CN115882451A - Method, device and equipment for predicting generated power of new energy power station - Google Patents
Method, device and equipment for predicting generated power of new energy power station Download PDFInfo
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Abstract
The application discloses a method, a device and equipment for predicting generated power of a new energy power station. Wherein, the method comprises the following steps: acquiring meteorological data of a target area where a target power station is located in a target time period and predicted power generation power corresponding to the target power station; acquiring a target influence factor of meteorological data on actual generated power, wherein the target influence factor is used for representing the influence degree of the meteorological data on the actual generated power; and adjusting the predicted generating power according to the target influence factor to obtain a target predicted power value. The method and the device solve the technical problems that in the process of predicting the generated power of the power station in the related technology, the influence of various meteorological data on the generated power of the power station is ignored, the prediction result is inaccurate, and the deviation from the actual generated power is large.
Description
Technical Field
The application relates to the field of power station power prediction, in particular to a method, a device and equipment for predicting the generated power of a new energy power station.
Background
In recent years, disasters and weather frequently occur in various regions of the world, typhoons, sand storms, solar eclipses, snowstorms and the like have great influence on the accuracy of wind power and photovoltaic power generation power prediction, so that the generated energy and assets of a new energy power station are damaged, and the stability of a power grid is also influenced. At present, the early warning of the disaster weather has timeliness, and is generally divided into blue early warning, yellow early warning, orange early warning and red early warning, so that the influence of various disaster weathers on the prediction of wind power and photovoltaic power generation power is difficult to quantify, and in the related technology, in the process of predicting the power generation power of a new energy power station, the influence of various meteorological data on the power generation power of the power station is often ignored, so that the predicted power generation power is inaccurate, and the deviation from the actual power generation power is large.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the application provides a method, a device and equipment for predicting the generated power of a new energy power station, and aims to at least solve the technical problems that in the process of predicting the generated power of the power station in the related technology, the influence of various meteorological data on the generated power of the power station is ignored, the prediction result is inaccurate, and the deviation from the actual generated power is large.
According to an aspect of an embodiment of the present application, there is provided a method for predicting generated power of a new energy power station, including: acquiring meteorological data of a target area where a target power station is located in a target time period and predicted power generation power corresponding to the target power station; acquiring a target influence factor of meteorological data on actual generated power, wherein the target influence factor is used for representing the influence degree of the meteorological data on the actual generated power; and adjusting the predicted generating power according to the target influence factor to obtain a target predicted power value.
Optionally, adjusting the predicted power generation power according to the target impact factor to obtain a target predicted power value, including: and determining a target predicted power value according to the product of the target influence factor and the predicted power generation power.
Optionally, obtaining a target influence factor of the meteorological data on the actual generated power includes: acquiring influence factors of each forecast grade on actual power generation in a historical time period; and determining a target forecast grade to which the meteorological data belongs, and acquiring an influence factor corresponding to the target forecast grade as a target influence factor.
Optionally, obtaining a target influence factor of the meteorological data on the actual generated power includes: analyzing the meteorological data to obtain each first weather type included in the meteorological data; and determining each first influence factor corresponding to each first weather type, and summing the first influence factors to obtain a target influence factor.
Optionally, before obtaining the target influence factor of the meteorological data on the actual generated power, the method further comprises: acquiring the power generation type of the power station, wherein the power generation type at least comprises one of the following types: light energy power generation, and wind energy power generation; the method comprises the steps of dividing different weather types according to power generation types of power stations, and clustering the different weather types into two main categories, wherein the first main category is the weather type which has influence on the power generation power of the power station adopting the light energy power generation, and the second main category is the weather type which has influence on the power generation power of the power station adopting the wind energy power generation.
Optionally, obtaining a target influence factor of the meteorological data on the actual generated power includes: analyzing the meteorological data to obtain each first weather type included in the meteorological data; determining a target power generation type of a target power station, screening each first weather type according to the target power generation type, eliminating weather types which have no influence on the power generation power of the target power station, and obtaining each second weather type left after elimination; and acquiring each second influence factor corresponding to each second weather type, and obtaining the target influence factor based on the second influence factors.
