KR102586116B1 - Method and system for predicting amount of wind power generation - Google Patents

Abstract

The present disclosure provides a method for predicting wind power generation, performed by at least one processor. The method includes receiving information associated with wind power generation collected by a wind generator, generating a power curve based on the information associated with wind power generation, and predicting wind power generation using the power curve. Includes.

Description

Wind power generation prediction method and system {METHOD AND SYSTEM FOR PREDICTING AMOUNT OF WIND POWER GENERATION}

The present disclosure relates to a method and system for predicting wind power generation. Specifically, a method and system for generating a power curve based on information related to wind power generation such as wind speed and wind direction, and predicting wind power generation using this. It's about.

Recently, due to problems such as resource depletion, environmental pollution caused by the use of fossil fuels, and greenhouse effect, research and development on eco-friendly renewable energy using wind, tidal, and solar power has been widely conducted. Wind power, a type of renewable energy, has the advantage of being economical as a pollution-free energy source. However, there is a problem in that it is difficult to predict wind power generation because wind power is not kept constant according to natural conditions.

Meanwhile, existing wind power generation prediction methods only consider the relationship between power generation and wind speed defined in a laboratory environment and do not consider other influences that exist in the actual operating environment. Accordingly, there is a problem that the power generation prediction performance of the existing method is significantly reduced.

The present disclosure provides a method and system (device) for predicting wind power generation to solve the above problems.

The present disclosure may be implemented in various ways, including as a method, system (device), or computer program stored in a readable storage medium.

According to one embodiment of the present disclosure, a method for predicting wind power generation includes receiving information associated with wind power generation collected by a wind generator, generating a power curve based on the information associated with wind power generation, and A step of predicting wind power generation using a power curve may be included.

According to an embodiment of the present disclosure, information related to wind power generation may include wind speed, wind direction, and power generation amount measured at the wind power generator.

According to an embodiment of the present disclosure, the power curve may be a model that calculates wind energy corresponding to wind speed and wind direction measured at the wind generator.

According to an embodiment of the present disclosure, the power curve may be generated by an artificial neural network-based model learned to output wind energy by inputting information related to wind power generation.

According to an embodiment of the present disclosure, predicting wind power generation using a power curve may include receiving weather prediction information from a weather prediction system.

According to an embodiment of the present disclosure, weather prediction information may include information associated with predicted wind speed and wind direction in an area where wind power generators are installed.

According to an embodiment of the present disclosure, predicting wind power generation using a power curve may further include calculating wind energy by inputting information related to wind speed and wind direction into the power curve.

According to an embodiment of the present disclosure, wind power generation may correspond to wind energy in a unit time period calculated based on information associated with wind speed and wind direction in the unit time period.

In order to execute the method according to an embodiment of the present disclosure on a computer, a computer program stored in a computer-readable recording medium may be provided.

A wind power generation prediction system according to an embodiment of the present disclosure, comprising a communication module, a memory, and at least one processor connected to the memory and configured to execute at least one computer-readable program included in the memory, and at least one The program may include instructions for receiving information related to wind power generation collected by a wind power generator, generating a power curve based on the information related to wind power generation, and predicting wind power generation amount using the power curve.

According to various embodiments of the present disclosure, a multi-directional power curve based on operational data can be generated by considering the influence of wind direction in the operating environment of an actual wind power generator. Accordingly, the performance of the wind power generation prediction system can be improved. Additionally, by predicting the amount of wind power generation per unit time period, the operation of the wind power generator can be planned in detail.

According to various embodiments of the present disclosure, a power curve that considers not only wind speed but also wind direction can be generated. Accordingly, the amount of wind power generation corresponding to wind energy can be more accurately predicted using the power curve. Additionally, operators of wind power generators can establish wind power generator operation plans to maximize profits based on predicted wind power generation.

The effects of the present disclosure are not limited to the effects mentioned above, and other effects not mentioned are clear to a person skilled in the art (referred to as “a person skilled in the art”) in the technical field to which the present disclosure pertains from the description of the claims. It will be understandable.

