CN115271211A - Power generation data generation method and device - Google Patents
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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 as follows: acquiring target forecast weather data; acquiring first power generation data of distributed photovoltaic; obtaining second power generation data of the distributed photovoltaic according to the ratio of the first power generation data of the distributed photovoltaic to the comparison parameter; and inputting the target predicted weather data and the second power generation data of the distributed photovoltaic into a preset power prediction model to obtain third power generation data of the distributed photovoltaic. The method improves the accuracy of the prediction of the distributed photovoltaic power generation amount.
Description
Technical Field
The disclosure relates to the technical field of new energy, in particular to the technical field of distributed photovoltaic power generation prediction, and particularly relates to a power generation data generation method and device.
Background
With the establishment of the national economic system for green low-carbon cycle development and the establishment of a clean low-carbon safe and efficient energy system, the demand for new energy power generation is higher and higher. At present, large-scale distributed photovoltaic devices are built in many domestic grade markets, and the installation amount is more and more.
In the prior art, the specific power generation data are rarely recorded in a distributed photovoltaic device, and the prediction of the future power generation amount is mainly realized through manual estimation.
Disclosure of Invention
The embodiment of the disclosure provides a power generation data generation method, a power generation data generation device, power generation equipment and a storage medium.
In a first aspect, an embodiment of the present disclosure provides a power generation data generation method, where the method includes: acquiring target forecast weather data; acquiring first power generation data of distributed photovoltaic; obtaining second power generation data of the distributed photovoltaic according to the ratio of the first power generation data of the distributed photovoltaic to the comparison parameter; and inputting the target predicted weather data and the second power generation data of the distributed photovoltaic into a preset power prediction model to obtain third power generation data of the distributed photovoltaic.
In a second aspect, an embodiment of the present disclosure provides a power generation data generation apparatus, including: a first acquisition module configured to acquire target predicted weather data; a second acquisition module configured to acquire first power generation data of the distributed photovoltaics; the data prediction module is configured to input the target predicted weather data and the second power generation data of the distributed photovoltaic into a preset power prediction model to obtain third power generation data of the distributed photovoltaic.
In a third aspect, embodiments of the present disclosure provide an electronic device, which includes one or more processors; a storage device having one or more programs stored thereon, which when executed by the one or more processors, cause the one or more processors to implement the power generation data generation method as any one of the embodiments of the first aspect.
In a fourth aspect, the disclosed embodiments provide a computer readable medium, on which a computer program is stored, which when executed by a processor, implements the power generation data generation method as in any of the embodiments of the first aspect.
In a fifth aspect, the present disclosure provides a computer program product comprising a computer program that, when executed by a processor, implements the power generation data generation method as in any of the embodiments of the first aspect.
The method and the device improve the accuracy of the prediction of the distributed photovoltaic power generation amount.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
FIG. 1 is an exemplary system architecture diagram in which the present disclosure may be applied;
FIG. 2 is a flow diagram of one embodiment of a method of generating power generation data according to the present disclosure;
FIG. 3 is a schematic illustration of an application scenario of a power generation data generation method according to the present disclosure;
FIG. 4 is a flow chart of yet another embodiment of a power generation data generation method according to the present disclosure;
FIG. 5 is a schematic diagram of one embodiment of a power generation data generation apparatus according to the present disclosure;
FIG. 6 is a schematic block diagram of a computer system suitable for use in implementing an electronic device of an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
It should be noted that, in the present disclosure, the embodiments and features of the embodiments may be combined with each other without conflict. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
FIG. 1 illustrates an exemplary system architecture 100 to which embodiments of the disclosed power generation data generation methods may be applied.
As shown in fig. 1, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may be power generation amount prediction type applications, communication type applications, and the like.
The terminal apparatuses 101, 102, and 103 may be hardware or software. When the terminal devices 101, 102, 103 are hardware, they may be various electronic devices having a display screen, including but not limited to a mobile phone and a notebook computer. When the terminal apparatuses 101, 102, 103 are software, they can be installed in the electronic apparatuses listed above. It may be implemented as a plurality of pieces of software or software modules (for example, to provide a power generation data generation service), or may be implemented as a single piece of software or software module. And is not particularly limited herein.