Optionally, obtaining the target influence factor based on the second influence factor includes: acquiring each weight value corresponding to each second influence factor under the condition that the power generation type of the target power station is the target power generation type; and determining the product of each second influence factor and the weight value corresponding to each second influence factor, and determining the target influence factor according to the product.
According to another aspect of the embodiments of the present application, there is also provided a generated power prediction apparatus of a new energy power station, including: the first acquisition module is used for acquiring meteorological data of a target area where a target power station is located in a target time period and predicted power generation power corresponding to the target power station; the second acquisition module is used for acquiring a target influence factor of the meteorological data on the actual generated power, wherein the target influence factor is used for representing the influence degree of the meteorological data on the actual generated power; and the adjusting module is used for adjusting the predicted generating power according to the target influence factor to obtain a target predicted power value.
According to another aspect of the embodiments of the present application, there is also provided a non-volatile storage medium, where the storage medium includes a stored program, and when the program runs, the apparatus where the storage medium is located is controlled to execute any one of the methods for predicting the generated power of the new energy power station.
According to another aspect of the embodiments of the present application, there is also provided a generated power prediction apparatus of a new energy power station, including: a processor; a memory for storing processor-executable instructions; the processor is configured to execute the instructions to realize the generated power prediction method of any new energy power station.
In the embodiment of the application, a mode of quantifying the influence degree of various meteorological data on the generated power to determine an influence factor is adopted, the meteorological data of a target area where a target power station is located in a target time period and the current predicted generated power are obtained, then, the target influence factor of the meteorological data on the actual generated power is obtained, the predicted generated power is adjusted based on the target influence factor to obtain a target predicted power value, and the purpose of adjusting the predicted generated power based on the quantified influence factor is achieved, so that the generated power of the power station is more accurately predicted, the technical effects of improving the accuracy and credibility of power station power prediction are achieved, and the technical problems that in the process of predicting the generated power of the power station in the related technology, the influence of various meteorological data on the generated power of the power station is neglected, the prediction result is inaccurate, and the deviation from the actual generated power is larger are solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a schematic flow diagram of a method for predicting power generation of a new energy power plant according to an embodiment of the present application;
FIG. 2 is a schematic flowchart of a process for obtaining a target influence factor of meteorological data on actual generated power in an embodiment of the present application;
FIG. 3 is a schematic diagram of a power generation flow framework of a new energy power plant in an embodiment of the present application;
fig. 4 is a schematic structural diagram of an alternative generated power prediction device of a new energy power station according to an embodiment of the present application;
fig. 5 is a schematic block diagram of an alternative generated power prediction device of a new energy power plant according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
According to an embodiment of the present application, there is provided an embodiment of a method for predicting power generation of a new energy power plant, where the steps illustrated in the flowchart of the drawings may be executed in a computer system, such as a set of computer executable instructions, and where a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be executed in an order different from that shown.
Fig. 1 is a method for predicting power generation of a new energy power station according to an embodiment of the present application, and as shown in fig. 1, the method includes the following steps:
step S102, acquiring meteorological data of a target area where a target power station is located in a target time period and predicted power generation power corresponding to the target power station;
it should be noted that the target time interval is a current time interval, for example, the current time interval is 12/2022 to 12/13/2022, and the meteorological data from 12 # to 13 # is acquired. For example, 1 month 2023 to 4 months 2023. For another example, the current time period may also be 2023 years to 2024 years. It is to be understood that the above target time interval may be further refined, for example, the target time interval may be divided by a unit hour, and the granularity of the division of the above target time interval is not particularly limited in the present application.
Step S104, acquiring a target influence factor of meteorological data on actual generated power, wherein the target influence factor is used for representing the influence degree of the meteorological data on the actual generated power;
and S106, adjusting the predicted generating power according to the target influence factor to obtain a target predicted power value.