Embodiments of the present disclosure will be described with reference to the accompanying drawings described below, in which like reference numerals indicate like elements, but are not limited thereto.
Figure 1 shows an example of a wind power generation prediction system according to an embodiment of the present disclosure.
Figure 2 is a schematic diagram showing a configuration in which an information processing system is connected to communicate with a wind power generator and a weather prediction system in order to predict wind power generation according to an embodiment of the present disclosure.
Figure 3 is a block diagram showing the internal configuration of a wind power generator and an information processing system according to an embodiment of the present disclosure.
Figure 4 is a diagram showing the internal configuration of a processor according to an embodiment of the present disclosure.
Figure 5 is a diagram showing an example of a power curve according to an embodiment of the present disclosure.
Figure 6 is a flowchart illustrating an example of a method according to an embodiment of the present disclosure.

Hereinafter, specific details for implementing the present disclosure will be described in detail with reference to the attached drawings. However, in the following description, detailed descriptions of well-known functions or configurations will be omitted if there is a risk of unnecessarily obscuring the gist of the present disclosure.

In the accompanying drawings, identical or corresponding components are given the same reference numerals. Additionally, in the description of the following embodiments, overlapping descriptions of identical or corresponding components may be omitted. However, even if descriptions of components are omitted, it is not intended that such components are not included in any embodiment.

Advantages and features of the disclosed embodiments and methods for achieving them will become clear by referring to the embodiments described below in conjunction with the accompanying drawings. However, the present disclosure is not limited to the embodiments disclosed below and may be implemented in various different forms. The present embodiments are merely provided to ensure that the present disclosure is complete and that the present disclosure does not convey the scope of the invention to those skilled in the art. It is provided only for complete information.

Terms used in this specification will be briefly described, and the disclosed embodiments will be described in detail. The terms used in this specification are general terms that are currently widely used as much as possible while considering the function in the present disclosure, but this may vary depending on the intention or precedent of a technician working in the related field, the emergence of new technology, etc. In addition, in certain cases, there are terms arbitrarily selected by the applicant, and in this case, the meaning will be described in detail in the description of the relevant invention. Accordingly, the terms used in this disclosure should be defined based on the meaning of the term and the overall content of the present disclosure, rather than simply the name of the term.

In this specification, singular expressions include plural expressions, unless the context clearly specifies the singular. Additionally, plural expressions include singular expressions, unless the context clearly specifies plural expressions. When it is said that a certain part includes a certain element throughout the specification, this does not mean excluding other elements, but may further include other elements, unless specifically stated to the contrary.

Additionally, the term 'module' or 'unit' used in the specification refers to a software or hardware component, and the 'module' or 'unit' performs certain roles. However, 'module' or 'unit' is not limited to software or hardware. A 'module' or 'unit' may be configured to reside on an addressable storage medium and may be configured to run on one or more processors. Thus, as an example, a 'module' or 'part' refers to components such as software components, object-oriented software components, class components and task components, processes, functions and properties. , procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, or variables. Components and 'modules' or 'parts' may be combined into smaller components and 'modules' or 'parts' or further components and 'modules' or 'parts'. Could be further separated.

According to an embodiment of the present disclosure, a 'module' or 'unit' may be implemented with a processor and memory. 'Processor' should be interpreted broadly to include general-purpose processors, central processing units (CPUs), microprocessors, digital signal processors (DSPs), controllers, microcontrollers, state machines, etc. In some contexts, 'processor' may refer to an application-specific integrated circuit (ASIC), programmable logic device (PLD), field programmable gate array (FPGA), etc. 'Processor' refers to a combination of processing devices, for example, a combination of a DSP and a microprocessor, a combination of a plurality of microprocessors, a combination of one or more microprocessors in combination with a DSP core, or any other such combination of configurations. You may. Additionally, 'memory' should be interpreted broadly to include any electronic component capable of storing electronic information. 'Memory' refers to random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), programmable read-only memory (PROM), erasable-programmable read-only memory (EPROM), May also refer to various types of processor-readable media, such as electrically erasable PROM (EEPROM), flash memory, magnetic or optical data storage, registers, etc. A memory is said to be in electronic communication with a processor if the processor can read information from and/or write information to the memory. The memory integrated into the processor is in electronic communication with the processor.

In the present disclosure, 'system' may include at least one of a server device and a cloud device, but is not limited thereto. For example, a system may consist of one or more server devices. As another example, a system may consist of one or more cloud devices. As another example, the system may be operated with a server device and a cloud device configured together.