The server 105 may be a server that provides various services, for example, to acquire to-be-processed data including: acquiring target forecast weather data; acquiring first power generation data of distributed photovoltaic; and obtaining second power generation data of the distributed photovoltaic according to the ratio of the first power generation data of the centralized photovoltaic to the second power generation data of the centralized photovoltaic and the first power generation data of the distributed photovoltaic, and inputting the target predicted weather data and the second power generation data of the distributed photovoltaic into a preset power prediction model to obtain third power generation data of the distributed photovoltaic.
The server 105 may be hardware or software. When the server 105 is hardware, it may be implemented as a distributed server cluster composed of a plurality of servers, or may be implemented as a single server. When the server is software, it may be implemented as a plurality of pieces of software or software modules (for example, for providing a power generation data generation service), or may be implemented as a single piece of software or software module. And is not particularly limited herein.
It should be noted that the power generation data generation method provided by the embodiment of the present disclosure may be executed by the server 105, by the terminal devices 101, 102, and 103, and may also be executed by the server 105 and the terminal devices 101, 102, and 103 in cooperation with each other. Accordingly, each part (for example, each unit, sub-unit, module, sub-module) included in the download data generation apparatus may be provided entirely in the server 105, may be provided entirely in the terminal devices 101, 102, and 103, or may be provided in the server 105 and the terminal devices 101, 102, and 103, respectively.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
FIG. 2 shows a flow diagram 200 of an embodiment of a method of generating power generation data. The power generation data generation method includes the steps of:
In this embodiment, the execution subject (e.g., the server 105 or the terminal devices 101, 102, 103 in fig. 1) may obtain the target predicted weather data locally or at a remote server storing the 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.
Here, the target predicted weather data may be weather data predicted by weather forecast, or may be weather data obtained by correcting weather data predicted by weather forecast, which is not limited in the present application.
The target predicted weather data may include a plurality of items of weather data that affect photovoltaic power generation, such as cloud cover, temperature, humidity, wind speed, irradiance, and the like.
In addition, the target forecast weather data may further include weights corresponding to the weather data, where the weights corresponding to the weather data may be determined according to the degree of influence of the weather data on the photovoltaic power generation.
In some alternatives, obtaining target predicted weather data includes: acquiring 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; 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; based on the corrected first predicted weather data, target predicted weather data is determined.
In this implementation, the execution subject may first acquire real weather data of a historical second time period adjacent to the current time and first predicted weather data adjacent to the current time. Wherein the first predicted weather data is typically used for weather data indicative of a weather forecast prediction.
Here, the historical second time period is generally equal in length to the future time period. The lengths of the historical time period and the future time period may be set according to experience and actual requirements, for example, 1 day, 3 days, a month, and the like, and the present application does not limit the lengths.
Specifically, the real weather data is weather data of historical 24 hours adjacent to the current time, and the first predicted weather data is weather data of future 24 hours adjacent to the current time.
Further, the execution subject may input the real weather data into a preset weather prediction model to obtain second predicted weather data, where the preset weather prediction model is obtained by training based on a sample of the real weather data marked with the second predicted weather.
The preset weather prediction model may be a model for processing a sequence modeling task in the prior art or future development technology, for example, a model based on LSTM (Long Short-Term Memory Network), a model based on TCN (Temporal Convolutional Network), and the like, which is not limited in this application.
After the execution main body obtains the second predicted weather data, the execution main body can correct the first predicted weather data according to the second predicted weather data to obtain corrected first predicted weather data, and determines target predicted weather data based on the corrected first predicted weather data.
The implementation mode comprises the steps that 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 are obtained; 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 forecast weather data based on the corrected first forecast weather data, thereby effectively improving the accuracy of the determined target forecast weather data.
In some alternative ways, determining the target predicted weather data based on the corrected first predicted weather data includes: and determining target forecast weather data according to the at least two items of weather data and the weight of each item of weather data in the at least two items of weather data.
In this implementation manner, the first predicted weather data includes at least two weather data, for example, temperature, humidity, and the like, and after the execution main body obtains the at least two weather data, the execution main body may further obtain weights corresponding to the weather data, and determine the target predicted weather data according to the at least two weather data and the weights of the weather data in the at least two weather data.
The weight of each item of weather data can be determined based on the influence degree of each item of weather data on photovoltaic power generation.
Specifically, the at least two items of weather data include: temperature, irradiance, cloud cover, humidity, the weight that each weather data corresponds respectively is: 0.8, 0.7, 0.5 and 0.4, the executive body can determine the target forecast weather data according to the at least two weather data and the weight of each weather data in the at least two weather data.