According to the method for predicting the generated power of the new energy power station, a mode of quantifying the influence degree of various meteorological data on the generated power is adopted to determine an influence factor, the meteorological data of a target area where a target power station is located in a target time period and the current predicted generated power are obtained, then a target influence factor of the meteorological data on the actual generated power is obtained, the predicted generated power is adjusted based on the target influence factor to obtain a target predicted power value, and the purpose of adjusting the predicted generated power based on the quantified influence factor is achieved, so that the generated power of the power station is predicted more accurately, the technical effects of improving the accuracy and credibility of power station power prediction are achieved, and the technical problems that the influence of various meteorological data on the generated power of the power station is neglected in the process of predicting the generated power of the power station in the related technology, the prediction result is inaccurate, and the deviation of the actual generated power is large are solved.
The generated power prediction method of the new energy power station can be used for predicting the generated power of new energy single stations such as wind power and photovoltaic, and is also suitable for predicting the generated power of new energy such as regional wind power and photovoltaic.
In some embodiments of the present application, the predicted power generation power is adjusted according to the target influence factor to obtain a target predicted power value, which may be determined by the following steps: the target predicted power value is determined according to the product of the target influence factor and the predicted generated power, and specifically, the target predicted power value can be obtained by subtracting the product from the predicted generated power. For example, the predicted generated power is 1000Wh, the target impact factor is 10%, and the target predicted power value may be 1000-1000 × 10% =900Wh.
In some optional embodiments of the application, the target influence factor of the meteorological data on the actual generated power is obtained, and the influence factor of each forecast grade on the actual generated power in a historical period can also be obtained; and determining a target forecast grade to which the meteorological data belongs, and acquiring an influence factor corresponding to the target forecast grade as a target influence factor. For example, the forecast levels are a blue early warning, a yellow early warning, an orange early warning and a red early warning, respectively, wherein the influence factor of the blue early warning on the actual power generation is 10%, the influence factor of the yellow early warning on the actual power generation is 20%, the influence factor of the orange early warning on the actual power generation is 25%, the influence factor of the red early warning on the actual power generation is 30%, and the target forecast level to which the current meteorological data belongs is the orange early warning, so that the target influence factor can be determined to be 30%.
As another optional embodiment, the target influence factor of the meteorological data on the actual power generation power is obtained, and the meteorological data may also be analyzed to obtain each first weather type included in the meteorological data; and determining each first influence factor corresponding to each first weather type, and summing the first influence factors to obtain a target influence factor. For example, if the weather data of the current time period includes weather types of strong wind, heavy rain and thunder, the first impact factors corresponding to the strong wind, heavy rain and thunder can be determined, and if the first impact factors corresponding to the strong wind, heavy rain and thunder are-30%, 10% and 10%, respectively, the target impact factor can be determined as [ (-30%) +10% +10% ] = -10%.
It should be noted that a positive value of the weight value indicates that the influence factor has a negative influence on the generated power of the plant, and a negative value indicates that the influence factor has a positive promotion effect on the generated power of the plant, for example, when the predicted generated power is 1000Wh, for the target influence factor [ (-30%) +10% +10% ] -10%, the target predicted power value is 1000-1000 × (-10%) =1100Wh.
In some embodiments of the application, in order to more accurately describe the influence of different weather types on different power stations, for example, in the same strong wind weather, the influence on the generated power of a photovoltaic power station is small, the power generation benefit of a wind power station is promoted, the influence is large, and the power generation type of the power station can be obtained before the target influence factor of meteorological data on the actual generated power is obtained; the method comprises the steps of dividing different weather types according to power generation types of power stations, and clustering the different weather types into two main categories, wherein the first main category is the weather type which has influence on the power generation power of the power station adopting the light energy power generation, and the second main category is the weather type which has influence on the power generation power of the power station adopting the wind energy power generation.