In this disclosure, 'display' may refer to any display device associated with a computing device, e.g., any display device capable of displaying any information/data controlled by or provided by the computing device. can refer to.

In the present disclosure, 'each of a plurality of A' or 'each of a plurality of A' may refer to each of all components included in a plurality of A, or may refer to each of some components included in a plurality of A. .

Figure 1 shows an example of the functions and operations of a wind power generation prediction system according to an embodiment of the present disclosure. In one embodiment, the wind power generation prediction system may receive wind generator information or information 110 related to wind power generation. For example, a wind power generator located remotely from the wind power generation prediction system or connected to a network may collect information 110 related to wind power generation through various sensors and transmit the information 110 to the wind power generation prediction system. Here, information related to wind power generation may include wind speed, wind direction, and power generation amount measured by a sensor in the wind power generator.

In one embodiment, the wind power generation prediction system may generate a power curve based on information 110 related to wind power generation (120). Here, the power curve may be a model that calculates wind energy corresponding to the wind speed and wind direction measured by the wind generator. At this time, the power curve can be generated by an artificial neural network-based model learned to output wind energy by inputting information related to wind power generation. For example, a power curve capable of calculating wind energy can be generated by inputting wind speed, wind direction, and power generation measured from a wind power generator into a gradient boosting model (GBM), but the present invention is not limited to this.

In one embodiment, wind speed and direction in an area equipped with a wind generator may be predicted by a weather prediction system (130). Here, the weather prediction system may include, but is not limited to, a weather forecast system of the Korea Meteorological Administration, a weather forecast system of the National Oceanic and Atmospheric Administration of the United States, which is located remotely from the wind power generation forecast system or connected to a network. This weather prediction information can be reflected in the power curve.

In one embodiment, the wind power generation prediction system can predict wind power generation using a power curve (140). Specifically, wind energy can be calculated by inputting information related to wind speed and wind direction into the power curve. Additionally, wind power generation can be calculated by converting from wind energy. Here, wind power generation may correspond to wind energy in a unit time period calculated based on information related to wind speed and wind direction in the unit time period. In other words, the wind power generation prediction system can predict the wind power generation amount in a unit time period using the power curve. Alternatively, the wind power generation prediction system may predict the daily wind power generation amount using a power curve.

Through this configuration, it is possible to generate a multi-directional power curve based on operational data by considering the influence of wind direction in the actual operating environment of a wind power generator. Accordingly, the performance of the wind power generation prediction system can be improved. Additionally, by predicting the amount of wind power generation per unit time period, the operation of the wind power generator can be planned in detail.

Figure 2 is a schematic diagram showing a configuration in which the information processing system 230 is connected to communicate with the wind power generator 210 and the weather prediction system 240 in order to predict wind power generation amount according to an embodiment of the present disclosure. As shown, the wind power generator 210 and the weather prediction system 240 may be connected to the information processing system 230 that can provide a wind power generation prediction service through the network 220.

In one embodiment, information processing system 230 is one or more server devices and/or databases capable of storing, providing, and executing computer-executable programs (e.g., downloadable applications) and data associated with providing wind power generation prediction services, etc. , or may include one or more distributed computing devices and/or distributed databases based on cloud computing services.

The wind power generation prediction service provided by the information processing system 230 may be provided to the user through a wind power generation prediction service application web browser or a web browser extension program installed on each user terminal (not shown). For example, the information processing system 230 may provide information or perform processing corresponding to a wind power generation prediction request received from a user terminal through a wind power generation prediction service application.

Wind generator 210 and weather prediction system 240 may communicate with information processing system 230 through network 220. The network 220 may be configured to enable communication between the wind power generator 210, the weather prediction system 240, and the information processing system 230. Depending on the installation environment, the network 220 may be, for example, a wired network such as Ethernet, a wired home network (Power Line Communication), a telephone line communication device, and RS-serial communication, a mobile communication network, a wireless LAN (WLAN), It may consist of wireless networks such as Wi-Fi, Bluetooth, and ZigBee, or a combination thereof. The communication method is not limited, and may include not only a communication method utilizing a communication network that the network 220 may include (for example, a mobile communication network, wired Internet, wireless Internet, broadcasting network, satellite network, etc.), but also short-range wireless communication.

