Server for controlling and trading energy resource based on predicitive model and method operation thereof

Abstract

According to various embodiments, a server for controlling and trading energy resources based on a prediction model comprises a communication interface and a processor, wherein the processor receives at least one energy data from at least one external electronic device for energy management through the communication interface, inputs the at least one energy data into a prediction model to determine at least one power generation prediction data, and is configured to transmit the at least one power generation prediction data to an external electronic device of at least one manager and / or buyer through the communication interface, and the prediction model is learned based on a plurality of energy data, a plurality of power generation prediction data, a first power generation efficiency data in which the plurality of power generation prediction data is calculated as the highest efficiency value, a second power generation efficiency data in which the plurality of power generation prediction data is calculated as the next efficiency value of the first power generation efficiency data, a third power generation efficiency data in which the plurality of power generation prediction data is calculated as the next efficiency value of the second power generation efficiency data, and a fourth power generation efficiency data in which the plurality of power generation prediction data is calculated as the lowest efficiency value.

Description

Technology Field

[0001] The present invention relates to a server for controlling and trading energy resources based on a prediction model and a method for operating the same. Background Technology

[0002] Conventionally, there were limitations in managing heterogeneous distributed energy resources, such as solar power, wind power, and energy storage systems (ESS), integrally on a single platform because they each possess distinct communication protocols and operational characteristics. Conventional energy management methods often utilized separate control systems for each facility or were limited to simple monitoring, making it difficult to comprehensively analyze power generation, facility status, and fault information. Consequently, interconnected operation between facilities was restricted, leading to reduced operational efficiency and increased management costs.

[0003] Traditionally, despite the significant fluctuations in renewable energy generation depending on weather conditions, there were issues with insufficient accuracy in generation forecasts or a lack of organic integration between forecast results and the power trading bidding process. Conventional power trading methods relied on manual processes or partial automation for generation forecasting, calculation of eligible bidding capacity, and the preparation and submission of bids, making it difficult to respond quickly and accurately to both day-ahead and real-time markets. Consequently, problems such as disadvantages caused by forecasting errors, bid failures, or reduced profits resulting from conservative bidding have persisted.

[0004] Traditionally, small-scale distributed resources faced limitations in effectively participating in the power market due to scale requirements and operational complexity. Conventional systems failed to provide a structure capable of integrating and managing multiple small-scale resources as aggregated resources and operating them as a single virtual power plant. Consequently, the potential value of distributed resources was not properly utilized in the power market, and demand response and load management were limited to a restricted scope. Prior art literature

[0005] Korean Published Patent Application No. 10-2025-0146655 (Oct. 13, 2025) Korean Registered Patent Application No. 10-2703932 (Sept. 3, 2024) The problem to be solved

[0006] The present invention aims to solve the above-mentioned problem by providing a server and a method for operating the same, for inputting at least one energy data obtained from a user's external electronic device into a prediction model to output determined power generation energy prediction data in various ways and providing it to a manager and / or buyer. means of solving the problem

[0007] According to various embodiments, a server for controlling and trading energy resources based on a prediction model comprises a communication interface and a processor, wherein the processor receives at least one energy data from at least one external electronic device for energy management through the communication interface, inputs the at least one energy data into a prediction model to determine at least one power generation prediction data, and is configured to transmit the at least one power generation prediction data to an external electronic device of at least one manager and / or buyer through the communication interface, and the prediction model is learned based on a plurality of energy data, a plurality of power generation prediction data, a first power generation efficiency data in which the plurality of power generation prediction data is calculated as the highest efficiency value, a second power generation efficiency data in which the plurality of power generation prediction data is calculated as the next efficiency value of the first power generation efficiency data, a third power generation efficiency data in which the plurality of power generation prediction data is calculated as the next efficiency value of the second power generation efficiency data, and a fourth power generation efficiency data in which the plurality of power generation prediction data is calculated as the lowest efficiency value.

[0008] According to other various embodiments, in a method for operating a server for controlling and trading energy resources based on a prediction model, the method is configured to receive at least one energy data from at least one external electronic device for energy management through a communication interface, input the at least one energy data into a prediction model through a processor to determine at least one power generation prediction data, and transmit the at least one power generation prediction data to the at least one external electronic device of a manager and / or buyer through the communication interface, and the prediction model is learned based on a plurality of energy data, a plurality of power generation prediction data, a first power generation efficiency data in which the plurality of power generation prediction data is calculated as the highest efficiency value, a second power generation efficiency data in which the plurality of power generation prediction data is calculated as the next efficiency value of the first power generation efficiency data, a third power generation efficiency data in which the plurality of power generation prediction data is calculated as the next efficiency value of the second power generation efficiency data, and a fourth power generation efficiency data in which the plurality of power generation prediction data is calculated as the lowest efficiency value. Effects of the invention

[0009] The server according to the present embodiment has the advantage of being able to manage at least one energy data obtained from a solar power device, a wind power device, and an energy management system in a platform form in real time, input it into a prediction model to determine at least one power generation prediction data, and output at least one power generation data in a platform structure to perform status and bidding effects regarding the power generation energy in real time. Brief explanation of the drawing

[0010] A brief description of each drawing is provided to help to better understand the drawings cited in the detailed description of the invention. FIG. 1 is a block diagram of a server and network according to an embodiment of the present invention; FIG. 2 is a specific block diagram of a server according to an embodiment of the present invention; FIG. 3 is a structural diagram of a prediction model according to an embodiment of the present invention; FIG. 4 is a flowchart for a method of server operation according to an embodiment of the present invention; FIG. 5 is an exemplary diagram of an overall operating system for a server according to an embodiment of the present invention; FIG. 6 is an exemplary diagram for the operational status of a server according to an embodiment of the present invention; FIG. 7 is an example of an output for at least one energy data received from a server according to an embodiment of the present invention; FIG. 8 is a comprehensive example diagram of power generation prediction data determined by a server from a prediction model according to an embodiment of the present invention; FIG. 9 is an example diagram of result value output for power generation prediction data determined by a server from a prediction model according to an embodiment of the present invention; FIG. 10 is a real-time comparison example of power generation prediction data determined by a server from a prediction model according to an embodiment of the present invention. Specific details for implementing the invention

[0011] Hereinafter, various embodiments of the present invention are described with reference to the accompanying drawings. The present invention is not limited to specific embodiments and should be understood to include various modifications, equivalents, and / or alternatives of the embodiments of the present invention. In connection with the description of the drawings, similar reference numerals may be used for similar components.

[0012] In this document, expressions such as "have," "can have," "include," or "can include" refer to the existence of the relevant feature (e.g., numerical values, functions, actions, or components, etc.) and do not exclude the existence of additional features.

[0013] In this document, expressions such as “A or B,” “at least one of A or / and B,” or “one or more of A or / and B” may include all possible combinations of items listed together. For example, “A or B,” “at least one of A and B,” or “at least one of A or B” may refer to cases including (1) at least one A, (2) at least one B, or (3) both at least one A and at least one B.

[0014] Expressions such as "first," "second," "first," or "second" used in this document may modify various components regardless of order and / or importance, and are used merely to distinguish one component from another without limiting such components. For example, without departing from the scope of rights set forth in this document, the first component may be named the second component, and similarly, the second component may be renamed the first component.

[0015] FIG. 1 is a block diagram of a server and network according to one embodiment of the present invention.

[0016] Referring to FIG. 1, the server (108) can operate an application composed of multiple execution screens or a website composed of multiple web pages, communicates with an electronic device (e.g., the electronic device (101, 102, 104) of FIG. 1) (e.g., smartphone, laptop, etc.) via a network (161), processes requests received from the electronic device (101) regarding the application or web page, and transmits requested information to the electronic device (101). The server (108) can transmit source code to the electronic device (101) that enables each execution screen of the dedicated application or website to be displayed on the electronic device (101), and the electronic device (101) receives the source code and can display an execution screen requested by at least one user of the electronic device (101) through the dedicated application or web browser. According to one embodiment, the components referred to as electronic devices (101) in the present disclosure may mean at least one user account that accesses a platform provided by a server (108) through said electronic device.