According to the implementation mode, the target forecast weather data is determined according to the at least two weather data and the weight of each weather data in the at least two weather data, the target forecast weather data and the second power generation data are input into the preset power forecast model, the third power generation data are obtained, the proportion of each weather data in the target forecast weather data is fully considered, and the accuracy of the determined second wind power generation data is further improved.
In some alternatives, the weather prediction model is a model based on a TCN network.
In this implementation, the executive agent may input the real weather data into the model based on the TCN network to obtain the second predicted weather data.
The TCN network can receive an input sequence with any length as an input and simultaneously map the input sequence into an output sequence with equal length, can realize large-scale parallel processing, and has the advantages of high training speed and less memory occupation.
According to the method and the device, the second predicted weather data can be obtained by inputting the real weather data into the model based on the TCN network, and the efficiency of generating the second predicted weather data is improved.
In this embodiment, the executing entity may obtain the first power generation data of the distributed photovoltaic locally or at a remote server storing the first power generation data of the distributed photovoltaic.
The first power generation data are used for indicating the maximum power generation data, namely the loading amount, in the historical first time period adjacent to the current time.
And step 203, obtaining second power generation data of the distributed photovoltaic according to the ratio of the first power generation data of the distributed photovoltaic to the comparison parameter.
In this embodiment, the executing entity may further obtain the first power generation data of the centralized photovoltaic and the second power generation data of the centralized photovoltaic, and obtain the second power generation data of the distributed photovoltaic according to a ratio of the first power generation data of the centralized photovoltaic to the second power generation data of the centralized photovoltaic, that is, a comparison parameter, and the first power generation data of the distributed photovoltaic.
Specifically, it can be represented by the following formula:
the control parameter = first power generation data of concentrated photovoltaic/second power generation data of concentrated photovoltaic = first power generation data of distributed photovoltaic/second power generation data of distributed photovoltaic.
The second power generation data is used for indicating power generation data of each time point in a historical second time period adjacent to the current time.
Specifically, if the first power generation data of the concentrated photovoltaic is 2000W, the second power generation data of the concentrated photovoltaic is 9 hr-800W, 12 hr-800W, 15 hr-600W, 18 hr-600W, 21 hr-800W, 24-1000W, and the first power generation data of the distributed photovoltaic is 1000W, the second power generation data of the distributed photovoltaic is 9 hr-400W, 12 hr-400W, 15 hr-300W, 18 hr-300W, 21 hr-400W, 24-500W.
And 204, inputting the target predicted weather data and the second power generation data of the distributed photovoltaic into a preset power prediction model to obtain third power generation data of the distributed photovoltaic.
In this embodiment, after obtaining the target predicted weather data and the second power generation data of the distributed photovoltaic, the execution subject may input the target predicted weather data and the second power generation data into a preset power prediction model to obtain third power generation data.
The third power generation data is used to indicate the power generation data of the future time period, that is, the power generation data at each time point in the future time period.
Here, the power prediction model is trained based on the predicted weather data and the second power generation data sample, which are labeled with the third power generation data.
Specifically, the target predicted weather data is weather data of a future 24-hour adjacent to the current time, and the second power generation data is power generation data at each time point in a historical 24-hour adjacent to the current time, for example, 9 hour-800W, 12 hour-800W, 15 hour-600W, 18 hour-600W, 21 hour-800W, 24-1000W. The execution main body inputs the target predicted weather data and the second power generation data into a preset power prediction model to obtain third power generation data, wherein the third power generation data is power generation data of each time point in the next 24 hours adjacent to the current time, such as 9 hours-600W, 12 hours-800W, 15 hours-600W, 18 hours-600W, 21 hours-600W and 24-1000W.
The power prediction model may be a machine learning model in the prior art or in the future development technology, for example, an XGBoost model, a lightGBM model, and the like, which is not limited in this application.
In some alternatives, the power prediction model is a lightGBM model.
In this implementation, the executive agent may input the target predicted weather data and the second power generation data into the lightGBM model to obtain third power generation data.
The principle of the method is that training integration is performed by using a base classifier (Decision Tree) to obtain an optimal model, and the method has the advantages of being fast in training speed and small in calculation amount.