The wind generator 210 may include sensors that measure wind speed, wind direction, and power generation. Here, the sensor can measure wind speed and direction using windmill, windpipe, or ultrasonic methods. Additionally, the sensor can measure the amount of electricity generated by the rotation of the wind generator's blades. The wind power generator 210 may transmit information related to the measured wind speed, wind direction, and power generation to the information processing system 230 through the network 220.

The weather prediction system 240 can predict weather such as wind speed and wind direction in an area where the wind generator 210 is installed. For example, the weather prediction system 240 may include, but is not limited to, the National Weather Service's weather forecast system, the National Oceanic and Atmospheric Administration's weather prediction system, etc. The weather prediction system 240 may transmit weather prediction information to the information processing system 230 through the network 220.

In Figure 2, the wind power generator 210, the weather prediction system 240, and the information processing system 230 are shown as separate, but the present invention is not limited thereto. For example, weather prediction information may be generated through a weather prediction program/application installed in the information processing system 230, without communication with the weather prediction system 240. In other examples, information processing system 230 may be implemented as part of wind generator 210 or weather prediction system 240.

Figure 3 is a block diagram showing the internal configuration of the wind power generator 210 and the information processing system 230 according to an embodiment of the present disclosure. The wind power generator 210 may generally refer to a device that produces power by rotating blades due to wind, and may include, for example, a vertical axis wind power generator or a horizontal axis wind power generator. As shown, wind generator 210 may include memory 312, processor 314, communication module 316, sensor 318, and generator 320. Similarly, information processing system 230 may include memory 332, processor 334, communication module 336, and input/output interface 338. As shown in FIG. 3, the wind generator 210 and the information processing system 230 are configured to communicate information and/or data through the network 220 using respective communication modules 316 and 336. It can be.

Memories 312 and 332 may include any non-transitory computer-readable recording medium. According to one embodiment, the memories 312 and 332 are non-permanent mass storage devices such as read only memory (ROM), disk drive, solid state drive (SSD), flash memory, etc. It can be included. As another example, non-perishable mass storage devices such as ROM, SSD, flash memory, disk drive, etc. may be included in the wind power generator 210 or the information processing system 230 as a separate persistent storage device that is distinct from memory. Additionally, an operating system and at least one program code may be stored in the memories 312 and 332.

These software components may be loaded from a computer-readable recording medium separate from the memories 312 and 332. These separate computer-readable recording media may include recording media directly connectable to the wind generator 210 and the information processing system 230, for example, floppy drives, disks, tapes, DVD/CD- It may include computer-readable recording media such as ROM drives and memory cards. As another example, software components may be loaded into the memories 312 and 332 through the communication modules 316 and 336 rather than computer-readable recording media. For example, at least one program is loaded into memory 312, 332 based on a computer program installed by files provided over the network 220 by developers or a file distribution system that distributes installation files for applications. It can be.

The processors 314 and 334 may be configured to process instructions of a computer program by performing basic arithmetic, logic, and input/output operations. Instructions may be provided to the processors 314 and 334 by memories 312 and 332 or communication modules 316 and 336. For example, the processors 314 and 334 may be configured to execute instructions received according to program codes stored in a recording device such as the memory 312 and 332.

The communication modules 316 and 336 may provide a configuration or function for the wind generator 210 and the information processing system 230 to communicate with each other through the network 220, and the wind generator 210 and/or the information processing system. System 230 may provide configuration or functionality for communicating with other wind turbines or other systems (e.g., separate cloud systems, etc.). For example, a control signal or command provided under the control of the processor 334 of the information processing system 230 is transmitted through the communication module 316 of the wind power generator 210 through the communication module 336 and the network 220. It can be delivered to the wind power generator 210.

The input/output interface 338 of the information processing system 230 may be connected to the information processing system 230 or may be a means for interfacing with a device (not shown) for input or output that the information processing system 230 may include. there is. In FIG. 3 , the input/output interface 338 is shown as an element configured separately from the processor 334, but the present invention is not limited thereto, and the input/output interface 338 may be included in the processor 334.

