Method and system for predicting photovoltaic power generation amount

Abstract

Disclosed is an information processing system. The information processing system comprises: a memory; and 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 includes instructions for: acquiring land cover data on the basis of satellite aerial image data; predicting data associated with an environment and solar radiation data by analyzing, on the basis of a wavelength, data collected from an environmental satellite and a weather observation system; and predicting data associated with the air quality on the basis of the land cover data and the data associated with the environment.

Description

Solar power generation prediction method and system

[0001] The present disclosure relates to a method and system for predicting solar power generation, and specifically, to a method for predicting solar power generation by reflecting the physical characteristics of the ground surface and an information processing system for the same.

[0002] As interest in climate change and environmental issues caused by global warming grows, the importance of solar power generation as a renewable energy source is increasing day by day. While solar power has the advantage of producing clean energy without carbon emissions, it has the disadvantage of being difficult to predict as its output is heavily dependent on climate and weather conditions. In particular, since various meteorological factors such as clouds, solar radiation, temperature, humidity, and wind affect solar power generation, accurately predicting them is essential for the efficient operation of solar power generation and the maintenance of power network stability.

[0003] In conventional technology, solar power generation was predicted using statistical methods based on meteorological data or physics-based climate models; however, there were problems with low prediction accuracy due to poor temporal or spatial resolution or failure to properly reflect the physical characteristics of the Earth's surface.

[0004] The information described above disclosed in the background technology of this invention is intended only to enhance understanding of the background of the present invention and may therefore include information that does not constitute prior art.

[0005] The present disclosure provides a method and system for predicting solar power generation by reflecting the land cover characteristics of the surface to solve the above-mentioned problems.

[0006] However, the technical problems that the present invention aims to solve are not limited to those described above, and other unmentioned problems can be clearly understood by those skilled in the art from the description of the invention below.

[0007] The present disclosure may be implemented in various ways, including a method, an apparatus (system), or a computer program stored on a readable storage medium.

[0008] According to one embodiment of the present disclosure, a method for predicting solar power generation performed by at least one processor comprises: acquiring land cover data based on satellite aerial imagery data; analyzing data collected from an environmental satellite and a weather observation system based on wavelength to predict environmental data and solar radiation data; predicting air quality data based on land cover data and environmental data; predicting at least one of direct solar radiation reaching the surface, scattered solar radiation, or surface temperature based on at least one of land cover data, air quality data, solar radiation data, or temperature data; estimating the module temperature of a solar panel based on the predicted surface temperature; and predicting solar power generation based on at least one of the estimated module temperature of the solar panel and the predicted direct solar radiation reaching the surface or scattered solar radiation.

[0009] According to one embodiment of the present disclosure, the step of acquiring land cover data includes the step of acquiring land cover data by inputting optical satellite image data and aerial image data into a first model trained based on deep learning, and the acquired land cover data includes data associated with at least one of forests, farmland, water bodies, roads, railways, bare land, or buildings.

[0010] According to one embodiment of the present disclosure, the step of predicting solar power generation includes predicting solar power generation based on the collection period of data collected from an environmental satellite and a weather observation system.

[0011] According to one embodiment of the present disclosure, a method for predicting solar power generation further comprises the step of generating a data lake comprising at least one of satellite aerial image data, acquired land cover data, data collected from an environmental satellite and a weather observation system, solar radiation data, data associated with air quality, temperature data, weather forecast data, predicted direct solar radiation reaching the surface, scattered solar radiation, surface temperature, estimated module temperature of a solar panel, or predicted solar power generation.

[0012] According to one embodiment of the present disclosure, after the step of generating a data lake, the solar power generation prediction method further includes the step of responding to a request message from a user terminal based on the generated data lake.

[0013] According to one embodiment of the present disclosure, a method for predicting solar power generation further comprises the steps of: calculating solar radiation reaching the inclined surface of a solar panel based on direct solar radiation reaching the surface and scattered solar radiation; calculating effective solar radiation of a solar panel based on solar radiation reaching the inclined surface of a solar panel and reflectance; and calculating the module temperature of a solar panel based on information related to the calculated effective solar radiation of the solar panel and the temperature of the solar panel.

[0014] According to one embodiment of the present disclosure, after the step of calculating the module temperature of a solar panel, the method further includes the step of verifying the estimated module temperature of a solar panel based on the calculated module temperature of the solar panel.

[0015] According to one embodiment of the present disclosure, a computer program stored on a computer-readable recording medium is provided for executing a solar power generation prediction method on a computer.

[0016] According to one embodiment of the present disclosure, an information processing system comprises a memory and a processor connected to the memory and configured to execute at least one computer-readable program included in the memory, wherein the at least one program comprises instructions for acquiring land cover data based on satellite aerial imagery data, analyzing data collected from an environmental satellite and a weather observation system based on wavelength to predict environmental data and solar radiation data, predicting air quality data based on land cover data and environmental data, predicting at least one of direct solar radiation reaching the surface, scattered solar radiation, or surface temperature based on at least one of land cover data, air quality data, solar radiation data, or temperature data, estimating the module temperature of a solar panel based on the predicted surface temperature, and predicting solar power generation based on at least one of the estimated module temperature of a solar panel, predicted direct solar radiation reaching the surface, or scattered solar radiation.

[0017] According to one embodiment of the present disclosure, at least one program includes instructions for acquiring land cover data by inputting optical satellite image data and aerial image data into a first model trained based on deep learning, and the acquired land cover data includes data associated with at least one of forests, farmland, water bodies, roads, railways, bare land, or buildings.