[0017] Additionally, the network (161) can communicate with at least one externally installed sensing module (210) and can transmit data obtained from at least one precision sensing module (210) to the server (108).

[0018] FIG. 2 is a specific block diagram of a server according to one embodiment of the present invention.

[0019] Referring to FIG. 2, a server (108) within a network environment (100) in various embodiments is described. The server (108) may include a bus (110), a processor (120), memory (130), an input / output interface (140), a display (150), a communication interface (160), and a database (170). In some embodiments, the server (108) may omit at least one of the components or additionally include other components. The bus (110) may include a circuit that connects the components (110-170) to each other and transmits communication (e.g., control messages or data) between the components. The processor (120) may include one or more of a central processing unit, an application processor, or a communication processor (CP). The processor (120) may, for example, perform operations or data processing regarding the control and / or communication of at least one other component of the server (108).

[0020] The memory (130) may include volatile and / or non-volatile memory. The memory (130) may store commands or data related to at least one other component of the server (108), for example.

[0021] The input / output interface (140) can, for example, transmit commands or data input from a user or other external device to other component(s) of the server (108), or output commands or data received from other component(s) of the server (108) to the user or other external device.

[0022] The display (150) may include, for example, a liquid crystal display (LCD), a light-emitting diode (LED) display, an organic light-emitting diode (OLED) display, a microelectromagnetic system (MEMS) display, or an electronic paper display. The display (150) may display various content (e.g., text, images, videos, icons, and / or symbols, etc.) to a user. The display (150) may include a touch screen and may receive touch, gesture, proximity, or hovering input using, for example, an electronic pen or a part of the user's body. The communication interface (160) may establish communication between, for example, a server (108) and an external device (e.g., a first external electronic device (102), a second external electronic device (104), or the server (108)). For example, the communication interface (160) can be connected to a network (161) via wireless or wired communication to communicate with an external device (e.g., a second external electronic device (104) or a server (108)).

[0023] Wireless communication may include cellular communication using at least one of, for example, LTE, LTE-A (LTE Advance), CDMA (code division multiple access), WCDMA (wideband CDMA), UMTS (universal mobile telecommunications system), WiBro (Wireless Broadband), or GSM (Global System for Mobile Communications). According to one embodiment, wireless communication may include at least one of, for example, WiFi (wireless fidelity), Bluetooth, Bluetooth Low Energy (BLE), Zigbee, NFC (near field communication), Magnetic Secure Transmission, Radio Frequency (RF), or Body Area Network (BAN). According to one embodiment, wireless communication may include GNSS. GNSS may be, for example, GPS (Global Positioning System), Glonass (Global Navigation Satellite System), Beidou Navigation Satellite System (hereinafter “”) or Galileo, the European global satellite-based navigation system. Hereinafter, in this document, “”) may be used interchangeably with “”). Wired communication may include at least one of, for example, USB (universal serial bus), HDMI (high definition multimedia interface), RS-232 (recommended standard 232), power line communication, or POTS (plain old telephone service).The network (161) may include at least one of a telecommunications network, for example, a computer network (e.g., LAN or WAN), the Internet, or a telephone network.

[0024] The database (170) may be implemented in memory (130) or on a separate storage medium. The database (170) may store all contents, details, etc. of data transmitted and received with user terminals (101, 102, 104) or a server (108). Data stored in the database (170) may be updated regularly according to a predetermined period, or may be updated frequently when new data is input through user terminals (101, 102, 104) or a server (108). According to an embodiment of the present disclosure, various information received from user terminals (101, 102, 104) or received from a server (108) may be stored in the database (170).

[0025] FIG. 3 is a structural diagram of a prediction model according to one embodiment of the present invention.

[0026] A prediction model according to one embodiment can be driven by an AI neural network model trained through unsupervised learning on basic information, as illustrated in FIG. 3. The prediction model can be configured to increase the ease of information collection and to provide various information output values. The prediction model is structured to output image information from text information, and at least one of BigScience’s bloom and T0pp, EleutherAI’s GPT series, Tsinghua UNIV’s GLM series, GOOGLE’s UL and T5 series, and META AI’s OPT series may be used. According to one embodiment, the prediction model can be implemented using a plurality of Cloud Foundation models, utilizing the structure of Microsoft’s Chat GPT, Google’s BARD series, and NVIDIA’s translation service-based Transformer model. For example, the prediction model can implement various image renderings by training a multimodal-based model based on at least one of text information, image information, and audio information. In one embodiment, compared to conventional deep learning methods, this can reduce the amount of training information per labeled task, and once established, various training can be performed with a small amount of training information, making information collection and labeling easier and improving accuracy. Additionally, the server (108) can perform the training process of a prediction model for outputting state prediction information by obtaining a result value (output information) using a prediction model to which arbitrary weights are assigned, comparing the obtained result value with the labeling information of the training information, and performing backpropagation according to the error to optimize the weights. Specifically, training of the prediction model refers to a process of training the prediction model based on training information and labeling information or unlabeled information so that the prediction model can determine output information regarding the input information. That is, the prediction model forms rules regarding the information and makes judgments.According to one embodiment, the server (108) may use a plurality of learning algorithms among a plurality of learning algorithms that calculate a predicted value. For example, an ensemble method may be used in the prediction model, and better prediction performance can be obtained compared to when the learning algorithms are used separately. The meaning of training the prediction model may mean adjusting the weights of the model. According to one embodiment, as a learning method, various methods such as supervised learning, unsupervised learning, reinforcement learning, imitation learning, and federated learning may be used.

[0027] Although not described, the server (108) may include an evaluation step for evaluating the performance of the prediction model during the learning process of the prediction model. In the evaluation step, the prediction model may be evaluated using an evaluation information set. The evaluation of the prediction model may be a step of evaluating the prediction model learned by the learning step and making predictions about new information using the prediction model. Specifically, the evaluation step may be a step of measuring whether the learned prediction model is capable of generalization to new information.

[0028] Furthermore, the prediction model is a type of artificial neural network specialized in learning patterns from time-series or sequential information, utilizing a Long Short-Term Memory (LSTM) structure. It is primarily an improved version of the Recurrent Neural Network (RNN). While RNNs predict the future based on past information, they suffer from the Gradient Vanishing problem, which makes learning difficult in long sequences and hinders the processing of long-term dependencies. However, the prediction model (LSTM) can learn long-term dependencies by introducing a special gate structure to control the flow of information and solve this problem. The prediction model can operate by selectively remembering and forgetting information using three gates: an Input Gate, a Forget Gate, and an Output Gate. These gates can perform the following actions at each stage. First, the Input Gate determines how much newly input information to accept; the Forget Gate determines how much previously stored information to forget; and the Output Gate determines the amount of information to be passed from the current state to the next. Based on the three gates at the top, the prediction model learns and adjusts on its own whether past information is needed for current predictions, and learns through learning whether information at the beginning of a sentence is needed at the end of a sentence.Furthermore, the predictive model (LSTM) is a natural language processing tool utilized in sentence generation, translation, and sentiment analysis. It possesses strengths particularly in machine translation, such as its ability to grasp the context of an entire sentence. It can recognize specific words or phrases by processing speech information as a time series, and it can be used to predict future stock prices by learning past stock price fluctuation patterns. It can also be applied to behavior prediction or scene classification by learning changes between frames over time in videos, and it can be used for long-term predictions—for example, the probability of disease onset—by analyzing a user's past medical history and treatment records. Additionally, the predictive model (LSTM) has the advantage of effectively remembering and utilizing long periods of historical information. However, the predictive model (LSTM) has high computational costs, and training times can become prolonged as the amount of information increases. Consequently, other sequential models, such as the Gated Recurrent Unit (GRU) and Transformer models, have emerged and can be selectively used in various situations to overcome these limitations.

[0030] FIG. 4 is a flowchart for a method of server operation according to an embodiment of the present invention.

[0032] In operation 401, the server (108) (e.g., processor (120) of FIG. 2) can receive at least one energy data from at least one energy management external electronic device (101, 102, 104) through a communication interface (e.g., communication interface (160) of FIG. 2).