Processor 314 of wind generator 210 may be configured to manage, process, and/or store information and/or data received from sensors 318, information processing system 230, and/or a plurality of external systems. . Additionally, processor 314 may use sensor 318 to receive information and/or data associated with wind speed, wind direction, and power generated by generator 320 . Information and/or data processed by processor 314 may be provided to information processing system 230 via communication module 316 and network 220. Additionally, the processor 314 of the wind generator 210 may control the generator 320 to generate power and transmit it to the energy storage system. Similarly, processor 334 of information processing system 230 may be configured to manage, process, and/or store information and/or data received from a plurality of wind generators 210 and/or a plurality of external systems. there is.

Wind generator 210 and information processing system 230 may include more components than those of FIG. 3 . However, there is no need to clearly show most prior art components.

FIG. 4 is a diagram showing the internal configuration of the processor 334 according to an embodiment of the present disclosure. As shown, the processor 334 of the information processing system may include a collection unit 410, a power curve generation unit 420, and a power generation prediction unit 430. In FIG. 4, a single processor is shown, but the processor is not limited thereto and may consist of a plurality of processors.

The collection unit 410 may receive information related to wind power generation collected by the wind power generator. Here, information related to wind power generation may include wind speed, wind direction, and power generation amount measured at the wind power generator. Additionally, information related to the received wind power generation may be transmitted to the power curve generator 420. Additionally or alternatively, information associated with received wind power generation may be stored in database 440.

In one embodiment, the collection unit 410 may receive weather prediction information from a weather prediction system. Here, the weather prediction information may include information related to predicted wind speed and wind direction in an area where wind power generators are installed. Additionally, the weather prediction system may include, but is not limited to, the National Weather Service's weather forecast system, the National Oceanic and Atmospheric Administration's weather forecast system, etc. The received weather prediction information may be transmitted to the power generation prediction unit 430. Additionally or alternatively, the collected weather forecast information may be stored in database 440.

The power curve generator 420 may generate a power curve based on information related to wind power generation. Here, the power curve may be a model that calculates wind energy corresponding to the wind speed and wind direction measured by the wind generator. Additionally, the power curve generator 420 may input information related to wind power generation and generate a power curve using an artificial neural network-based model learned to output wind energy. For example, the artificial neural network-based model may include, but is not limited to, a gradient boosting model (GBM) for fitting a power curve. The power curve generator 420 may transmit the generated power curve to the power generation prediction unit 430. Additionally or alternatively, the generated power curve may be stored in database 440.

The power generation prediction unit 430 can predict the wind power generation amount using the power curve generated by the power curve generator 420. Specifically, the power generation prediction unit 430 may receive weather prediction information from a weather prediction system. Here, the weather prediction information may include information related to predicted wind speed and wind direction in an area where wind power generators are installed. In particular, weather prediction information may include information related to wind speed and wind direction in a unit time period.

Thereafter, the power generation prediction unit 430 may calculate wind energy by inputting information related to wind speed and wind direction into the power curve. In this case, the power generation prediction unit 430 can predict the wind power generation amount by converting the calculated wind energy into power generation amount. At this time, wind power generation can correspond to wind energy in a unit time period.

The internal configuration of the processor 334 shown in FIG. 4 is only an example, and in some embodiments, other configurations other than the illustrated internal configuration may be additionally included, some configurations may be omitted, and some processes may be performed using other configurations or external systems. It can be performed by . In addition, although the internal components of the processor 334 are described separately by function in FIG. 4, this does not necessarily mean that the internal components are physically divided.

Figure 5 is a diagram showing an example of a power curve according to an embodiment of the present disclosure. The first example 510 is an example showing a power curve generated according to the prior art. The existing power curve may be a model that calculates wind energy corresponding to wind speed. Each point on the graph can represent the wind energy value according to the corresponding wind speed. Based on this data, a power curve that can be expressed as a line on a two-dimensional graph can be generated by fitting. For example, the power curve generated according to the prior art can be expressed as Equation 1 below.

Here, C _p is the power coefficient and may be a function of yaw angle, pitch angle, and tip speed ratio (TSR). Additionally, ρ can represent the air density, A can represent the rotating cross-sectional area of the turbine blade, and v can represent the wind speed. However, because the existing power curve calculates wind energy by considering only wind speed, the performance of predicting wind power generation may be significantly reduced.

The second example 520 is an example showing a power curve generated according to an embodiment of the present disclosure. Unlike conventional power curves, the power curve of the present disclosure can consider both wind speed and wind direction. Accordingly, a power curve that can be expressed as a surface on a 3D graph can be generated by fitting. For example, the power curve generated according to an embodiment of the present disclosure may be expressed as Equation 2 below.