[0018] According to some embodiments of the present disclosure, a method for predicting solar power generation that reflects the land cover characteristics of the surface is provided, thereby improving the accuracy of the prediction.

[0019] According to some embodiments of the present disclosure, real-time prediction of solar power generation can be performed even if a solar irradiance measuring device is not provided at the solar power plant.

[0020] However, the effects obtainable through the present invention are not limited to those described above, and other unmentioned technical effects will be clearly understood by those skilled in the art from the description of the invention below.

[0021] The following drawings attached to this specification illustrate preferred embodiments of the present invention and serve to further enhance understanding of the technical concept of the present invention together with the detailed description of the invention provided below; therefore, the present invention should not be interpreted as being limited only to the matters described in such drawings.

[0022] FIG. 1 is a schematic diagram illustrating a solar power generation prediction device according to one embodiment of the present disclosure.

[0023] FIG. 2 is a schematic diagram showing a configuration in which an information processing system is connected to communicate with a plurality of user terminals to predict solar power generation by reflecting land cover data of the surface according to one embodiment of the present disclosure.

[0024] FIG. 3 is a block diagram showing the internal configuration of a user terminal and an information processing system according to one embodiment of the present disclosure.

[0025] FIG. 4 is a drawing for explaining in detail a method for predicting solar power generation by reflecting land cover data of the surface according to one embodiment of the present disclosure.

[0026] FIG. 5 is a diagram illustrating the process of calculating and verifying the module temperature of a solar panel according to one embodiment of the present disclosure.

[0027] FIG. 6 is a drawing showing a graph related to photovoltaic power generation efficiency according to one embodiment of the present disclosure.

[0028] FIG. 7 is a flowchart illustrating a method for predicting solar power generation according to one embodiment of the present disclosure.

[0029] FIG. 8 is a flowchart illustrating a method for calculating the module temperature of a solar panel according to one embodiment of the present disclosure.

[0030] Embodiments of the present disclosure will be described with reference to the accompanying drawings described below, wherein similar reference numerals indicate similar elements, but are not limited thereto.

[0031] Hereinafter, specific details for implementing the present disclosure will be described in detail with reference to the attached drawings. However, in the following description, specific descriptions regarding widely known functions or configurations will be omitted if there is a risk that the gist of the present disclosure may be unnecessarily obscured.

[0032] In the attached drawings, identical or corresponding components are assigned the same reference numerals. Additionally, in the description of the following embodiments, the description of identical or corresponding components may be omitted. However, even if a description of a component is omitted, it is not intended that such component is not included in any embodiment.

[0033] The advantages and features of the disclosed embodiments and the 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 but may be implemented in various different forms, and the embodiments provided are merely to make the present disclosure complete and to fully inform those skilled in the art of the scope of the invention.

[0034] The terms used in this specification will be briefly explained, and the disclosed embodiments will be described in detail. The terms used in this specification have been selected to be as generally used as possible, taking into account their functions in this disclosure; however, these terms may vary depending on the intent of those skilled in the art, case law, the emergence of new technologies, etc. Additionally, in specific cases, terms may be arbitrarily selected by the applicant, and in such cases, their meanings will be described in detail in the relevant description of the invention. Therefore, the terms used in this disclosure should be defined not merely by their names, but based on their meanings and the content throughout this disclosure.

[0035] In this specification, singular expressions include plural expressions unless the context clearly specifies them as singular. Additionally, plural expressions include singular expressions unless the context clearly specifies them as plural. Throughout the specification, when a part is described as including a certain component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components.

[0036] Additionally, the terms 'module' or 'part' as used in the specification refer to software or hardware components, and the 'module' or 'part' performs certain roles. However, the meaning of 'module' or 'part' is not limited to software or hardware. The 'module' or 'part' may be configured to reside in an addressable storage medium or configured to run on one or more processors. Thus, as an example, the 'module' or 'part' may include components such as software components, object-oriented software components, class components, and task components, and at least one of processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, or variables. The components and the functions provided within the 'module' or 'part' may be combined into a smaller number of components and 'modules' or 'parts', or further separated into additional components and 'modules' or 'parts'.

[0037] According to one embodiment of the present disclosure, a ‘module’ or ‘part’ may be implemented as a processor and memory. The term ‘processor’ should be broadly interpreted to include a general-purpose processor, a central processing unit (CPU), a microprocessor, a digital signal processor (DSP), a controller, a microcontroller, a state machine, etc. In some environments, the term ‘processor’ may refer to an application-specific integrated circuit (ASIC), a programmable logic device (PLD), a field programmable gate array (FPGA), etc. The term ‘processor’ may also refer to a combination of processing devices, such as, for example, a combination of a DSP and a microprocessor, a combination of multiple microprocessors, a combination of one or more microprocessors combined with a DSP core, or any other combination of such configurations. Additionally, the term ‘memory’ should be broadly interpreted to include any electronic component capable of storing electronic information. 'Memory' may refer to various types of processor-readable media, such as 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), Electrically Erasable PROM (EEPROM), Flash Memory, Magnetic or Optical Data Storage Devices, and Registers. If a processor can read information from memory and write information to memory, the memory is said to be in an electronic communication state with the processor. Memory integrated into the processor is in an electronic communication state with the processor.