[0033] According to one embodiment, at least one energy data may include power plant energy data composed of at least one of solar energy data obtained from at least one solar power device, wind energy data obtained from at least one wind power device, and tidal energy data obtained from at least one tidal power device, and energy management data obtained from an Energy Management System (EMS) device for at least one power plant energy data.

[0034] Specifically, at least one solar energy data can be obtained from at least one solar device including a solar panel, an inverter, a junction box, etc. The solar energy data may consist of at least one of the following: power generation amount per hour, cumulative power generation amount, output value per panel, inverter conversion efficiency, solar irradiance, panel temperature, whether shading occurs, and fault status information. By collecting solar energy data in real time or periodically, the server (108) can precisely identify changes in the power generation efficiency of the solar power generation facility, output variability due to weather conditions, and whether there is an abnormality in the facility.

[0035] Additionally, solar energy data can be generated into power plant energy data after being collected in the form of raw sensor data and undergoing preprocessing steps such as normalization, noise removal, and correction of missing data. Additionally, the server (108) can utilize the solar energy data as input data that can be used for predicting solar power generation, estimating equipment degradation, and optimizing the power generation schedule.

[0036] Specifically, at least one wind energy data may be obtained from at least one wind power device comprising a wind turbine, a nacelle, blades, a generator, and a control device. The wind energy data may consist of at least one of wind speed, wind direction, turbine rotational speed, output power, blade pitch angle, generator temperature, vibration information, and fault history information.

[0037] Additionally, the server (108) can analyze the correlation between wind conditions and actual power generation based on wind energy data, and can also determine the output variability of the wind turbine, peak power generation time periods, and abnormal operation conditions. In one embodiment, wind energy data may be classified and stored by time period or by turbine unit, and data collected from multiple wind turbines may reflect the power generation characteristics of the complex unit through integrated analysis.

[0038] Specifically, at least one tidal energy data may be obtained from at least one tidal power device including a tidal turbine, a sluice gate, a water level sensor, and a power generation control device. Additionally, the tidal energy data may consist of at least one of tidal current speed, water level difference, power generation output, turbine rotation status, tidal cycle information, and power generation time zone information.

[0039] The server (108) can process tidal energy data into temporal pattern data based on the tidal period and can also input it into a power generation prediction model that reflects the characteristics of tidal power generation having a certain periodicity. In addition, the tidal energy data can be used to evaluate power generation stability and determine maintenance timing by reflecting changes in the marine environment or equipment load conditions.

[0040] Specifically, power plant energy data can be generated by integrating at least one of solar energy data, wind energy data, and tidal energy data. Power plant energy data may consist of at least one of output information by power source, total power generation, power generation ratio by time of day, complementarity indicators between power sources, and power generation variability indicators.

[0041] The server (108) can comprehensively determine the overall energy production status in a power generation environment where multiple renewable energy sources are mixed based on power plant energy data, and can perform power supply stability evaluation, output control strategy formulation, and grid connection optimization.

[0042] Specifically, energy management data can be obtained from an energy management system device corresponding to power plant energy data. The energy management data may consist of at least one of power generation control commands, output limit information, energy storage device charge / discharge status, load forecast information, power trading information, and grid connection status information.

[0043] The server (108) can determine real-time energy supply and demand balance, power generation adjustment, and energy storage strategies by linking energy management data with power plant energy data. Additionally, the energy management data may consist of policy-based control data or AI-based prediction results, and can be used as basic data to maximize power generation efficiency and minimize operating costs.

[0044] In operation 403, the server (108) (e.g., the processor (120) of FIG. 2) can input at least one energy data into a prediction model to determine at least one power generation prediction data.

[0045] According to another embodiment, the server (108) can calculate power generation comparison data by comparing power plant energy data with previous power plant output data stored in memory (e.g., memory (130) of FIG. 2).

[0046] Specifically, the server (108) can calculate power generation comparison data by comparing power plant energy data collected at the current time with previous power plant output data stored in memory (e.g., memory (130) of FIG. 2). Additionally, the power plant energy data may consist of energy data obtained from integrated power generation data including at least one of solar energy data, wind energy data, and tidal energy data, and the previous power plant output data may consist of data stored in memory (e.g., memory (130) of FIG. 2) as power generation amount, power generation efficiency, output variability, and ratio data by power source during a specific time or period in the past.

[0047] Specifically, the server (108) can generate power generation comparison data by calculating the change in output by power source, the rate of increase or decrease in power generation by time period, the similarity of power generation patterns, and the error data of the actual power generation compared to the expected power generation. Additionally, the server (108) can determine equipment performance degradation, environmental changes, or abnormal power generation conditions by comparing past power generation output data and current power generation energy data under the same weather conditions or similar tidal cycles. Furthermore, the server (108) can utilize the power generation comparison data, which consists of basic data for correcting the side model, determining the need for equipment maintenance, and establishing power generation operation strategies.

[0048] According to another embodiment, the server (108) can produce energy management comparison data by comparing energy management data with previous energy management data stored in memory (e.g., memory (130) of FIG. 2).

[0049] Specifically, the server (108) can produce energy management comparison data by comparing energy management data with previous energy management data stored in memory (e.g., memory (130) of FIG. 2). The energy management data is data obtained from an energy management system device and may consist of at least one of power generation control commands, output limit data, charge / discharge status of an energy storage device, load prediction data, and power grid connection status data.

[0050] Specifically, the server (108) can generate energy management comparison data by comparing the current energy management policy, control history, and storage device operation status with previous energy management data. Additionally, the server (108) can calculate output control efficiency, storage device utilization, and the degree of energy loss by comparing past control results and current control results under the same power plant energy data conditions. Furthermore, the server (108) can use the included energy management comparison data as a judgment criterion value for evaluating the validity of the energy management strategy, improving the energy storage system control algorithm, and enhancing energy supply stability.

[0051] According to another embodiment, the server (108) can determine at least one power generation prediction data by inputting power generation comparison data and management comparison data into a prediction model.

[0052] Specifically, the server (108) can determine at least one power generation prediction data by inputting power generation comparison data and energy management comparison data into a prediction model. The power generation comparison data is data generated by comparing current power plant energy data with previous power plant output data stored in memory (e.g., memory (130) in FIG. 2), and may include data on output change amount by power source, rate of increase or decrease in power generation by time period, similarity of power generation pattern, and change in power generation efficiency. Additionally, the energy management comparison data is data generated by comparing current energy management data with previous energy management data, and may include output control efficiency, energy storage device utilization, history of control policy change, and energy loss indicator data.

[0053] Specifically, the server (108) can configure power generation comparison data and energy management comparison data as input vectors for the prediction model. Additionally, the prediction model is implemented as at least one of a statistical model, a machine learning model, or a deep learning model, and by reflecting the physical power generation characteristics of the power generation facility and the control characteristics of the energy management system together, it can determine at least one power generation prediction data that considers operating conditions rather than a simple power generation estimation.

[0054] Specifically, at least one power generation forecast data may consist of expected power generation data for a specific future point in time or period, contribution data by power source, output stability indicator data, range of energy generation possible, efficiency value data relative to power generation, and profit value data based on purchase. Additionally, the server (108) can determine substantially available power generation energy data under the same environmental conditions by simultaneously considering past performance changes of solar, wind, and tidal power generation and the operating status of the energy storage device. Furthermore, the server (108) can provide more precise power generation energy data that reflects fluctuations in power generation according to operating strategies as well as environmental factors.

[0055] Specifically, the server (108) can utilize at least one determined power generation prediction data as reference data for subsequent control operations. Additionally, the server (108) can dynamically determine power output control, adjustment of the charging and discharging schedule of the energy storage device, or power grid connection strategies based on the power generation energy data. Furthermore, the server (108) can utilize the power generation energy data, which consists of key decision-making indicators reflecting the actual operating environment of the power plant, by comparing it in real-time using various methods and calculating the efficiency relative to the winning bid.