According to the implementation mode, the third power generation data is obtained by inputting the target forecast weather data and the second power generation data into the lightGBM model, and the efficiency of generating the third power generation data is improved.
In some optional ways, the method further comprises: and correcting the third power generation data according to the standard power curve of the centralized photovoltaic system to obtain target power generation data.
In this implementation, after acquiring the third power generation data of the distributed photovoltaics, the execution subject may further acquire a standard power curve of the centralized photovoltaics, where the standard power curve may indicate a maximum output power and a minimum output power that can be output by the centralized photovoltaics. The execution subject may correct the third power generation data according to the maximum output power and the minimum output power indicated in the standard power curve to obtain corrected third power generation data, and determine the corrected third power generation data as the target power generation data.
Specifically, the third power generation data is: since the maximum output power is 1000W as seen from the standard power curve, the maximum output power is 1000W at 9 th-500W, 12 th-600W, 15 th-800W, 18 th-1000W, 21 th-1000W, and 24 th-1200W, the output power at 24 st 1200W is an abnormal value, and the output power at 24 st can be corrected to 1000W from the standard power curve.
According to the implementation mode, the third power generation data is corrected according to the standard power curve of the centralized photovoltaic system to obtain the target power generation data, the generated power generation data is effectively prevented from being too large or too small, and the accuracy of the generated power generation data is further improved.
With continued reference to fig. 3, fig. 3 is a schematic diagram of an application scenario of the power generation data generation method according to the present embodiment. The execution subject 301 acquires target predicted weather data 302, where the target predicted weather data 302 is used to indicate predicted weather data for a future time period adjacent to the current time, for example, weather data of a future day predicted by weather forecast; acquiring first power generation data 303 of distributed photovoltaic, wherein the first power generation data is used for indicating maximum power generation data in a historical first time period adjacent to the current time; obtaining second power generation data 304 of the distributed photovoltaic according to a ratio of the first power generation data of the distributed photovoltaic to a comparison parameter, where the comparison parameter indicates a ratio of the first power generation data of the centralized photovoltaic to the second power generation data of the centralized photovoltaic, where the second power generation data is used to indicate power generation data of each time point in a historical second time period adjacent to the current time, and the historical second time period is equal to the future time period in length, for example, the power generation data of the distributed photovoltaic at each time point on the current day; inputting the target predicted weather data 302 and the second power generation data 304 of the distributed photovoltaic into a preset power prediction model 305 to obtain third power generation data 306 of the distributed photovoltaic, wherein the third power generation data is used for indicating power generation data of a future time period, such as power generation data of the distributed photovoltaic in a future day, and the power prediction model is obtained by training based on the predicted weather data and the second power generation data which are marked with the third power generation data.
According to the power generation data generation method provided by the embodiment of the disclosure, target forecast weather data is obtained; acquiring first power generation data of distributed photovoltaic; according to the ratio of the first power generation data of the distributed photovoltaic to the comparison parameter, second power generation data of the distributed photovoltaic is obtained, the target forecast weather data and the second power generation data of the distributed photovoltaic are input into a preset power forecasting model, third power generation data of the distributed photovoltaic are obtained, and the accuracy of the distributed photovoltaic power generation quantity forecasting is improved.
With further reference to FIG. 4, a flow 400 of yet another embodiment of the power generation data generation method shown in FIG. 2 is illustrated. In this embodiment, the flow 400 of the power generation data generation method may include the following steps:
In this embodiment, details of implementation and technical effects of step 401 may refer to the description of step 201, and are not described herein again.
In this embodiment, the execution main body may first acquire performance index data of the distributed photovoltaic in the historical first time period adjacent to the current time, and remove the abnormal day according to the performance index data to obtain data after removing the abnormal day.
The performance indicator data may include: mean, variance, maximum, minimum, etc. of the power generation data.
Further, the execution main body can determine a preset number of fine days, namely days in which the distributed photovoltaic can normally generate power, from the data after the abnormal days are eliminated, and determine a first average value of the minimum value of the power generation amount of the preset number of fine days according to the minimum value of the power generation amount of the preset number of fine days each day.
Here, the minimum value of the power generation amount on each fine day may be determined according to the power generation amount on the whole day on each fine day, or may be determined according to the power generation amount in a preset time period on each fine day, which is not limited in the present application.
The preset time period may be a time period of distributed photovoltaic centralized power generation, for example, 11 hours to 14 hours, 12 hours to 14 hours, and the like, and may be specifically set according to an actual demand.