[0038] In the present disclosure, the 'system' may include at least one of a server device and a cloud device, but is not limited thereto. For example, the system may be composed of one or more server devices. As another example, the system may be composed of one or more cloud devices. As yet another example, the system may be configured and operated with both a server device and a cloud device.

[0039] In the present disclosure, 'display' may refer to any display device associated with a computing device, for example, any display device capable of displaying any information / data controlled by or provided by the computing device.

[0040] 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 each of some components included in a plurality of A.

[0041] In the present disclosure, 'photovoltaic power generation' is an energy production method that converts sunlight into electricity, and is carried out using solar panels, and the solar panels may include solar cells that receive light and convert it into electricity.

[0042] In the present disclosure, 'irradiance' may include beam irradiance and diffuse irradiance. Beam irradiance is irradiance that reaches the surface of the earth directly without scattering or absorption by the atmosphere, and may be direct solar energy. Diffuse irradiance is irradiance that reaches the surface of the earth after being scattered in various directions by particles in the atmosphere, and may be indirect solar energy. Beam irradiance may include both beam irradiance and diffuse irradiance.

[0043] FIG. 1 is a schematic diagram for explaining a solar power generation prediction device (100) according to one embodiment of the present disclosure.

[0044] Referring to FIG. 1, the solar power generation prediction device (100) can predict solar power generation based on collected information even without directly measuring the solar irradiance incident on the solar panel.

[0045] The solar power generation prediction device (100) can collect data from at least one of an optical satellite (12), an aviation system (14), an environmental satellite (16), a weather observation system (18), or a weather forecasting system (20) (S22).

[0046] The solar power generation prediction device (100) can collect satellite aerial image data from an optical satellite (12) and an aviation system (14). The satellite aerial image data may include image data of the surface of the earth taken from above.

[0047] Here, the optical satellite (12) can photograph the Earth's surface using multiple bands of electromagnetic waves (e.g., visible light, near-infrared light, etc.). The optical satellite (12) may include the Arirang satellite, but the present disclosure is not limited thereto. Additionally, the aviation system (14) can acquire aerial image data of the Earth's surface by using a camera, radar, lidar, etc., mounted on an aircraft. The aviation system (14) can collect aerial image data through various platforms (e.g., including a national land information platform).

[0048] The solar power generation prediction device (100) can collect data from an environmental satellite (16) and a weather observation system (18). The solar power generation prediction device (100) can analyze the collected data and predict data related to the environment.

[0049] Here, the environmental satellite (16) collects data related to weather and environment and can precisely monitor weather observations, air pollution, changes in Earth's temperature, cloud distribution, weather, etc., and may include a weather satellite. The environmental satellite (16) may include GK2A (Geostationary Korea Multi-Purpose Satellite-2A), which rotates at the same speed as the Earth's rotation, or Sentinel-5p, which monitors air pollution, climate change, and atmospheric components, but the present disclosure is not limited thereto.

[0050] The weather observation system (18) can observe data related to temperature, humidity, wind speed, wind direction, atmospheric pressure, precipitation, visibility, clouds, etc. The weather observation system (18) may include an ASOS (Automated Surface Observing System), but is not limited thereto.

[0051] The weather forecasting system (20) may include a numerical forecasting model for weather forecasting and may provide weather prediction services and weather forecasting services. The weather forecasting system (20) may include LDAPS (Limited Area Data Assimilation and Prediction System), GFS (Global Forecast System), etc., but the present disclosure is not limited thereto.

[0052] The solar power generation prediction device (100) can perform calculations based on collected data. The solar power generation prediction device (100) can acquire land cover data based on satellite aerial imagery data. The land cover data may include data associated with at least one of forests, farmland, water bodies, roads, railways, bare land, or buildings, and may include data classifying natural or artificial cover on the Earth's surface.

[0053] The solar power generation prediction device (100) can predict environmental data and solar radiation data by analyzing data collected from an environmental satellite (16) based on wavelength. In addition, the solar power generation prediction device (100) can predict air quality data based on environmental data and land cover data.

[0054] A solar power generation prediction device (100) can predict at least one of direct solar radiation reaching the surface, scattered solar radiation, and / or surface temperature based on at least one of land cover data, data related to air quality, solar radiation data and / or temperature data.

[0055] The solar power generation prediction device (100) can estimate the module temperature of the solar panel based on the predicted surface temperature, and can predict the solar power generation based on at least one of the estimated module temperature of the solar panel, the predicted direct solar radiation reaching the surface, or the scattered solar radiation.

[0056] The solar power generation prediction device (100) performs calculations related to calculation or prediction based on collected data, and then, when it receives a request for information related to solar power generation from a user terminal (30) (S24), it can respond to the request (S26).

[0057] FIG. 2 is a schematic diagram showing a configuration in which an information processing system is connected to communicate with a plurality of user terminals to predict solar power generation by reflecting land cover data of the surface according to one embodiment of the present disclosure.

[0058] Referring to FIG. 2, a plurality of user terminals (210_1, 210_2, 210_3) can be connected to an information processing system (230) that predicts solar power generation by reflecting land cover data through a network (220). Here, the plurality of user terminals (210_1, 210_2, 210_3) may include a terminal that requests data related to solar power generation.

[0059] In one embodiment, the information processing system (230) may include a computer-executable program (e.g., a downloadable application) for predicting solar power generation based on various data including land cover data, and one or more server devices and / or databases capable of storing, providing, and executing data, or one or more distributed computing devices and / or distributed databases based on cloud computing services.