[0056] According to another embodiment, the prediction model can be learned based on a plurality of energy data, a plurality of power generation prediction data, a first power generation efficiency data in which the plurality of power generation prediction data is calculated as the highest efficiency value, a second power generation efficiency data in which the plurality of power generation prediction data is calculated as the next efficiency value of the first power generation efficiency data, a third power generation efficiency data in which the plurality of power generation prediction data is calculated as the next efficiency value of the second power generation efficiency data, and a fourth power generation efficiency data in which the plurality of power generation prediction data is calculated as the lowest efficiency value, as illustrated in FIG. 3.

[0057] In addition, the prediction model can be further trained with multiple solar energy data, multiple wind energy data, multiple tidal energy data, multiple power plant energy data, multiple energy management data, multiple power generation comparison data, and multiple energy management comparison data.

[0058] In addition, since the prediction model for determining at least one power generation prediction data is the same as the artificial intelligence model described in the contents of Fig. 3, a detailed explanation thereof may be omitted.

[0059] In operation 405, the server (108) (e.g., the processor (120) of FIG. 2) can determine whether at least one power generation prediction data exceeds a preset first power generation threshold.

[0060] According to one embodiment, the server (108) can quantify, respectively, the expected power generation data, the contribution data by power source, the output stability indicator data, the power generation potential energy range data, the efficiency value data relative to power generation, and the profit value data included in at least one power generation prediction data.

[0061] Additionally, the server (108) calculates the expected power generation data, the contribution data by power source, the output stability indicator data, the range of energy that can be generated, and the efficiency value data relative to the power generation amount as 0 to 100, respectively, such that the value is calculated as a lower efficiency or a deteriorating value as it approaches 0, and the value is calculated as a higher efficiency or an increased value as it approaches 100.

[0062] Additionally, the server (108) calculates profit value data from -100 to 200, where if it is 0 or less, it is a value where a loss occurs compared to the conventional value, if it is 0 to 100, it is a value where a profit occurs but is greater than each efficiency value compared to the conventional value, and if it is 100 or more, it is a value where the profit increases significantly compared to the conventional value.

[0063] The server (108) can calculate the average value of the energy efficiency of the power generation amount by combining the expected power generation amount data, the contribution amount data by power source, the output stability indicator data, the range of energy that can be generated, and the efficiency value data relative to the power generation amount, respectively.

[0064] Additionally, the server (108) can determine whether the total energy efficiency value data and the profit value data exceed a preset first power generation threshold value.

[0065] In addition, the first power generation threshold value can be set to a reference value of 80.0 or less for the total energy efficiency value data and 100 or less for the profit value data, and can be set to an exceeded reference value if at least one of these values ​​is exceeded, and the error range of the total energy efficiency value data can be varied from -2 to 10 and the profit value data from 100 to 125 according to the manager's settings.

[0066] The server (108) can determine whether at least one power generation prediction data has been exceeded based on a first power generation threshold value set by the manager.

[0067] In operation 407, if the server (108) (e.g., the processor (120) of FIG. 2) determines that at least one power generation prediction data does not exceed a first power generation threshold value, it can determine that at least one power generation prediction data is the first power generation efficiency data that is calculated as the highest efficiency value.

[0068] According to one embodiment, the server (108) can determine that the total value is not exceeded from the first power generation threshold value (e.g., power generation energy efficiency average value data_80, profit value data_100, at least one value exceeding the first power generation threshold value), and can determine that at least one power generation prediction data is not exceeded from the first power generation efficiency data which is calculated as the highest efficiency value.

[0069] Meanwhile, in operation 405, if the server (108) (e.g., the processor (120) of FIG. 2) determines that at least one power generation prediction data exceeds a first power generation threshold, in operation 409, the server (108) (e.g., the processor (120) of FIG. 2) can determine whether at least one power generation prediction data exceeds a second power generation threshold set to be smaller than the first power generation threshold.

[0070] According to one embodiment, the server (108) can determine that when the value of the expected power generation data is 93.1 to 95.3, the value of the contribution data by power source is 87.25 to 89.33, the value of the output stability index data is 89.24 to 92.11, the value of the power generation possible energy range data is 84.25 to 86.94, and the value of the efficiency value data relative to power generation is 78.35 to 81.22, the average value of the average value of the power generation energy efficiency data is 86.44 to 88.98, and the profit value data is 94.29 to 96.97, the average value of the power generation energy efficiency data is not exceeded at the first power generation threshold value (e.g., power generation energy efficiency average value data_80, profit value data_100, at least one value exceeded), but the entire range of the profit value data is exceeded, and the server can determine that at least one power generation prediction data exceeds the first power generation threshold value.

[0071] According to another embodiment, the server (108) can determine that when the value of the expected power generation data is 83.1 to 87.9, the value of the contribution data by power source is 81.04 to 83.92, the value of the output stability index data is 80.44 to 81.98, the value of the power generation potential energy range data is 77.34 to 81.11, and the value of the efficiency value data relative to power generation is 74.97 to 76.35, the average value of the average value of the power generation energy efficiency data is 79.378 to 82.25, and the profit value data is 104.11 to 109.44, the profit value data is not exceeded at the first power generation threshold value (e.g., power generation energy efficiency average value data_80, profit value data_100, at least one value exceeded), but the maximum range of the power generation energy efficiency average value data is not exceeded, but the minimum range is exceeded, and the server can determine that at least one power generation prediction data has exceeded the first power generation threshold value.

[0072] Additionally, the server (108) can determine whether the total energy efficiency value data and the profit value data exceed a second power generation threshold set to be smaller than a first power generation threshold.

[0073] In addition, the second power generation threshold value can be set to a reference value of 65.0 or less for the total energy efficiency value data and 80 or less for the profit value data, and can be set to an exceeded reference value if at least one of these values ​​is exceeded, and the error range of the total energy efficiency value data can be varied from -5 to 12 and the profit value data from 80 to 92.5 according to the manager's settings.

[0074] The server (108) can determine whether at least one power generation prediction data has been exceeded based on a second power generation threshold value set by the manager.

[0075] In operation 411, if the server (108) (e.g., the processor (120) of FIG. 2) determines that at least one power generation prediction data has exceeded a first power generation threshold but has not exceeded a second power generation threshold, the at least one power generation prediction data can be determined as the second power generation efficiency data which is calculated as the next efficiency value of the first power generation efficiency data.

[0076] According to one embodiment, if the server (108) calculates that the value of the expected power generation data is 78.33 to 80.23, the value of the contribution data by power source is 76.25 to 78.46, the value of the output stability index data is 70.12 to 73.01, the value of the power generation potential energy range data is 67.11 to 69.46, and the value of the efficiency value data relative to power generation is 64.18 to 67.07, and if the average value of the power generation energy efficiency average value data is calculated to be 71.20 to 73.65 and the profit value data is calculated to be 90.13 to 94.34, the server may determine that the entire amount exceeds the first power generation threshold value (e.g., power generation energy efficiency average value data_80, profit value data_100, exceeding at least one value), and may determine that the entire amount does not exceed the second power generation threshold value (e.g., power generation energy efficiency average value data_65, profit value data_80, exceeding at least one value), and at least one It can be determined that the power generation prediction data exceeded the first power generation threshold but did not exceed the second power generation threshold.

[0077] Additionally, the server (108) can determine at least one power generation prediction data as a second power generation efficiency data that is calculated as the next efficiency value of the first power generation efficiency data.

[0078] Meanwhile, in operation 409, if the server (108) (e.g., the processor (120) of FIG. 2) determines that at least one power generation prediction data exceeds a first power generation threshold and a second power generation threshold, in operation 413, the server (108) (e.g., the processor (120) of FIG. 2) can determine whether at least one power generation prediction data exceeds a third power generation threshold set to be smaller than the second power generation threshold.