Here, C _θ is a power coefficient due to wind direction, which can be preset based on past experience or estimated by fitting by an artificial neural network-based model. Equation 2 is a formula that calculates a power curve considering both wind direction and wind speed, and C _θ may be set differently depending on information related to wind power generation collected by each wind power generator. In this way, by using the power curve, wind energy can be predicted based on the predicted wind speed and wind direction in the area where the wind generator is installed. Additionally, wind power generation can be calculated by converting from wind energy.

With this configuration, a power curve can be generated that takes into account not only wind speed but also wind direction. Accordingly, the amount of wind power generation corresponding to wind energy can be more accurately predicted using the power curve. Additionally, operators of wind power generators can establish wind power generator operation plans to maximize profits based on predicted wind power generation.

Figure 6 is a flow chart illustrating an example method 600 according to one embodiment of the present disclosure. In one embodiment, method 600 may be performed by at least one processor. Method 600 may begin with a processor receiving information associated with wind power collected by a wind generator (S610). Here, information related to wind power generation may include wind speed, wind direction, and power generation amount measured at the wind power generator.

Afterwards, the processor may generate a power curve based on information related to wind power generation (S620). Here, the power curve may be a model that calculates wind energy corresponding to the wind speed and wind direction measured by the wind generator. Additionally, the power curve can be generated by an artificial neural network-based model learned to output wind energy by inputting information related to wind power generation.

Afterwards, the processor can predict wind power generation using the power curve (S630). Here, wind power generation may correspond to wind energy in a unit time period calculated based on information related to wind speed and wind direction in the unit time period.

In one embodiment, the processor may receive weather prediction information from a weather prediction system. Here, the weather prediction information may include information related to predicted wind speed and wind direction in an area where wind power generators are installed. Additionally, the processor can calculate wind energy by inputting information related to wind speed and wind direction into the power curve.

The above-described method may be provided as a computer program stored in a computer-readable recording medium for execution on a computer. Media may be used to continuously store executable programs on a computer, or may be temporarily stored for execution or download. In addition, the medium may be a variety of recording or storage means in the form of a single or several pieces of hardware combined. It is not limited to a medium directly connected to a computer system and may be distributed over a network. Examples of media include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical recording media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and There may be something configured to store program instructions, including ROM, RAM, flash memory, etc. Additionally, examples of other media include recording or storage media managed by app stores that distribute applications, sites or servers that supply or distribute various other software, etc.

The methods, operations, or techniques of this disclosure may be implemented by various means. For example, these techniques may be implemented in hardware, firmware, software, or a combination thereof. Those skilled in the art will understand that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the disclosure herein may be implemented in electronic hardware, computer software, or combinations of both. To clearly illustrate this interchange of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and design requirements imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementations should not be interpreted as causing a departure from the scope of the present disclosure.

In a hardware implementation, the processing units used to perform the techniques may include one or more ASICs, DSPs, digital signal processing devices (DSPDs), programmable logic devices (PLDs). ), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, electronic devices, and other electronic units designed to perform the functions described in this disclosure. , a computer, or a combination thereof.

Accordingly, the various illustrative logical blocks, modules, and circuits described in connection with this disclosure may be general-purpose processors, DSPs, ASICs, FPGAs or other programmable logic devices, discrete gate or transistor logic, discrete hardware components, or It may be implemented or performed as any combination of those designed to perform the functions described in. A general-purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, such as a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other configuration.

For firmware and/or software implementations, techniques include random access memory (RAM), read-only memory (ROM), non-volatile random access memory (NVRAM), and PROM ( on computer-readable media such as programmable read-only memory (EPROM), electrically erasable PROM (EEPROM), flash memory, compact disc (CD), magnetic or optical data storage devices, etc. It may also be implemented as stored instructions. Instructions may be executable by one or more processors and may cause the processor(s) to perform certain aspects of the functionality described in this disclosure.

When implemented in software, the techniques may be stored on or transmitted through a computer-readable medium as one or more instructions or code. Computer-readable media includes both computer storage media and communication media, including any medium that facilitates transfer of a computer program from one place to another. Storage media may be any available media that can be accessed by a computer. By way of non-limiting example, such computer readable media may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or the desired program code in the form of instructions or data structures. It can be used to transfer or store data and can include any other media that can be accessed by a computer. Any connection is also properly termed a computer-readable medium.