[0060] Information related to solar power generation provided by the information processing system (230) may be provided to the user through an image generation application web browser or web browser extension program installed on each of the multiple user terminals (210_1, 210_2, 210_3). For example, the information processing system (230) may provide information related to solar power generation received from the user terminals (210_1, 210_2, 210_3) through the application, etc., or perform corresponding processing.

[0061] Multiple user terminals (210_1, 210_2, 210_3) can communicate with an information processing system (230) through a network (220). The network (220) can be configured to enable communication between the multiple user terminals (210_1, 210_2, 210_3) and the information processing system (230). Depending on the installation environment, the network (220) may be configured as a wired network such as Ethernet, Power Line Communication, telephone line communication devices and RS-serial communication, a mobile communication network, a Wireless LAN (WLAN), Wi-Fi, Bluetooth and ZigBee, or a combination thereof. The communication method is not limited and may include not only communication methods utilizing communication networks that the network (220) may include (e.g., mobile communication network, wired internet, wireless internet, broadcasting network, satellite network, etc.) but also short-range wireless communication between user terminals (210_1, 210_2, 210_3).

[0062] In FIG. 2, a mobile phone terminal (210_1), a tablet terminal (210_2), and a PC terminal (210_3) are illustrated as examples of user terminals, but are not limited thereto. The user terminals (210_1, 210_2, 210_3) may be any computing device capable of wired and / or wireless communication and capable of installing and running GUI service applications or web browsers, or GUI service applications or web browsers, etc. For example, user terminals may include AI speakers, smartphones, mobile phones, navigation systems, computers, laptops, digital broadcasting terminals, PDAs (Personal Digital Assistants), PMPs (Portable Multimedia Players), tablet PCs, game consoles, wearable devices, IoT (Internet of Things) devices, VR (Virtual Reality) devices, AR (Augmented Reality) devices, set-top boxes, etc. Additionally, FIG. 2 illustrates three user terminals (210_1, 210_2, 210_3) communicating with an information processing system (230) through a network (220), but is not limited thereto, and may be configured so that a different number of user terminals communicate with an information processing system (230) through a network (220).

[0063] In FIG. 2, a configuration in which user terminals (210_1, 210_2, 210_3) receive prediction information related to solar power generation by communicating with an information processing system (230) is illustrated as an example, but is not limited thereto. For example, user terminals (210_1, 210_2, 210_3) can predict solar power generation without communicating with the information processing system (230).

[0064] FIG. 3 is a block diagram showing the internal configuration of a user terminal and an information processing system according to one embodiment of the present disclosure.

[0065] Referring to FIG. 3, the user terminal (210) may refer to any computing device capable of running applications, web browsers, etc., and capable of wired / wireless communication, and may include, for example, the mobile phone terminal (210_1), tablet terminal (210_2), PC terminal (210_3) of FIG. 2. As illustrated, the user terminal (210) may include memory (312), a processor (314), a communication module (316), and an input / output interface (318). Similarly, the information processing system (230) may include memory (332), a processor (334), a communication module (336), and an input / output interface (338). As illustrated in FIG. 3, the user terminal (210) and the information processing system (230) may be configured to communicate information and / or data through a network (220) using their respective communication modules (316, 336). Additionally, the input / output device (320) may be configured to input information and / or data to the user terminal (210) or output information and / or data generated from the user terminal (210) through the input / output interface (318).

[0066] The memory (312, 332) may include any non-transient computer-readable recording medium. According to one embodiment, the memory (312, 332) may include a permanent mass storage device such as ROM (read-only memory), a disk drive, a solid-state drive (SSD), or flash memory. As another example, a permanent mass storage device such as ROM, an SSD, flash memory, or a disk drive may be included in the user terminal (210) or information processing system (230) as a separate permanent storage device distinct from the memory. Additionally, an operating system and at least one program code may be stored in the memory (312, 332).

[0067] These software components may be loaded from a computer-readable recording medium separate from memory (312, 332). This separate computer-readable recording medium may include a recording medium that can be directly connected to the user terminal (210) and the information processing system (230), for example, a computer-readable recording medium such as a floppy drive, disk, tape, DVD / CD-ROM drive, or memory card. As another example, the software components may be loaded into memory (312, 332) via a communication module (316, 336) rather than a computer-readable recording medium. For example, at least one program may be loaded into memory (312, 332) based on a computer program installed by files provided through a network (220) by developers or a file distribution system that distributes installation files for the application.

[0068] The processor (314, 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 processor (314, 334) by memory (312, 332) or a communication module (316, 336). For example, the processor (314, 334) may be configured to execute instructions received according to program code stored in a recording device such as memory (312, 332).

[0069] The communication module (316, 336) may provide a configuration or function for the user terminal (210) and the information processing system (230) to communicate with each other via the network (220), and may provide a configuration or function for the user terminal (210) and / or the information processing system (230) to communicate with another user terminal or another system (e.g., a separate cloud system). For example, a request or data generated by the processor (314) of the user terminal (210) according to program code stored in a recording device such as memory (312) may be transmitted to the information processing system (230) via the network (220) under the control of the communication module (316). Conversely, a control signal or command provided under the control of the processor (334) of the information processing system (230) may be received by the user terminal (210) through the communication module (316) of the user terminal (210) via the communication module (336) and the network (220).