[0079] According to one embodiment, the server (108) can determine that the entire amount exceeds the first power generation threshold (e.g., power generation energy efficiency average value data_80, profit value data_100, exceeding at least one value) when the value of the expected power generation data is calculated to be 70.12 to 73.47, the value of the contribution data by power source is calculated to be 68.43 to 70.97, the value of the output stability index data is calculated to be 66.04 to 68.42, the value of the power generation potential energy range data is calculated to be 61.23 to 63.65, and the value of the efficiency value data relative to power generation is calculated to be 57.48 to 60.11, and when the average value of the power generation energy efficiency average value data is calculated to be 64.66 to 67.32 and the profit value data is calculated to be 87.34 to 91.24, and the entire amount exceeds the second power generation threshold (e.g., power generation energy efficiency average value data_65, profit value data_80, exceeding at least one value), but the profit value data does not exceed the power generation energy efficiency average value It can be determined that the data has exceeded the minimum range, and it can be determined that at least one power generation prediction data has exceeded the first power generation threshold and the second power generation threshold.

[0080] According to another embodiment, the server (108) can determine that the entire amount exceeds the first power generation threshold (e.g., power generation energy efficiency average value data_80, profit value data_100, exceeding at least one value) when the value of the expected power generation data is calculated to be 70.12–73.47, the value of the contribution data by power source is calculated to be 68.43–70.97, the value of the output stability index data is calculated to be 67.34–69.57, the value of the power generation potential energy range data is calculated to be 63.14–65.18, and the value of the efficiency value data relative to power generation is calculated to be 59.92–63.54, the average value of the power generation energy efficiency average value data is calculated to be 65.79–68.546, and the profit value data is calculated to be 77.91–82.13, and the total amount exceeds the second power generation threshold (e.g., power generation energy efficiency average value data_65, profit value data_80, exceeding at least one value). However, it can be determined that the profit value data has exceeded the minimum range, and at least one power generation prediction data can be determined that it has exceeded the first power generation threshold and the second power generation threshold.

[0081] Additionally, the server (108) can determine whether the total energy efficiency value data and the profit value data exceed a third power generation threshold set to be smaller than a second power generation threshold.

[0082] In addition, the third power generation threshold value can be set to a reference value of 40.0 or less for the total energy efficiency value data and 60 or less for the profit value data, and can be set to an exceeded reference value if all of the values ​​are exceeded. Depending on the manager's settings, the error range of the total energy efficiency value data can be changed from -10 to 15, and the profit value data can be changed from 50 to 70.25.

[0083] The server (108) can determine whether at least one power generation prediction data has been exceeded based on a third power generation threshold value set by the manager.

[0084] In operation 415, if the server (108) (e.g., the processor (120) of FIG. 2) determines that at least one power generation prediction data has exceeded a second power generation threshold but has not exceeded a third power generation threshold, the power generation prediction data can be determined as a third power generation efficiency data that is calculated as the next efficiency value of the second power generation efficiency data.

[0085] According to one embodiment, the server (108) can determine that the total is exceeded at a second power generation threshold (e.g., power generation energy efficiency average value data_65, profit value data_80, exceeding at least one value) when the value of the expected power generation data is 46.28 to 49.74, the value of the contribution data by power source is 44.34 to 46.48, the value of the output stability index data is 45.08 to 47.93, the value of the power generation possible energy range data is 40.21 to 40.95, and the value of the efficiency value data relative to power generation is 36.85 to 38.97, the average value of the power generation energy efficiency average value data is 42.55 to 45.32, and the profit value data is 61.34 to 63.92, and the total value is not exceeded at a third power generation threshold (e.g., power generation energy efficiency average value data_40, profit value data_60, exceeding the total value), and at least one power generation prediction data It can be determined that the second generation threshold value has been exceeded but the third generation threshold value has not been exceeded.

[0086] According to another embodiment, the server (108) can determine that the total is exceeded at the second power generation threshold (e.g., power generation energy efficiency average value data_65, profit value data_80, at least one value exceeded) and that the minimum value in the power generation energy efficiency average value data is exceeded at the third power generation threshold (e.g., power generation energy efficiency average value data_40, profit value data_60, at least one value exceeded). It can be determined that the profit value data is not exceeded, and it can be determined that at least one power generation prediction data exceeds the second power generation threshold but does not exceed the third power generation threshold.

[0087] According to another embodiment, the server (108) may determine that the total is exceeded at the second power generation threshold (e.g., power generation energy efficiency average value data_65, profit value data_80, exceeding at least one value) when the value of the expected power generation data is 46.28 to 49.74, the value of the contribution data by power source is 44.34 to 46.48, the value of the output stability index data is 45.08 to 47.93, the value of the power generation potential energy range data is 40.21 to 40.95, and the value of the efficiency value data relative to power generation is 36.85 to 38.97, and when the average value of the power generation energy efficiency average value data is 42.55 to 45.32 and the profit value data is 53.18 to 56.34, and the total is exceeded, but the total power generation It can be determined that the entire average value of energy efficiency is not exceeded, and that at least one power generation prediction data exceeds the second power generation threshold but does not exceed the third power generation threshold.

[0088] The server (108) can determine the power generation prediction data as the third power generation efficiency data, which is calculated as the next efficiency value of the second power generation efficiency data.

[0089] Meanwhile, in operation 413, if the server (108) (e.g., the processor (120) of FIG. 2) determines that at least one power generation prediction data exceeds the second power generation threshold and the third power generation threshold, in operation 417, the server (108) (e.g., the processor (120) of FIG. 2) may determine the power generation prediction data as the fourth power generation efficiency data that yields the lowest efficiency value.

[0090] According to one embodiment, if the server (108) calculates that the value of the expected power generation data is 43.91 to 46.45, the value of the contribution data by power source is 41.58 to 43.98, the value of the output stability index data is 40.01 to 42.47, the value of the power generation potential energy range data is 38.85 to 40.12, and the value of the efficiency value data relative to power generation is 33.48 to 35.62, and the average value of the average power generation energy efficiency value data is calculated to be 39.56 to 47.73, and the profit value data is calculated to be 43.85 to 47.49, then the server may determine that the entire value exceeds the second power generation threshold (e.g., average power generation energy efficiency value data_65, profit value data_80, exceeding at least one value), and determine that the minimum value of the average power generation energy efficiency value data exceeds the third power generation threshold (e.g., average power generation energy efficiency value data_40, profit value data_60, exceeding the entire value), and the profit It can be determined that the value data has been exceeded, and it can be determined that at least one power generation prediction data has exceeded the second power generation threshold and the third power generation threshold.

[0091] The server (108) can determine the fourth power generation efficiency data, which is the lowest efficiency value of the power generation prediction data.

[0092] Meanwhile, the server (108) can compare whether the power generation prediction data exceeds a third power generation threshold value, or exceeds a fourth power generation threshold value set lower than the third power generation threshold value. Here, the fourth power generation threshold value can be set based on an average power generation energy efficiency value of 20 or less, a profit value of 10 or less, and can be determined as exceeding the total value.

[0093] Additionally, if the power generation prediction data exceeds the fourth power generation threshold, the server (108) may determine that the power generation prediction data is inefficient and potentially inefficient data.

[0094] In operation 419, the server (108) (e.g., the processor (120) of FIG. 2) can transmit at least one power generation data and result data determined as at least one of the first power generation efficiency data, the second power generation efficiency data, the third power generation efficiency data, and the fourth power generation efficiency data to at least one external electronic device (101, 102, 104) of an administrator and / or buyer through a communication interface (e.g., the communication interface (160) of FIG. 2).

[0095] According to one embodiment, the server (108) may combine at least one power generation data and one power generation efficiency data, a second power generation efficiency data, a third power generation efficiency data, a fourth power generation efficiency data, and / or data without power generation efficiency, determined according to a first power generation threshold value and / or a fourth power generation threshold value, into one data or display a distinction according to each data.

[0096] The server (108) can transmit data to at least one external electronic device (101, 102, 104) of an administrator and / or buyer through a communication interface (e.g., communication interface (160) of FIG. 2), which is determined by at least one combined or displayed power generation data and one power generation efficiency data, a second power generation efficiency data, a third power generation efficiency data, a fourth power generation efficiency data, and / or data without power generation efficiency, determined according to a first power generation threshold and / or a fourth power generation threshold.

[0097] The buyer can check at least one power generation data received on their external electronic device (101, 102, 104) in real time by outputting it to a platform structure as shown in FIGS. 5 to 10, which will be described later, and can also check at least one power generation data entered into a pre-configured form and configured as a report, and can perform bidding.