Specifically, the execution subject may first obtain performance index data of the distributed photovoltaic in a historical first time period, for example, 20 days each day, and reject the abnormal days according to the performance index data of each day, to obtain data after rejecting the abnormal days, for example, performance index data of the distributed photovoltaic in 16 days. Further, the execution subject determines a preset number of sunny days, for example, 5 sunny days, and minimum values of the power generation amounts in 11 hours to 14 days each day, for example, 500W, 600W, 400W, 300W, and 200W, respectively, from the performance table data of the distributed photovoltaic devices in the remaining 16 days, and then calculates an average value, that is, a first average value, 400W, according to the minimum value of the power generation amounts in 5 days.
Further, the execution main body can determine a preset number of non-fine days, namely days in which the distributed photovoltaic cannot normally generate power, such as cloudy days, rainy days and the like, from the data after the abnormal days are removed, and determine a second average value of the maximum value of the generated energy of each day of the preset number of non-fine days according to the maximum value of the generated energy of each day of the preset number of non-fine days.
Specifically, the execution subject may first obtain performance index data of the distributed photovoltaic in a historical first time period, for example, 20 days each day, and reject the abnormal days according to the performance index data of each day to obtain data after rejecting the abnormal days, for example, performance index data of the distributed photovoltaic in 16 days.
Further, the execution subject determines a preset number of non-fine days, for example, 5 non-fine days, from the performance table data of the distributed photovoltaics for the remaining 16 days, and maximum values of the power generation amounts during 11 hours to 14 days, for example, 100W, 50W, 40W, 60W, and 50W, and further calculates an average value, that is, a second average value, 60W, according to the maximum value of the power generation amounts for the 5 days.
And step 403, determining the difference value of the first average value and the second average value as first power generation data of the distributed photovoltaic.
In this embodiment, after the execution subject acquires the first mean value and the second mean value, the execution subject may directly determine a difference value between the first mean value and the second mean value as the first power generation data of the distributed photovoltaic.
Specifically, the first average value is 400W, the second average value is 60W, and the difference between the first average value and the second average value is 340W, that is, the first power generation data is 340W.
And step 404, obtaining second power generation data of the distributed photovoltaic according to the ratio of the first power generation data of the distributed photovoltaic to the comparison parameter.
In this embodiment, details of implementation and technical effects of step 404 may refer to the description of step 203, and are not described herein again.
In this embodiment, details and technical effects of the implementation of step 405 may refer to the description of step 204, and are not described herein again.
Compared with the embodiment shown in fig. 2, the embodiment of the disclosure highlights performance index data of distributed photovoltaics in a historical first time period every day, determines a first average value of the minimum values of the power generation amounts of a preset number of fine days and a second average value of the maximum values of the power generation amounts of a preset number of non-fine days, determines a difference value of the first average value and the second average value as first power generation data of the distributed photovoltaics, further obtains second power generation data of the distributed photovoltaics according to a ratio of the first power generation data of the distributed photovoltaics to a comparison parameter, inputs the target predicted weather data and the second power generation data of the distributed photovoltaics into a preset power prediction model, obtains third power generation data of the distributed photovoltaics, improves accuracy of the determined second power generation data of the distributed photovoltaics, and further improves accuracy of the determined predicted power generation data of the distributed photovoltaics.
With further reference to fig. 5, as an implementation of the methods shown in the above figures, the present disclosure provides an embodiment of a power generation data generation apparatus, which corresponds to the method embodiment shown in fig. 1, and which may be applied in various electronic devices.
As shown in fig. 5, the power generation data generation device 500 of the present embodiment includes: a first acquisition module 501, a second acquisition module 502, a data calculation module 503, and a data prediction module 504.
The first obtaining module 501 may be configured to obtain target predicted weather data.
The second obtaining module 502 may be configured to obtain first power generation data of the distributed photovoltaic.
The data calculation module 503 may be configured to obtain second power generation data of the distributed photovoltaic according to a ratio of the first power generation data of the distributed photovoltaic to the comparison parameter.
The data prediction module 504 may be configured to input the target predicted weather data and the second power generation data of the distributed photovoltaic into a preset power prediction model, so as to obtain third power generation data of the distributed photovoltaic.