It should be noted that the power generation types include, but are not limited to: light energy power generation, wind energy power generation, hydropower stations and the like.
FIG. 2 is a schematic flow chart of obtaining a target influence factor of meteorological data on actual generated power in some embodiments of the present application, and as shown in FIG. 2, the process may be implemented by:
s202, analyzing the meteorological data to obtain each first weather type included in the meteorological data;
s204, determining a target power generation type of the target power station, screening each first weather type according to the target power generation type, and eliminating weather types which have no influence on the power generation power of the target power station in the process of eliminating, so as to obtain each second weather type which remains after elimination;
for example, the first weather type included in the meteorological data includes high temperature, sand and dust and strong wind, the target power generation type of the target power station is photovoltaic power generation, and the weather type of the strong wind can be eliminated if the influence of the strong wind on the target power station is small.
And S206, acquiring each second influence factor corresponding to each second weather type, and obtaining the target influence factor based on the second influence factors.
Optionally, obtaining the target influence factor based on the second influence factor includes: acquiring each weight value corresponding to each second influence factor under the condition that the power generation type of the target power station is the target power generation type; and determining the product of each second influence factor and the weight value corresponding to each second influence factor, and determining the target influence factor according to the product.
It should be noted that, the target influence factor is determined according to the product, the target influence factor may be obtained by summing all the products, the target influence factor may also be obtained by multiplying all the products, and other calculation manners may also be used.
It can be understood that, for the same type of weather, the generated power of different types of power stations is different, for example, in windy weather, the influence degree on the generated power of a photovoltaic power station and a wind power station is greatly different. Therefore, the weight values of the influence factors corresponding to different weathers can be determined, and the target influence factor is determined based on the weighted influence factors. It should be noted that the weighted value may be determined by analyzing the influence of the target power station in the historical time period and different weather on the actual generated power, or may be set according to manual experience.
Fig. 3 is a schematic diagram of a generated power prediction process of a new energy power station in an embodiment of the present application, and as shown in fig. 3, the process mainly includes:
the first step is as follows: collecting forecast grade of meteorological data of a target power station in a historical period, current meteorological disaster data of an area where the target power station is located, predicted power generation power of the target power station in the historical period and actual power generation power of the target power station in the historical period;
the second step is that: by analyzing the relation between the forecast grade and the meteorological disaster data, the disaster grade of the disaster forecast grade is output, namely, the different forecast grades are quantized.
The third step: outputting an influence factor of the meteorological disaster on the power generation capacity of the power station by analyzing the relation between the meteorological disaster data and the predicted power generation power and the actual power generation power, namely determining the influence factor of the meteorological disaster on the power generation capacity;
the fourth step: and classifying various meteorological disasters by analyzing the relationship between the second step and the third step, determining forecast grades corresponding to the various meteorological disasters, and obtaining influence factors of the various forecast grades on the power generation capacity, namely determining the influence factors of the various meteorological disaster forecast grades on the power generation capacity of the new energy power station.
The fifth step: and outputting the target prediction power value of the target power station after considering the real-time meteorological change according to the real-time meteorological change.
Fig. 4 is a generated power prediction apparatus of a new energy power station according to an embodiment of the present application, as shown in fig. 4, the apparatus includes:
the first acquisition module 40 is configured to acquire meteorological data of a target area where a target power station is located in a target time period and predicted power generation power corresponding to the target power station;
a second obtaining module 42, configured to obtain a target influence factor of the meteorological data on the actual generated power, where the target influence factor is used to represent an influence degree of the meteorological data on the actual generated power;
and the adjusting module 44 is configured to adjust the predicted power generation power according to the target influence factor to obtain a target predicted power value.