[0098] In addition, the manager can check at least one power generation data received on their external electronic device (101, 102, 104) in real time by outputting it to a platform structure as shown in FIGS. 5 to 10, and can also check at least one power generation data entered into a pre-configured form and configured as a report, and can proceed with the bidding according to the confirmation and execution of the buyer's bidding.

[0100] FIG. 5 is an exemplary diagram of an overall operating system for a server according to one embodiment of the present invention.

[0101] According to one embodiment, the server (108) can provide an integrated energy management environment including an energy management system (EMS), an integrated control system, a demand response demo, and a virtual power plant (VPP) demo for performing energy management functions in a campus-level renewable energy operation environment, as illustrated in FIG. 5. The server (108) manages the status of energy resources based on energy data collected from solar power generation, wind power generation, and energy storage devices (ESS), and each system can be operated independently or in conjunction.

[0102] In another embodiment, the server (108) can perform user authentication, real-time operation status monitoring, generation and storage history lookup, fault history management, facility control management, and data reception status management through energy management system functions. Additionally, the server (108) can provide system management functions such as facility management, site management, and user management, and can improve the efficiency of energy operation through maximum demand analyzer, power usage analysis, reference load (CBL) calculation, energy storage device operation control, and power conversion device (PCS) interlocking control functions.

[0103] In another embodiment, the server (108) can provide key energy data processed by the energy management system in the form of a dashboard through an integrated control system. Additionally, the server (108) can support an administrator in intuitively understanding the overall energy operation status by displaying key status data, power generation, and operating status of energy storage devices, solar and wind power generation facilities in real time. Such an integrated control function can be configured to manage multiple energy resources on a single screen.

[0104] In another embodiment, the server (108) can provide demand response and virtual power plant demonstration functions that utilize temporary resources and simulation data without linking actual energy resources. The server (108) can verify the demand response and virtual power plant operation structure by performing maximum load forecasting, real-time power supply and demand status, reduction history management, and bidding scenario processing. Additionally, the server (108) can provide an energy management structure that is scalable even when linking actual resources in the future by collecting Modbus communication-based data from power converters, solar and wind power facilities through a data agent.

[0106] FIG. 6 is an example diagram for the operational status of a server according to one embodiment of the present invention.

[0107] FIG. 7 is an example of an output for at least one energy data received from a server according to an embodiment of the present invention.

[0108] According to one embodiment, the server (108) can manage the status of solar power generation operation based on solar power generation data collected from the solar power generation facility, as illustrated in FIGS. 6 and 7. The solar power generation data may include power generated by time of day, cumulative power generation, power generation efficiency relative to solar irradiance, and output fluctuation data, and the server (108) can analyze this in real-time or periodically to determine whether the solar power facility is operating normally and to determine changes in power generation performance. In addition, the server (108) can detect a decrease in power generation efficiency or an abnormal state by comparing it with past power generation history.

[0109] According to another embodiment, the server (108) can manage the operational status of the wind power generation based on wind power generation data obtained from the wind power generation facility, as illustrated in FIG. 6. The wind power generation data may include wind speed, turbine rotation speed, power output, and output stability data, and the server (108) can determine the operating status of the wind power facility by analyzing output variability according to wind conditions. In addition, the server (108) can calculate an overall power generation status that reflects mutually complementary renewable energy generation characteristics by considering solar power generation data and wind power generation data together.

[0110] According to another embodiment, the server (108) can manage the status of energy storage and supply operations based on power load data, power converter status data, and battery status data, as illustrated in FIG. 6. The load data may include real-time power usage, maximum demand, and load pattern data, and the server (108) can perform load forecasting and demand response judgments based on this. In addition, the server (108) can monitor the output status, conversion efficiency, and control status of the power converter, and comprehensively analyze the charge / discharge status, remaining capacity, and degradation status of the battery to support the stable operation of the energy storage device.

[0111] According to another embodiment, the server (108) can manage the status of data reception based on the reception status of data received from solar, wind, power conversion devices, and battery facilities, as illustrated in FIG. 7. The server (108) can determine the reliability of data collection by monitoring the data reception cycle, whether missing data occurs, and the status of communication errors. In addition, the server (108) can accurately reflect the operational status of the entire energy management system by verifying and matching the Modbus communication-based data collected through the data agent.

[0113] FIG. 8 is a comprehensive example of power generation prediction data determined by a server from a prediction model according to one embodiment of the present invention.

[0114] According to one embodiment, the server (108) can manage power generation prediction data determined by inputting power generation comparison data and energy management comparison data into a prediction model, as shown in FIG. 8. The power generation prediction data may include expected power generation for a specific future point in time or period, contribution by power source, and output stability indicators, and the server (108) can utilize this as core prediction data for the platform. The prediction model may be configured to predict short-term or medium-to-long-term power generation by reflecting the past power generation and operation history of solar power, wind power, and energy storage devices (energy storage devices), and the server (108) can output at least one power generation data predicted in real time to the platform structure.

[0115] According to another embodiment, the server (108) can process power generation prediction data in conjunction with weather data, facility data, and power generation data. Weather data may include solar radiation, wind speed, temperature, and weather change prediction data, and facility data may include data on the operating status, efficiency, and availability of solar panels, wind turbines, and energy storage devices. By utilizing the weather data and facility data as auxiliary input values ​​or verification data for the prediction model, the server (108) can improve the accuracy of power generation prediction that reflects the actual operating environment.

[0116] According to another embodiment, the server (108) can collect daily energy storage device operation data in real time and output current charge / discharge status, remaining capacity, and available energy data on the platform. Additionally, the server (108) can receive solar power generation and wind power generation amounts from each power plant in real time and display the current output, cumulative power generation, and generation trends for each power source. Through this, the buyer can intuitively compare the difference between the predicted power generation amount and the actual power generation amount.

[0117] According to another embodiment, the server (108) can integrate power generation forecast data, weather data, facility and power generation data, and real-time energy storage device and power plant operation data to provide a platform structure in the form of a dashboard. By outputting the forecast data and real-time data in conjunction on the same screen, the server (108) can support a comprehensive understanding of the power generation operation status and rapid decision-making. Such a platform structure can be configured to manage multiple power plants and energy resources in an scalable manner.

[0118] Additionally, the server (108) can transmit the output power generation prediction data from the top to the buyer's external electronic device (101, 102, 104) in the form of a platform through a communication interface (e.g., the communication interface (160) of FIG. 2), and the buyer can compare it in real time in the form of a platform on their external electronic device (101, 102, 104).

[0120] FIG. 9 is an example of outputting result values ​​for power generation prediction data determined by a server from a prediction model according to an embodiment of the present invention.

[0121] According to one embodiment, the server (108) can calculate the maximum load amount based on power generation prediction data determined by a prediction model, as illustrated in FIG. 9, and output it on a platform. Additionally, the server (108) can calculate the maximum load amount by combining past load history, current power usage patterns, and predicted power generation, and can provide the expected maximum power demand based on a specific time period or day in the form of a numerical value or a graph. Furthermore, the buyer can recognize future load concentration periods in advance.

[0122] According to another embodiment, the server (108) can output a real-time power supply and demand status by linking power generation forecast data with real-time power generation data. Additionally, the server (108) can output a real-time power supply and demand status including total power generation, total power demand, and surplus or deficit power data, and can visually display the supply and demand balance status by classifying it into normal, caution, or danger stages. Furthermore, the administrator can intuitively determine the current power supply stability.

[0123] According to another embodiment, the server (108) can output real-time power usage status. Additionally, the server (108) can output real-time power usage status including power usage by building, facility, or zone unit, consumption trends by time of day, and the ratio of current usage to maximum demand. By providing the data together with power generation prediction data, the server (108) can support immediate verification of whether actual usage exceeds the predicted range.

[0125] FIG. 10 is a real-time comparison example of power generation prediction data determined by a server from a prediction model according to one embodiment of the present invention.

[0126] According to one embodiment, the server (108) can output real-time power status in the form of a nationwide map, as illustrated in FIG. 10. Additionally, the server (108) can visually distinguish and display regional power generation, power demand, and power supply and demand status on a map, and can represent the power status of each region using colors or icons. Furthermore, the buyer can grasp the nationwide power supply and demand distribution on a single screen.