In some alternatives of this embodiment, the second obtaining module is further configured to: determining a first average value of the minimum values of the generated energy on a preset number of sunny days and a second average value of the maximum values of the generated energy on a preset number of non-sunny days based on performance index data of distributed photovoltaic in a historical first time period adjacent to the current time every day; and determining the difference value of the first average value and the second average value as first power generation data of the distributed photovoltaic.
In some alternatives of the embodiment, the first obtaining module is further configured to obtain 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; 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; based on the corrected first predicted weather data, target predicted weather data is determined.
In some optional manners of this embodiment, determining the target predicted weather data based on the corrected first predicted weather data includes: and determining target forecast weather data according to the at least two items of weather data and the weight of each item of weather data in the at least two items of weather data.
In some optional manners of this embodiment, the apparatus further includes: a correction module configured to correct the third power generation data according to a standard power curve of the concentrated photovoltaic, resulting in target power generation data.
In the technical scheme of the disclosure, the acquisition, storage, application and the like of the personal information of the related user all accord with the regulations of related laws and regulations, and do not violate the customs of public sequences.
The present disclosure also provides an electronic device, a readable storage medium, and a computer program product according to embodiments of the present disclosure.
As shown in fig. 6, is a block diagram of an electronic device of a power generation data generation method according to an embodiment of the present disclosure.
600 is a block diagram of an electronic device of a power generation data generation method according to an embodiment of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 6, the electronic apparatus includes: one or more processors 601, memory 602, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions for execution within the electronic device, including instructions stored in or on the memory to display graphical information of a GUI on an external input/output apparatus (such as a display device coupled to the interface). In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories and multiple memories, as desired. Also, multiple electronic devices may be connected, with each device providing portions of the necessary operations (e.g., as a server array, a group of blade servers, or a multi-processor system). In fig. 6, one processor 601 is taken as an example.
The memory 602 is a non-transitory computer readable storage medium provided by the present disclosure. Wherein the memory stores instructions executable by at least one processor to cause the at least one processor to perform a power generation data generation method provided by the present disclosure. The non-transitory computer-readable storage medium of the present disclosure stores computer instructions for causing a computer to execute the power generation data generation method provided by the present disclosure.
The memory 602, which is a non-transitory computer-readable storage medium, may be used to store non-transitory software programs, non-transitory computer-executable programs, and modules, such as program instructions/modules corresponding to the power generation data generation method in the embodiments of the present disclosure (for example, the first acquisition module 501, the second acquisition module 502, the data calculation module 503, and the data prediction module 504 shown in fig. 5). The processor 601 executes various functional applications of the server and data processing by running non-transitory software programs, instructions, and modules stored in the memory 602, that is, implements the power generation data generation method in the above-described method embodiments.
The memory 602 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created by use of the electronic device for face tracking, and the like. Further, the memory 602 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 602 may optionally include memory remotely located from the processor 601, which may be connected to lane line detection electronics over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The electronic device of the power generation data generation method may further include: an input device 603 and an output device 604. The processor 601, the memory 602, the input device 603 and the output device 604 may be connected by a bus or other means, and fig. 6 illustrates the connection by a bus as an example.
The input device 603 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the lane line detecting electronic apparatus, such as a touch screen, a keypad, a mouse, a track pad, a touch pad, a pointing stick, one or more mouse buttons, a track ball, a joystick, or other input devices. The output devices 604 may include a display device, auxiliary lighting devices (e.g., LEDs), and tactile feedback devices (e.g., vibrating motors), among others. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device can be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, integrated circuitry, application specific ASICs (application specific integrated circuits), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
These computer programs (also known as programs, software applications, or code) include machine instructions for a programmable processor, and may 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, apparatus, and/or device (e.g., magnetic discs, optical disks, memory, programmable Logic Devices (PLDs)) used to provide machine instructions and/or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a 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 may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
According to the technical scheme of the embodiment of the disclosure, the accuracy of the prediction of the distributed photovoltaic power generation amount is improved.
It should be understood that various forms of the flows shown above, reordering, adding or deleting steps, may be used. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.