[0070] The input / output interface (318) may be a means for interfacing with an input / output device (320). As an example, the input device may include a device such as a camera including an audio sensor and / or an image sensor, a keyboard, a microphone, or a mouse, and the output device may include a device such as a display, a speaker, or a haptic feedback device. As another example, the input / output interface (318) may be a means for interfacing with a device in which the configuration or function for performing input and output is integrated into one, such as a touchscreen. For example, when the processor (314) of the user terminal (210) processes instructions of a computer program loaded in memory (312), a service screen configured using information and / or data provided by an information processing system (230) or another user terminal may be displayed on a display through the input / output interface (318). In FIG. 3, the input / output device (320) is depicted as not being included in the user terminal (210), but is not limited thereto and may be configured as a single device with the user terminal (210). Additionally, the input / output interface (338) of the information processing system (230) may be a means for interfacing with a device for input or output (not shown, such as a display module or device) that is connected to the information processing system (230) or that the information processing system (230) may include. In FIG. 3, the input / output interface (318, 338) is shown as an element configured separately from the processor (314, 334), but is not limited thereto, and the input / output interface (318, 338) may be configured to be included in the processor (314, 334).

[0071] The user terminal (210) and the information processing system (230) may include more components than those of FIG. 3. However, it is not necessary to clearly illustrate most of the prior art components. In one embodiment, the user terminal (210) may be implemented to include at least some of the input / output devices (320) described above. Additionally, the user terminal (210) may further include other components such as a transceiver, a GPS (Global Positioning System) module, a camera, various sensors, a database, etc. Additionally, the information processing system (230) may include at least some of the input / output devices, although not illustrated.

[0072] While a program for training an artificial neural network model, an image generation application, etc. is running, the processor (314) can receive text, images, video, voice and / or actions, etc. that are input or selected through an input device such as a touch screen, keyboard, audio sensor and / or image sensor, camera, microphone, etc. connected to an input / output interface (318), and can store the received text, images, video, voice and / or actions, etc. in memory (312) or provide them to an information processing system (230) through a communication module (316) and a network (220).

[0073] The processor (314) of the user terminal (210) may be configured to manage, process, and / or store information and / or data received from an input / output device (320), another user terminal, an information processing system (230), and / or multiple external systems. The information and / or data processed by the processor (314) may be provided to the information processing system (230) through a communication module (316) and a network (220). The processor (314) of the user terminal (210) may transmit information and / or data to the input / output device (320) through an input / output interface (318) to output it. For example, the processor (314) may output or display the received information and / or data on the screen of the user terminal (210).

[0074] The processor (334) of the information processing system (230) may be configured to manage, process, and / or store information and / or data received from a plurality of user terminals (210) and / or a plurality of external systems. Information and / or data processed by the processor (334) may be provided to the user terminal (210) through a communication module (336) and a network (220). Additionally, if the information processing system (230) includes an input / output device (not shown), information, data, images, etc., may be output through the input / output device. In the case of GUI-based content shown below, it may be displayed on the input / output device through the input / output interface (318, 338) of the user terminal (210) or the information processing system (230). Processing described in various embodiments in the drawings below may be performed by the processor (314, 334).

[0075] FIG. 4 is a drawing for explaining in detail a method (400) for predicting solar power generation by reflecting land cover data of the surface according to one embodiment of the present disclosure.

[0076] Referring to FIG. 4, the processor may include a configuration (AA) associated with data collection and a process (BB) associated with data operation.

[0077] The processor can receive satellite aerial image data (S416). The processor can collect satellite aerial image data through an optical satellite (412) and an aviation system (414).

[0078] The processor can acquire land cover data (418) based on satellite aerial imagery data. The processor can classify and predict land cover types (e.g., cities, forests, bodies of water, etc.) by analyzing multispectral or high-resolution images.

[0079] In one embodiment, the processor may acquire land cover data by inputting optical satellite image data and aerial image data into a first model trained based on deep learning. Here, the acquired land cover data may include data associated with at least one of forests, agricultural land, water bodies, roads, railways, bare land, or buildings, but the present disclosure is not limited thereto. The processor may update the acquired land cover data at a preset interval. For example, the processor may update the state of the land cover data at intervals of 10 to 15 minutes, but the present disclosure is not limited thereto.

[0080] The processor can receive data collected from an environmental satellite (422) and a weather observation system (424) (S426). The processor can obtain data related to the environment by analyzing based on wavelength and predict solar radiation data.

[0081] The weather observation system (424) can collect data such as temperature, humidity, wind speed, wind direction, atmospheric pressure, cloud height, precipitation, and visibility, but the present disclosure is not limited thereto.

[0082] In one embodiment, the processor may obtain data related to particles constituting the environment, data related to climate change indicators, data related to water resources, data related to natural disasters, etc., for wavelength bands such as ultraviolet light (100 to 400 nm), visible light (400 to 700 nm), and near-infrared light (700 to 1400 nm), but the present disclosure is not limited thereto. In addition, the processor may predict data related to solar radiation energy using wavelength bands such as visible light and near-infrared light.

[0083] In one embodiment, the processor can predict data related to air quality based on land cover data and data related to the environment (428). In this case, the data related to air quality may include data related to aerosols. Aerosols may include solids or particulates suspended in the atmosphere.

[0084] Data related to air quality may include data related to fine dust (PM2.5, PM10, etc.), ozone, sulfur dioxide, nitrogen dioxide, carbon monoxide, carbon dioxide, methane, etc., but the present disclosure is not limited thereto.

[0085] The processor can predict direct solar radiation reaching the surface and / or scattered solar radiation based on at least one of land cover data, data related to air quality, solar radiation data or temperature data (430).