[0127] According to another embodiment, the server (108) can calculate and output the average available capacity in conjunction with a national map. Additionally, the server (108) can calculate the average available capacity by synthesizing the available output of power generation facilities by region, the remaining capacity of energy storage devices, and real-time load, and can provide it in the form of a table or an indicator. Furthermore, the server (108) can analyze the deviation of available capacity by region to distinguish between power surplus areas and areas with potential power shortages.

[0128] According to another embodiment, the server (108) can calculate and output profit point data based on average available capacity and power status. Additionally, the server (108) can output profit point data including the potential for utilizing surplus power, cost reduction effects resulting from avoiding peak load, or power trading opportunity data. By providing profit point data in the form of numerical values ​​or indicators, the server (108) can support the manager in establishing power operation strategies and energy management policies more efficiently, and enable the buyer to establish the status of increased purchasing desire and purchasing based on power operation in real time.

[0130] The server (108) according to the present embodiment has the advantage of being able to manage at least one energy data obtained from a solar power device, a wind power device, and an energy management system in real time in a platform form, and to determine at least one power generation prediction data by inputting it into a prediction model, and to output at least one power generation data in a platform structure to perform status and bidding effects regarding the power generation energy in real time.

[0132] According to various embodiments, a server for controlling and trading energy resources based on a prediction model comprises a communication interface and a processor, wherein the processor receives at least one energy data from at least one external electronic device for energy management through the communication interface, inputs the at least one energy data into a prediction model to determine at least one power generation prediction data, and is configured to transmit the at least one power generation prediction data to an external electronic device of at least one manager and / or buyer through the communication interface, and the prediction model is learned based on a plurality of energy data, a plurality of power generation prediction data, a first power generation efficiency data in which the plurality of power generation prediction data is calculated as the highest efficiency value, a second power generation efficiency data in which the plurality of power generation prediction data is calculated as the next efficiency value of the first power generation efficiency data, a third power generation efficiency data in which the plurality of power generation prediction data is calculated as the next efficiency value of the second power generation efficiency data, and a fourth power generation efficiency data in which the plurality of power generation prediction data is calculated as the lowest efficiency value.

[0133] According to various embodiments, the at least one energy data comprises power plant energy data composed of at least one of solar energy data obtained from at least one solar power device, wind energy data obtained from at least one wind power device, and tidal energy data obtained from at least one tidal power device, and energy management data obtained from an Energy Management System (EMS) device for the at least one power plant energy data.

[0134] According to various embodiments, the processor further comprises a memory, wherein the processor calculates power generation comparison data by comparing the power plant energy data with previous power plant output data stored in the memory, calculates energy management comparison data by comparing the energy management data with previous energy management data stored in the memory, and is configured to determine at least one power generation prediction data by inputting the power generation comparison data and the management comparison data into the prediction model, and the prediction model further learns a plurality of solar energy data, a plurality of wind energy data, a plurality of tidal energy data, a plurality of power plant energy data, a plurality of energy management data, a plurality of power generation comparison data, and a plurality of energy management comparison data.

[0135] According to various embodiments, the processor determines that if the at least one power generation prediction data does not exceed a preset first power generation threshold value, the at least one power generation prediction data is determined to be the first power generation efficiency data calculated as the highest efficiency value; if the at least one power generation prediction data is determined to exceed the first power generation threshold value, the at least one power generation prediction data is determined to be the second power generation efficiency data calculated as the next efficiency value of the first power generation efficiency data; and is configured to transmit the at least one power generation data and the result data determined as at least one of the first power generation efficiency data or the second power generation efficiency data to an external electronic device of the at least one manager and / or buyer through the communication interface.

[0136] According to other various embodiments, in a method for operating a server for controlling and trading energy resources based on a prediction model, the method is configured to receive at least one energy data from at least one external electronic device for energy management through a communication interface, input the at least one energy data into a prediction model through a processor to determine at least one power generation prediction data, and transmit the at least one power generation prediction data to the at least one external electronic device of a manager and / or buyer through the communication interface, and the prediction model is learned based on a plurality of energy data, a plurality of power generation prediction data, a first power generation efficiency data in which the plurality of power generation prediction data is calculated as the highest efficiency value, a second power generation efficiency data in which the plurality of power generation prediction data is calculated as the next efficiency value of the first power generation efficiency data, a third power generation efficiency data in which the plurality of power generation prediction data is calculated as the next efficiency value of the second power generation efficiency data, and a fourth power generation efficiency data in which the plurality of power generation prediction data is calculated as the lowest efficiency value.

[0138] As used in this document, the expression "configured to" may be replaced, depending on the context, with, for example, "suitable for," "having the capacity to," "designed to," "adapted to," "made to," or "capable of." The term "configured to" does not necessarily mean "specifically designed to."

[0139] In this document, terms transmitted or received between the first electronic device(s) and the second electronic device(s), such as “command,” “instruction,” “control information,” “message,” “information,” “data,” “packet,” “data packet,” “intent,” and / or “signal,” may include or refer to humanly perceptible ideas or specific electrical representations (e.g., digital codes / analog physical quantities) without being limited by their expression. It will be obvious to a person skilled in the art to which the invention disclosed in this document pertains that the exemplary expressions listed above may be interpreted in various ways depending on the context in which they are used. In this document, “a is greater than B” means not only that “a is greater than B” but also includes the meaning that “a is equal to or greater than B”.

[0140] The terms used in this document are used merely to describe specific embodiments and are not intended to limit the scope of other embodiments. Singular expressions may include plural expressions unless the context clearly indicates otherwise. Terms used herein, including technical or scientific terms, may have the same meaning as generally understood by those skilled in the art described in this document. Terms used in this document that are defined in general dictionaries may be interpreted as having the same or similar meaning as they have in the context of the relevant technology, and are not to be interpreted in an ideal or overly formal sense unless explicitly defined in this document. In some cases, even terms defined in this document may not be interpreted to exclude the embodiments of this document.

[0141] Although all components constituting an embodiment of the present invention have been described above as being combined or operating in combination, the present invention is not necessarily limited to such embodiments. That is, within the scope of the purpose of the present invention, all components may be selectively combined in one or more ways to operate.

[0142] Meanwhile, the various embodiments described herein may be implemented by hardware, middleware, microcode, software and / or combinations thereof. For example, the various embodiments may be implemented in one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), processors, controllers, microcontrollers, microprocessors, other electronic units designed to perform the functions presented herein, or combinations thereof.

[0143] Additionally, for example, various embodiments may be stored or encoded on a computer-readable medium containing instructions. Instructions stored or encoded on a computer-readable medium may enable a programmable processor or other processor to perform a method, for example, when the instructions are executed. A computer-readable medium includes a computer storage medium, and the computer storage medium may be any available medium accessible by a computer. For example, such a computer-readable medium may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage media, magnetic disk storage media or other magnetic storage devices.

[0144] Such hardware, software, firmware, etc., may be implemented within the same device or in individual devices to support the various operations and functions described in this specification. Additionally, components, units, modules, components, etc., described as "parts" in this invention may be implemented together or individually as separate but interoperable logic devices. Descriptions of different features of modules, units, etc., are intended to highlight different functional embodiments and do not necessarily imply that they must be realized by individual hardware or software components. Rather, functions associated with one or more modules or units may be performed by individual hardware or software components or integrated within common or individual hardware or software components.

[0145] Although operations are depicted in a specific order in the drawings, it should not be understood that these operations must be performed in the specific order depicted or in a sequential order to achieve the desired result, or that all depicted operations must be performed. In any environment, multitasking and parallel processing may be advantageous. Furthermore, the distinction of various components in the above-described embodiments should not be understood as requiring such distinction in all embodiments, and it should be understood that the described components may generally be integrated together into a single software product or packaged into multiple software products.

[0146] The electronic device, server, or external device according to the various embodiments of the present document described above may include, for example, at least one of a smartphone, tablet PC, mobile phone, video phone, desktop PC, laptop PC, PDA (personal digital assistant), PMP (portable multimedia player), MP3 player, mobile medical device, camera, or wearable device.