In the device for predicting the generated power, a first obtaining module 40 is used for obtaining meteorological data of a target area where a target power station is located in a target time interval and the predicted generated power corresponding to the target power station; the second obtaining module 42 is configured to obtain a target influence factor of the meteorological data on the actual generated power, where the target influence factor is used to represent a degree of influence of the meteorological data on the actual generated power; the adjusting module 44 is configured to adjust the predicted power generation power according to the target influence factor to obtain a target predicted power value, so as to achieve the purpose of adjusting the predicted power generation power based on the quantized influence factor, thereby achieving the technical effects of more accurately predicting the power generation power of the power station and improving the accuracy and credibility of power station power prediction, and further solving the technical problems of inaccurate prediction result and large deviation from the actual power generation power caused by neglecting the influence of various meteorological data on the power station power generation power in the process of predicting the power generation power of the power station in the related art.
According to another aspect of the embodiments of the present application, there is also provided a non-volatile storage medium, where the storage medium includes a stored program, and when the program runs, the apparatus where the storage medium is located is controlled to execute any one of the methods for predicting the generated power of the new energy power station.
Specifically, the storage medium is used for storing program instructions of the following functions, and the following functions are realized:
acquiring meteorological data of a target area where a target power station is located in a target time period and predicted power generation power corresponding to the target power station; acquiring a target influence factor of meteorological data on actual generated power, wherein the target influence factor is used for representing the influence degree of the meteorological data on the actual generated power; and adjusting the predicted generating power according to the target influence factor to obtain a target predicted power value.
Alternatively, in the present embodiment, the storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the aforementioned storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the aforementioned.
In an exemplary embodiment of the application, there is also provided a computer program product comprising a computer program which, when executed by a processor, implements the method of generating power of a new energy power plant of any of the above.
Optionally, the computer program may, when executed by a processor, implement the steps of:
acquiring meteorological data of a target area where a target power station is located in a target time period and predicted power generation power corresponding to the target power station; acquiring a target influence factor of meteorological data on actual generated power, wherein the target influence factor is used for representing the influence degree of the meteorological data on the actual generated power; and adjusting the predicted generating power according to the target influence factor to obtain a target predicted power value.
According to an embodiment of the present application, there is provided a generated power prediction apparatus of a new energy power station including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform any one of the methods of generating power for a new energy power plant described above.
Optionally, the generated power prediction device of the new energy power station may further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.
FIG. 5 shows a schematic block diagram of a generated power prediction device 500 of an example new energy plant that may be used to implement embodiments of the present application. The generated power prediction device of the new energy power station is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The generated power prediction apparatus of the new energy plant 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 present application that are described and/or claimed herein.
As shown in fig. 5, the device 500 comprises a computing unit 501 which may perform various suitable actions and processes according to a computer program stored in a Read Only Memory (ROM) 502 or a computer program loaded from a storage unit 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the device 500 can also be stored. The calculation unit 501, the ROM 502, and the RAM 503 are connected to each other by a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
A number of components in the device 500 are connected to the I/O interface 505, including: an input unit 506 such as a keyboard, a mouse, or the like; an output unit 507 such as various types of displays, speakers, and the like; a storage unit 508, such as a magnetic disk, optical disk, or the like; and a communication unit 509 such as a network card, modem, wireless communication transceiver, etc. The communication unit 509 allows the device 500 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.
The computing unit 501 may be a variety of general-purpose and/or special-purpose processing components having processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The calculation unit 501 executes the respective methods and processes described above, such as the generated power prediction method of the new energy power plant. For example, in some embodiments, the generated power prediction method of a new energy power plant may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 508. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 500 via the ROM 502 and/or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the computing unit 501, one or more steps of the generated power prediction method of the new energy plant described above may be performed. Alternatively, in other embodiments, the calculation unit 501 may be configured by any other suitable means (e.g. by means of firmware) to perform the generated power prediction method of the new energy plant.
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), 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.
Program code for implementing the methods of the present application may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program code, when executed by the processor or controller, causes the functions/acts specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this application, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
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 may 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. The server may be a cloud server, a server of a distributed system, or a server combining a blockchain.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present application, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application, or portions or all or portions of the technical solutions that contribute to the prior art, may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic disk, or an optical disk, and various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present application and it should be noted that, as will be apparent to those skilled in the art, numerous modifications and adaptations can be made without departing from the principles of the present application and such modifications and adaptations are intended to be considered within the scope of the present application.