[0086] Here, direct solar radiation reaching the surface is solar radiation that reaches the surface directly, and scattered solar radiation may be solar radiation energy that reaches the surface after sunlight passes through the atmosphere and is scattered by molecules in the air, aerosols, clouds, etc.

[0087] The processor can predict the surface temperature based on at least one of land cover data (S432), data associated with air quality (S434), solar radiation data (S436), or temperature data (440).

[0088] The processor can estimate the module temperature of a solar panel based on the predicted surface temperature. In this case, the processor may estimate the module temperature by additionally utilizing characteristics such as the module temperature being higher than the surface temperature, the inclination of the installed solar panel, wind speed, and solar irradiance reaching the surface, but the present disclosure is not limited thereto.

[0089] The processor can predict solar power generation based on at least one of the estimated module temperature of the solar panel, predicted direct solar irradiance or scattered solar irradiance (450).

[0090] The processor can predict solar power generation based on the collection cycle of data collected from the environmental satellite and the weather observation system. For example, if data is received from the environmental satellite (422) and the weather observation system (424) at a cycle of 10 to 15 minutes, the solar power generation can be predicted at a cycle of 10 to 15 minutes. However, the prediction cycle is not limited to this.

[0091] The processor can store data (S462) collected from a configuration (AA) associated with data collection and data (S464) collected from a process (BB) associated with data operation in a data lake. Specifically, the processor can create a data lake based on at least one of satellite aerial imagery data, acquired land cover data, data collected from environmental satellites and weather observation systems, solar radiation data, data associated with air quality, temperature data, weather forecast data, predicted direct solar radiation reaching the surface, scattered solar radiation, surface temperature, estimated module temperature of a solar panel, or predicted solar power generation. The processor can update the data lake according to the collection cycle of the collected data. Additionally, the processor can respond to a request message associated with the solar power generation of a user terminal based on the created data lake.

[0092] FIG. 5 is a diagram illustrating the process of calculating and verifying the module temperature of a solar panel according to one embodiment of the present disclosure.

[0093] Referring to FIG. 5, the processor can acquire numerical weather prediction data (NWPD) (510). The numerical weather prediction data may include data such as time, solar radiation reaching the surface (512), and wind speed. Here, the solar radiation (512) may include beam solar radiation that reaches the surface directly without scattering or absorption by the atmosphere, and scattered solar radiation that reaches the surface after scattering by the atmosphere.

[0094] In the separation step (S520), the processor can separate direct solar radiation reaching the surface (beam solar radiation) and scattered solar radiation.

[0095] The processor can calculate the solar radiation reaching the inclined surface of the solar panel based on the direct solar radiation reaching the surface and the scattered solar radiation (S522) separated in the switching step (S530).

[0096] In the effective solar irradiance calculation step (S540), the processor can calculate the effective solar irradiance based on the solar irradiance reaching the inclined surface of the calculated solar panel (S532) and the reflectivity of the solar panel.

[0097] In the step of calculating the module temperature of the solar panel (S550), the processor can calculate the module temperature of the solar panel based on the effective solar irradiance (S542).

[0098] In the solar panel module temperature verification step (S560), the processor can adjust the estimated solar panel module temperature by comparing the estimated solar panel module temperature with the calculated solar panel module temperature.

[0099] The processor can calculate the solar radiation reaching the inclined surface of the solar panel based on the solar radiation reaching the surface, calculate the effective solar radiation of the solar panel based on the solar radiation reaching the inclined surface of the solar panel and the reflectance, and calculate the module temperature of the solar panel based on the calculated effective solar radiation of the solar panel and information related to the temperature of the solar panel.

[0100] At this time, the processor can verify the estimated module temperature of the solar panel based on the calculated module temperature of the solar panel.

[0101] FIG. 6 is a drawing showing a graph related to photovoltaic power generation efficiency according to one embodiment of the present disclosure.

[0102] Referring to the first graph (610), the processor can set a short-circuit current (612) that occurs when the voltage of the solar panel is 0V in a short-circuit state without the circuit being open, for the maximum output operating current (616). Additionally, the processor can set an open-circuit voltage (622) that is the maximum voltage when the circuit is open for the maximum output operating voltage (618). Furthermore, the processor can set an optimal operating point (614) that satisfies the maximum output operating current (616) and the maximum output operating voltage (618).

[0103] Referring to the second graph (620), the processor can display the voltage versus current according to the solar irradiance. For example, if the measured solar irradiance is 600 W / m 2 (628), 800 W / m 2 (628), 1000 W / m 2 (628) It can be confirmed that as the value increases, a higher current value is observed.

[0104] Referring to the third graph (630), it can be seen that when the module temperature of the solar panel is higher than 25 degrees Celsius, the power efficiency decreases.

[0105] In one embodiment, the processor uses the module temperature of the solar panel to improve the solar power generation efficiency (Z PV ) can be calculated based on the following [Mathematical Formula 1].

[0106] [Mathematical Formula 1]

[0107] Z PV = Q / GA = B * Zr * pF (1-C (T - Tr)),

[0108] (Q: Power output of the solar cell (W), G: Total solar irradiance, A: Collector area, B: Transmittance of the glass cover, Zr: Solar efficiency at reference temperature, pF: Packaging factor, C: Solar temperature coefficient according to output, T: Average solar temperature)

[0109] FIG. 7 is a flowchart illustrating a method for predicting solar power generation according to one embodiment of the present disclosure.