Claims (13)
1. A power generation data generation method, comprising:
acquiring target predicted weather data, wherein the target predicted weather data is used for indicating predicted weather data of a future time period adjacent to the current moment;
acquiring first power generation data of distributed photovoltaic, wherein the first power generation data are used for indicating the maximum power generation data in a historical first time period adjacent to the current moment;
obtaining second power generation data of the distributed photovoltaic according to a ratio of the first power generation data of the distributed photovoltaic to a comparison parameter, wherein the comparison parameter is used for indicating the ratio of the first power generation data of the centralized photovoltaic to the second power generation data of the centralized photovoltaic, the second power generation data is used for indicating power generation data of each time point in a historical second time period adjacent to the current time, and the historical second time period is equal to the future time period in length;
and inputting the target predicted weather data and the second power generation data of the distributed photovoltaic into a preset power prediction model to obtain third power generation data of the distributed photovoltaic, wherein the third power generation data is used for indicating the power generation data of the future time period, and the power prediction model is obtained by training based on the predicted weather data marked with the third power generation data and the second power generation data sample.
2. The method of claim 1, wherein the acquiring first power generation data for distributed photovoltaics comprises:
determining a first average value of the minimum values of the generated energy on a preset number of sunny days and a second average value of the maximum values of the generated energy on a preset number of non-sunny days based on performance index data of distributed photovoltaic in a historical first time period adjacent to the current moment every day;
and determining the difference value of the first average value and the second average value as first power generation data of the distributed photovoltaic.
3. The method of claim 1, wherein said obtaining target predicted weather data comprises:
acquiring 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;
inputting the real weather data into a preset weather prediction model to obtain second predicted weather data, wherein the preset weather prediction model is obtained by training based on a real weather data sample marked with the 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 target predicted weather data based on the corrected first predicted weather data.
4. The method of claim 3, wherein the corrected first predicted weather data comprises at least two weather data, and the determining the target predicted weather data based on the corrected first predicted weather data comprises:
and determining target forecast weather data according to the at least two items of weather data and the weight of each item of weather data in the at least two items of weather data.
5. The method according to any one of claims 1-4, further comprising:
and correcting the third power generation data according to a standard power curve of the centralized photovoltaic system to obtain target power generation data.
6. A power generation data generation device comprising:
a first obtaining module configured to obtain target predicted weather data indicating predicted weather data for a future time period adjacent to a current time;
the system comprises a second acquisition module, a first acquisition module and a second acquisition module, wherein the second acquisition module is configured to acquire first power generation data of distributed photovoltaic, and the first power generation data is used for indicating maximum power generation data in a historical first time period adjacent to the current time;
the data calculation module is configured to obtain second power generation data of the distributed photovoltaic according to a ratio of first power generation data of the distributed photovoltaic to a comparison parameter, wherein the comparison parameter is used for indicating the ratio of the first power generation data of the centralized photovoltaic to the second power generation data of the centralized photovoltaic, the second power generation data is used for indicating power generation data of each time point in a historical second time period adjacent to the current time, and the historical second time period is equal to the future time period in length;
the data prediction module is configured to input the target predicted weather data and the second power generation data of the distributed photovoltaic into a preset power prediction model to obtain third power generation data of the distributed photovoltaic, the third power generation data is used for indicating the power generation data of the future time period, and the power prediction model is obtained based on a predicted weather data marked with the third power generation data and a second power generation data sample training.
7. The apparatus of claim 6, wherein the second acquisition module is further configured to:
determining a first average value of the minimum values of the generated energy on a preset number of sunny days and a second average value of the maximum values of the generated energy on a preset number of non-sunny days based on performance index data of distributed photovoltaic in a historical first time period adjacent to the current time every day;
and determining the difference value of the first average value and the second average value as first power generation data of the distributed photovoltaic.
8. The apparatus of claim 7, wherein the first acquisition module is further configured to:
acquiring 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;
inputting the real weather data into a preset weather prediction model to obtain second predicted weather data, wherein the preset weather prediction model is obtained by training based on a real weather data sample marked with the 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 target predicted weather data based on the corrected first predicted weather data.
9. The apparatus of claim 8, wherein the corrected first predicted weather data comprises at least two weather data, and the determining the target predicted weather data based on the corrected first predicted weather data comprises:
and determining target forecast weather data according to the at least two items of weather data and the weight of each item of weather data in the at least two items of weather data.
10. The apparatus of any of claims 6-9, further comprising:
a correction module configured to correct the third power generation data according to a standard power curve of the concentrated photovoltaic, resulting in target power generation data.
11. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein, the first and the second end of the pipe are connected with each other,
the memory is stored with instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-5.
12. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-5.
13. A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-5.
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