Claims (10)
1. A method for predicting the generated power of a new energy power station is characterized by comprising the following steps:
acquiring meteorological data of a target area where a target power station is located in a target time period and predicted power generation power corresponding to the target power station;
acquiring a target influence factor of the meteorological data on actual generated power, wherein the target influence factor is used for representing the influence degree of the meteorological data on the actual generated power;
and adjusting the predicted generating power according to the target influence factor to obtain a target predicted power value.
2. The method of claim 1, wherein adjusting the predicted generated power to a target predicted power value based on the target impact factor comprises:
and determining the target predicted power value according to the product of the target influence factor and the predicted power generation power.
3. The method of claim 1, wherein obtaining a target impact factor of the meteorological data on actual generated power comprises:
acquiring influence factors of each forecast grade on actual power generation in a historical period;
and determining the target forecast grade to which the meteorological data belongs, and acquiring an influence factor corresponding to the target forecast grade as the target influence factor.
4. The method of claim 1, wherein obtaining a target impact factor of the meteorological data on actual generated power comprises:
analyzing the meteorological data to obtain each first weather type included in the meteorological data;
determining each first influence factor corresponding to each first weather type, and summing the first influence factors to obtain the target influence factor.
5. The method of claim 1, wherein prior to obtaining the target impact factor of the meteorological data on the actual generated power, the method further comprises:
acquiring the power generation type of the power station, wherein the power generation type at least comprises one of the following types: light energy power generation, and wind energy power generation;
the method comprises the steps of dividing different weather types according to power generation types of power stations, and clustering the different weather types into two main categories, wherein the first main category is the weather type which has influence on the power generation power of the power station adopting the light energy power generation, and the second main category is the weather type which has influence on the power generation power of the power station adopting the wind energy power generation.
6. The method of claim 5, wherein obtaining a target impact factor of the meteorological data on the actual generated power comprises:
analyzing the meteorological data to obtain each weather type included in the meteorological data;
determining a target power generation type of the target power station, screening each first weather type according to the target power generation type, eliminating weather types which have no influence on the power generation power of the target power station in the process of eliminating, and obtaining each second weather type which remains after elimination;
and acquiring each second influence factor corresponding to each second weather type, and obtaining the target influence factor based on the second influence factors.
7. The method of claim 6, wherein deriving the target impact factor based on a second impact factor comprises:
acquiring each weight value corresponding to each second influence factor under the condition that the power generation type of the target power station is the target power generation type;
and determining the product of each second influence factor and the weight value corresponding to each second influence factor, and determining the target influence factor according to the product.
8. A generated power prediction device of a new energy power station is characterized by comprising:
the first acquisition module is used for acquiring meteorological data of a target area where a target power station is located in a target time period and predicted power generation power corresponding to the target power station;
the second acquisition module is used for acquiring a target influence factor of the meteorological data on the actual generated power, wherein the target influence factor is used for representing the influence degree of the meteorological data on the actual generated power;
and the adjusting module is used for adjusting the predicted generating power according to the target influence factor to obtain a target predicted power value.
9. A non-volatile storage medium, characterized in that the storage medium comprises a stored program, wherein when the program runs, the device where the storage medium is located is controlled to execute the method for predicting the generated power of the new energy power station according to any one of claims 1 to 7.
10. A generated power prediction apparatus of a new energy power station, characterized by comprising:
a processor;
a memory for storing the processor-executable instructions;
wherein the processor is configured to execute the instructions to implement the method of predicting generated power of a new energy power plant of any of claims 1 to 7.
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| CN116933950B (en) * | 2023-09-19 | 2024-01-16 | 国能日新科技股份有限公司 | Transmission method, device, equipment and storage medium of power prediction data |
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