[0110] In step S710, the processor can acquire land cover data based on satellite aerial imagery data.

[0111] Specifically, the processor can acquire land cover data by inputting optical satellite image data and aerial image data into a first model trained based on deep learning. The acquired land cover data may include data associated with at least one of forests, farmland, water bodies, roads, railways, bare land, or buildings.

[0112] In step S720, the processor can predict environment-related data and solar radiation data by analyzing data collected from environmental satellites and weather observation systems based on wavelength. The processor can acquire environment-related data and predict solar radiation data, but is not limited to these.

[0113] In step S730, the processor can predict data related to air quality based on land cover data and data related to the environment.

[0114] In step S740, the processor can predict at least one of direct solar radiation reaching the surface, scattered solar radiation, or surface temperature based on at least one of land cover data, data associated with air quality, solar radiation data, or temperature data.

[0115] In step S750, the processor can estimate the module temperature of the solar panel based on the predicted surface temperature.

[0116] In step S760, the processor can predict solar power generation based on at least one of the estimated module temperature of the solar panel, the predicted solar irradiance reaching the surface, or the scattered solar irradiance.

[0117] The processor can predict solar power generation based on the data collection cycle of environmental satellites and weather observation systems.

[0118] The processor can generate a data lake comprising at least one of satellite aerial imagery data, acquired land cover data, data collected from environmental satellites and weather observation systems, solar radiation data, data related to air quality, temperature data, weather forecast data, predicted direct solar radiation reaching the surface, scattered solar radiation, surface temperature, estimated module temperature of a solar panel, or predicted solar power generation.

[0119] The processor can respond to request messages from a user terminal based on a generated data lake. The processor can respond in real-time to real-time request messages from the user terminal. For example, the processor may receive a REST API request message from the user terminal associated with a request to provide at least one of forecast information regarding air quality, solar radiation, or solar power generation. The processor may provide a response message (JSON / XML response message) to the user terminal in real-time regarding the request for provision associated with at least one of forecast information regarding air quality, solar radiation, or solar power generation. Accordingly, user convenience may be enhanced.

[0120] FIG. 8 is a flowchart illustrating a method for calculating the module temperature of a solar panel according to one embodiment of the present disclosure.

[0121] In step S810, the process can calculate the solar radiation reaching the inclined surface of the solar panel based on the solar radiation reaching the surface.

[0122] In step S820, the processor can calculate the effective solar irradiance of the solar panel based on the solar irradiance reaching the inclined surface of the solar panel and the reflectance.

[0123] In step S830, the processor can calculate the module temperature of the solar panel based on information related to the calculated effective solar irradiance of the solar panel and the temperature of the solar panel.

[0124] Based on the calculated module temperature of the solar panel, the processor can verify the estimated module temperature of the solar panel in step S760.

[0125] The sequence diagrams of FIGS. 7 and 8 and the description above are merely examples and the scope of the present disclosure is not limited thereto. For example, at least one step may be added, changed, or deleted, or the order of each step may be changed.

[0126] The method described above may be provided as a computer program stored on a computer-readable recording medium for execution on a computer. The medium may continuously store a program executable by a computer, or temporarily store it for execution or download. Additionally, the medium may be various recording or storage means in the form of a single or multiple hardware components combined, and may not be limited to a medium directly connected to a computer system but may exist distributed over a network. Examples of media may 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 media configured to store program instructions, including ROM, RAM, and flash memory. Furthermore, other examples of media may include recording or storage media managed by app stores that distribute applications or sites and servers that supply or distribute various other software.

[0127] The methods, operations, or techniques of the present 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 exemplary logical blocks, modules, circuits, and algorithmic steps described in connection with the disclosure herein may be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate such interchangeability between hardware and software, various exemplary components, blocks, modules, circuits, and steps have been generally described above in terms of their functional aspects. Whether such functions are implemented in hardware or in software depends on the design requirements imposed on the specific application and the overall system. Those skilled in the art may implement the functions described in various ways for each specific application, but such implementations should not be construed as departing from the scope of the present disclosure.

[0128] In a hardware implementation, the processing units used to perform the techniques may be implemented in one or more ASICs, DSPs, GPUs, digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, electronic devices, other electronic units designed to perform the functions described in this disclosure, computers, or a combination thereof.

[0129] Accordingly, the various exemplary logic blocks, modules, and circuits described in connection with the present disclosure may be implemented or performed by any combination of general-purpose processors, DSPs, ASICs, FPGAs or other programmable logic devices, discrete gate or transistor logic, discrete hardware components, or those designed to perform the functions described herein. A general-purpose processor may be a microprocessor, but alternatively, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, for example, a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors coupled with a DSP core, or any other combination of configurations.

[0130] In firmware and / or software implementations, techniques may be implemented as instructions stored on a computer-readable medium such as 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), electrically erasable PROM (EEPROM), flash memory, compact disc (CD), magnetic or optical data storage devices, etc. The instructions may be executable by one or more processors, and the processor(s) may be enabled to perform specific aspects of the functions described in this disclosure.

[0131] When implemented in software, techniques may be stored on a computer-readable medium as one or more instructions or code, or transmitted through a computer-readable medium. Computer-readable media include both computer storage media and communication media, including any medium that facilitates the transmission of a computer program from one place to another. Storage media may be any available medium that can be accessed by a computer. As a 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 any other medium that can be used to transfer or store desired program code in the form of instructions or data structures and can be accessed by a computer. Additionally, any connection is appropriately made to the computer-readable medium.