For example, if the Software is transmitted from a website, server, or other remote source using coaxial cable, fiber optic cable, twisted pair cable, digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwave, , fiber optic cable, twisted pair, digital subscriber line, or wireless technologies such as infrared, radio, and microwave are included within the definition of medium. As used herein, disk and disk include CD, laser disk, optical disk, digital versatile disc (DVD), floppy disk, and Blu-ray disk, where disks are usually magnetic. It reproduces data optically, while discs reproduce data optically using lasers. Combinations of the above should also be included within the scope of computer-readable media.

A software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known. An exemplary storage medium may be coupled to the processor such that the processor may read information from or write information to the storage medium. Alternatively, the storage medium may be integrated into the processor. The processor and storage medium may reside within an ASIC. ASIC may exist within the user terminal. Alternatively, the processor and storage medium may exist as separate components in the user terminal.

Although the above-described embodiments have been described as utilizing aspects of the presently disclosed subject matter in one or more standalone computer systems, the disclosure is not limited thereto and may also be implemented in conjunction with any computing environment, such as a network or distributed computing environment. . Furthermore, aspects of the subject matter of this disclosure may be implemented in multiple processing chips or devices, and storage may be similarly effected across the multiple devices. These devices may include PCs, network servers, and portable devices.

Although the present disclosure has been described in relation to some embodiments in this specification, various modifications and changes may be made without departing from the scope of the present disclosure as can be understood by a person skilled in the art to which the invention pertains. Additionally, such modifications and changes should be considered to fall within the scope of the claims appended hereto.

110: Information related to wind power generation
120: Power curve generation
130: Weather forecast
140: Power generation forecast

Claims (10)

In a method for predicting wind power generation, performed by at least one processor,
Receiving information associated with wind power collected by the wind generator;
generating a three-dimensional power curve based on information related to the wind power generation; and
Predicting wind power generation using the 3D power curve
Including,
The information related to the wind power generation includes wind speed, wind direction, and power generation amount measured at the wind power generator,
The three-dimensional power curve is generated by an artificial neural network-based model learned to output wind energy by inputting wind speed, wind direction, and power generation measured from the wind power generator,
The three-dimensional power curve is a three-dimensional model that calculates wind energy corresponding to the wind speed and wind direction measured by the wind generator,
The three-dimensional power curve is expressed as a surface on a three-dimensional graph,
The three-dimensional power curve is associated with a power coefficient set based on the wind direction measured at the wind power generator.
delete delete delete According to paragraph 1,
The step of predicting wind power generation using the 3D power curve is,
Receiving weather prediction information from a weather prediction system;
Method for predicting wind power generation, including.
According to clause 5,
The weather prediction information includes information related to predicted wind speed and wind direction in an area where the wind power generator is installed.
According to clause 6,
The step of predicting wind power generation using the 3D power curve is,
Calculating wind energy by inputting information related to the wind speed and wind direction into the three-dimensional power curve.
A method for predicting wind power generation, further comprising:
According to paragraph 1,
The wind power generation amount corresponds to wind energy in a unit time period calculated based on information associated with the wind speed and wind direction in the unit time period.
A computer program stored in a computer-readable recording medium for executing the method according to any one of claims 1 and 5 to 8 on a computer.
As a wind power generation prediction system,
communication module;
Memory;
display; and
At least one processor connected to the memory and configured to execute at least one computer-readable program included in the memory,
The at least one program is,
Receive information related to wind power collected by wind power generators,
Generating a three-dimensional power curve based on information related to the wind power generation,
Contains instructions for predicting wind power generation using the 3D power curve,
The information related to the wind power generation includes wind speed, wind direction, and power generation amount measured at the wind power generator,
The three-dimensional power curve is generated by an artificial neural network-based model learned to output wind energy by inputting wind speed, wind direction, and power generation measured from the wind generator,
The three-dimensional power curve is a three-dimensional model that calculates wind energy corresponding to the wind speed and wind direction measured by the wind generator,
The three-dimensional power curve is expressed as a surface on a three-dimensional graph,
The three-dimensional power curve is associated with a power coefficient set based on the wind direction measured at the wind power generator.

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