target forecast weather data based on the corrected first forecast weather data includes: Target forecast weather data is determined based on the at least two weather data points and the weights of each weather data point within those at least two weather data points.

10. The apparatus according to any one of claims 6-9, wherein the apparatus further comprises: The correction module is configured to correct the third power generation data according to the standard power curve of centralized photovoltaic power generation to obtain the target power generation data.

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

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

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

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