[0132] For example, if 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, coaxial cable, fiber optic cable, twisted pair cable, digital subscriber line, or wireless technologies such as infrared, radio, and microwave are included within the definition of a medium. As used herein, disk and disc include CD, laser disc, optical disc, DVD (digital versatile disc), floppy disk, and Blu-ray disc, wherein disks usually play data magnetically, whereas discs play data optically using a laser. The above combinations should also be included within the scope of computer-readable media.

[0133] The software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, a hard disk, a removable disk, a CD-ROM, or any other known form of storage medium. An exemplary storage medium may be connected to a processor so that the processor can read information from the storage medium or write information to the storage medium. Alternatively, the storage medium may be integrated into the processor. The processor and the storage medium may exist within an ASIC. The ASIC may exist within a user terminal. Alternatively, the processor and the storage medium may exist as separate components within the user terminal.

[0134] Although the embodiments described above have been described as utilizing aspects of the subject matter disclosed herein in one or more standalone computer systems, the present disclosure is not limited thereto and may be implemented in conjunction with any computing environment, such as a network or a distributed computing environment. Furthermore, aspects of the subject matter in the present disclosure may be implemented in a plurality of processing chips or devices, and storage may be similarly affected across a plurality of devices. Such devices may include PCs, network servers, and portable devices.

[0135] Although the present disclosure has been described in relation to some embodiments, various modifications and changes may be made without departing from the scope of the present disclosure as understood by a person skilled in the art to which the invention of the present disclosure pertains. Furthermore, such modifications and changes should be considered to fall within the scope of the claims appended to this specification.

Claims

In a method for predicting solar power generation performed by at least one processor, A step of acquiring land cover data based on satellite aerial imagery data; A step of obtaining environment-related data and predicting solar radiation data by analyzing data collected from environmental satellites and weather observation systems based on wavelength; A step of predicting data related to air quality based on the above land cover data and data related to the environment; A step of predicting at least one of direct solar radiation reaching the surface, scattered solar radiation, or surface temperature based on at least one of the above land cover data, above air quality associated data, above solar radiation data, or above temperature data; A step of estimating the module temperature of the solar panel based on the above-mentioned predicted surface temperature; and A step of predicting solar power generation based on at least one of the estimated module temperature of the solar panel, the predicted direct solar irradiance reaching the surface, or scattered solar irradiance. A method for predicting solar power generation, including In paragraph 1, The step of acquiring the above land cover data is, A step of acquiring the land cover data by inputting optical satellite image data and aerial image data into a first model trained based on deep learning. Includes, The land cover data obtained above is, A method for predicting solar power generation, comprising data associated with at least one of forests, farmland, water bodies, roads, railways, bare land, or buildings. In paragraph 1, The step of predicting the above solar power generation amount is, A step of predicting the amount of solar power generation based on the collection cycle of data collected from the above environmental satellite and weather observation system A method for predicting solar power generation, including In paragraph 1, A step of generating a data lake comprising at least one of the above satellite aerial imagery data, the above acquired land cover data, the above environmental satellite and weather observation system, the above solar radiation data, the above atmospheric quality-related data, the above temperature data, weather forecast data, the above predicted direct solar radiation reaching the surface, scattered solar radiation, surface temperature, the above estimated module temperature of the solar panel, or the above predicted solar power generation amount. A solar power generation prediction method that further includes In paragraph 4, After the step of creating the above data lake, A step of responding in real time to a request message from a user terminal based on the above-mentioned generated data lake. A solar power generation prediction method that further includes In paragraph 1, A step of calculating the solar radiation reaching the inclined surface of the solar panel based on the solar radiation directly reaching the surface and scattered solar radiation; A step of calculating the effective solar irradiance of the solar panel based on the solar irradiance reaching the inclined surface of the solar panel and the reflectance; and A step of calculating the module temperature of the solar panel based on information related to the effective solar irradiance of the solar panel calculated above and the temperature of the solar panel. A solar power generation prediction method that further includes In paragraph 6, After the step of calculating the module temperature of the above solar panel, A step of verifying the estimated module temperature of the solar panel based on the module temperature of the solar panel calculated above. A solar power generation prediction method that further includes A computer-readable, non-transient recording medium recording instructions for executing the method according to paragraph 1 on a computer. In information processing systems, Memory; and A processor connected to the memory and configured to execute at least one computer-readable program contained in the memory. Includes, The above at least one program is, Acquire land cover data based on satellite aerial imagery data, and By analyzing data collected from environmental satellites and weather observation systems based on wavelength, we obtain environment-related data and predict solar radiation data, and Predicting air quality-related data based on the above land cover data and the above environment-related data, and Based on at least one of the above land cover data, above air quality associated data, above solar radiation data or temperature data, at least one of direct solar radiation reaching the surface, scattered solar radiation, or surface temperature is predicted, and Estimate the module temperature of the solar panel based on the above-mentioned predicted surface temperature, and An information processing system comprising instructions for predicting solar power generation based on at least one of the estimated module temperature of the solar panel, the predicted direct solar irradiance or scattered solar irradiance. In Paragraph 9, The above at least one program is, It includes instructions for acquiring the land cover data by inputting optical satellite image data and aerial image data into a first model trained based on deep learning, and The land cover data obtained above is, An information processing system comprising data associated with at least one of forests, farmland, water bodies, roads, railways, bare land, or buildings.

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