[0147] According to various embodiments, the wearable device may include at least one of an accessory type (e.g., a watch, ring, bracelet, anklet, necklace, glasses, contact lens, or head-mounted device (HMD)), a fabric or clothing integrated type (e.g., electronic clothing), a body-attached type (e.g., a skin pad or tattoo), or a bio-implantable type (e.g., an implantable circuit).

[0148] In some embodiments, the electronic device or external device may be a home appliance. The home appliance may include, for example, at least one of a television, a DVD player (Digital Video Disk player), audio, a refrigerator, an air conditioner, a vacuum cleaner, an oven, a microwave oven, a washing machine, an air purifier, a set-top box, a home automation control panel, a security control panel, a TV box, a game console, an electronic dictionary, an electronic key, a camcorder, or a digital photo frame.

[0149] In another embodiment, the electronic device, external device, and wearable device may include at least one of various medical devices (e.g., various portable medical measuring devices (blood glucose meter, heart rate monitor, blood pressure monitor, or body temperature monitor, etc.), MRA (magnetic resonance angiography), MRI (magnetic resonance imaging), CT (computed tomography), imaging device, or ultrasound device, etc.), navigation device, satellite navigation system (GNSS (Global Navigation Satellite System)), EDR (event data recorder), FDR (flight data recorder), automotive infotainment device, home robot, or Internet of Things device (e.g., light bulb, various sensor, electric or gas meter, sprinkler device, fire alarm, thermostat, street light, exercise equipment, hot water tank, heater, boiler, etc.).

[0151] As described above, the best embodiments have been disclosed in the drawings and specification. Specific terms have been used herein, but they are used only for the purpose of describing the invention and are not intended to limit the meaning or the scope of the invention as described in the claims. Therefore, those skilled in the art will understand that various modifications and equivalent alternative embodiments are possible therefrom. Accordingly, the true technical scope of protection of the invention should be determined by the technical spirit of the appended claims.

Claims

Claim 1 Communication interface; The system includes a processor, wherein the processor receives at least one energy data from at least one external electronic device for energy management through the communication interface, inputs the at least one energy data into a prediction model to determine at least one power generation prediction data, and if it is determined that the at least one power generation prediction data does not exceed a preset first power generation threshold, the at least one power generation prediction data is determined as the first power generation efficiency data calculated as the highest efficiency value, and if it is determined that the at least one power generation prediction data exceeds the first power generation threshold, the at least one power generation prediction data is determined as the second power generation efficiency data calculated as the next efficiency value of the first power generation efficiency data, and is configured to transmit the at least one power generation prediction data and the result data determined as at least one of the first power generation efficiency data or the second power generation efficiency data to the external electronic device of the at least one manager and / or buyer through the communication interface, and the prediction model comprises a plurality of energy data, a plurality of power generation prediction data, a first power generation efficiency data calculated as the highest efficiency value of the plurality of power generation prediction data, and a plurality of power generation prediction data as the next efficiency of the first power generation efficiency data. A second power generation efficiency data calculated as a value, a plurality of power generation prediction data are learned based on a third power generation efficiency data calculated as the next efficiency value of the second power generation efficiency data, and a fourth power generation efficiency data calculated as the lowest efficiency value of the plurality of power generation prediction data, and the processor extracts expected power generation data, contribution data by power source, output stability indicator data, potential energy range data, efficiency value data relative to power generation, and profit value data included in the at least one power generation prediction data, and the power generation data, the contribution data by power source,The above output stability indicator data, the above power generation potential energy range data, and the above efficiency value relative to power generation are quantified and calculated on a scale from 0 to 100, where values ​​closer to 0 represent lower efficiency or deterioration, and values ​​closer to 100 represent higher efficiency or increase; the above power generation data, the above contribution data by power source, the above output stability indicator data, the above power generation potential energy range data, and the above efficiency value relative to power generation are respectively combined to calculate the average value of the power generation energy efficiency average value data; the above profit value data is calculated from -100 to 200, where a value of 0 or less indicates a loss compared to the conventional method, a value between 0 and 100 indicates a profit generated but calculated to be greater than each efficiency value compared to the conventional method, and a value of 100 or more indicates a very significant increase in profit compared to the conventional method; and the above power generation energy efficiency average value and the above profit value data are respectively compared with the above first power generation threshold value and the above second power generation threshold value, wherein the above first power generation threshold value is set to the largest value, and the above second power generation threshold value is the above first power generation threshold A server for the control and trading of energy resources based on a prediction model set lower than the value. Claim 2 A server for controlling and trading energy resources based on a prediction model according to claim 1, wherein the at least one energy data comprises power plant energy data composed of at least one of solar energy data obtained from at least one solar power device, wind energy data obtained from at least one wind power device, and tidal energy data obtained from at least one tidal power device, and energy management data obtained from an Energy Management System (EMS) device for the at least one power plant energy data. Claim 3 A server for controlling and trading energy resources based on a prediction model, wherein the processor calculates power generation comparison data by comparing the power plant energy data with previous power plant output data stored in the memory, calculates energy management comparison data by comparing the energy management data with previous energy management data stored in the memory, and inputs the power generation comparison data and the management comparison data into the prediction model to determine at least one power generation prediction data, and the prediction model is further trained with a plurality of solar energy data, a plurality of wind energy data, a plurality of tidal energy data, a plurality of power plant energy data, a plurality of energy management data, a plurality of power generation comparison data, and a plurality of energy management comparison data. Claim 4 delete Claim 5 A method for operating a server for controlling and trading energy resources based on a prediction model, wherein the method comprises receiving at least one energy data from at least one external electronic device for energy management through a communication interface, inputting the at least one energy data into a prediction model through a processor to determine at least one power generation prediction data, and if the processor determines that the at least one power generation prediction data does not exceed a preset first power generation threshold, determining the at least one power generation prediction data as the first power generation efficiency data calculated as the highest efficiency value, and if the processor determines that the at least one power generation prediction data exceeds the first power generation threshold, determining the at least one power generation prediction data as the second power generation efficiency data calculated as the next efficiency value of the first power generation efficiency data, and, through the communication interface, transmitting the at least one power generation prediction data and result data determined as at least one of the first power generation efficiency data or the second power generation efficiency data to an external electronic device of at least one manager and / or buyer, wherein the prediction model comprises a plurality of energy data, a plurality of power generation prediction data, and a plurality of power generation prediction data as the highest efficiency value The method is learned based on a first power generation efficiency data, a plurality of power generation prediction data, a second power generation efficiency data calculated as the next efficiency value of the first power generation efficiency data, a plurality of power generation prediction data calculated as the next efficiency value of the second power generation efficiency data, and a fourth power generation efficiency data calculated as the lowest efficiency value of the plurality of power generation prediction data, and the method is learned through the processor, the expected power generation data, power source contribution data, output stability indicator data included in the at least one power generation prediction data,Extracting data on the range of potential energy generation, data on efficiency values ​​relative to generation, and data on profit values; and through the processor, quantifying the generation data, the contribution data by power source, the output stability index data, the range of potential energy generation, and the efficiency values ​​relative to generation, calculating them on a scale from 0 to 100, where values ​​closer to 0 represent lower efficiency or deterioration, and values ​​closer to 100 represent higher efficiency or increase; and through the processor, calculating average generation energy efficiency data by combining the generation data, the contribution data by power source, the output stability index data, the range of potential energy generation, and the efficiency values ​​relative to generation, respectively, to calculate an average value; and through the processor, calculating the profit data on a scale from -100 to 200, where a value of 0 or less indicates a loss compared to the conventional method, a value between 0 and 100 indicates a profit generated but greater than each efficiency value compared to the conventional method, and a value of 100 or more indicates a very significant increase in profit compared to the conventional method; and through the processor, the average generation energy efficiency A method of comparing the value and the profit value data with the first power generation threshold and the second power generation threshold, respectively, wherein the first power generation threshold is set to the largest value and the second power generation threshold is set lower than the first power generation threshold.

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