KR102670391B1 - Apparatus and method for supporting trading of carbon emissions and electricity, and program stored in computer readable medium performing the same - Google Patents

Abstract

A carbon emissions trading device and method and a program stored in a computer-readable recording medium to perform the same are provided. The carbon emissions trading device according to an embodiment of the present invention receives first energy use data for a preset aggregation period of the first user, calculates second energy use data for the previous year corresponding to the aggregation period, and calculates the first energy use data for the previous year corresponding to the aggregation period. Energy use data and secondary energy use data are used to calculate energy increase/decrease, energy increase/decrease is used to set buyers and sellers of carbon emissions rights, and recommended seller information is generated for buyers.

Description

Apparatus and method for assisting in carbon emissions and power trading, and a program stored in a computer-readable recording medium to perform the same

The present invention relates to a technology that can collect energy use information through building maintenance data and match carbon emissions credits and direct electricity transactions.

Carbon emissions rights are the right to emit greenhouse gases that cause and aggravate global warming, and companies allocated carbon emissions rights are obligated to use greenhouse gases within the scope of allocation. And any remaining or insufficient emissions permits can be traded on the market.

In other words, carbon credits are for carbon dioxide (CO ₂ ), methane (CH ₄ ), nitrous oxide (N ₂ O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and sulfur hexafluoride (SF ₆ ), which are the main causes of global warming. It is the right to emit greenhouse gases. Since carbon dioxide accounts for the largest proportion of greenhouse gases, the purpose is to regulate carbon dioxide emissions. Countries that have joined the Kyoto Protocol have decided to reduce carbon dioxide emissions by an average of 5% compared to 1990 by 2012, and countries or companies that fail to do so are required to purchase carbon credits from outside. Therefore, companies that emit a lot of carbon dioxide must either reduce emissions themselves through technological development such as energy saving, or purchase the rights from companies that own surplus emissions rights due to low emissions.

Carbon emissions credits are issued by the UN Framework Convention on Climate Change (UNFCCC), and issued carbon credits can be freely traded like commodities in the market. Carbon emissions rights are divided into two types: ‘allowance credits’ and ‘credit credits.’ Here, the quota is the right to emit greenhouse gases for each entity subject to mandatory reduction. When the government allocates emission rights to a company, the company can trade excess or remaining emissions in the market in the form of emissions rights.　 And 'credit' refers to a method of issuing credits corresponding to the reduction amount when greenhouse gases are reduced from the originally expected emissions through greenhouse gas reduction activities.

Specifically, these types of carbon emissions rights include AAUs (national quotas for countries with reduction obligations under the Kyoto Protocol), EUAs (quotas set by the EU ETS (European Union Emissions Trading System)), and CERs (greenhouse gas reduction amounts through the Clean Development Mechanism (CDM)). ), 　ERUs (greenhouse gas reduction amount through JI (Joint Implementation System)), 　RMUs (greenhouse gas absorption amount through afforestation projects, etc. of countries with reduction obligations under the Kyoto Protocol), etc. 　Among the countries implementing the carbon emissions trading system, the one where trading is most active is the European Union (EU), which first established the carbon exchange in 2005 and implemented the system.

Meanwhile, the carbon emissions credit system is leading to the development of various new technologies to reduce carbon, a representative example being carbon capture and storage technology (CCS). This refers to a technology that extracts carbon dioxide (CO ₂ ) generated from fossil fuels before it is discharged into the atmosphere, then applies pressure to change it into a liquid state and store it.

As global cooperation continues to prevent global warming, which is gradually accelerating, unlike other forms of asset trading where transactions are actively taking place, the criteria for asset recognition are still vague for power trading and carbon emissions trading, and transaction methods are not clearly established. Therefore, we need a system that allows for easy trading of electricity and carbon emissions rights that can be traded at the private level.

One technical problem to be solved by the present invention is a carbon emissions trading device and method for receiving energy usage data from a user, setting buyers and sellers, and generating recommended seller information for buyers, and computer-readable records for performing the same. It provides programs stored on media.

Another technical problem to be solved by the present invention is to provide a carbon emissions trading device and method for calculating energy increase or decrease using carbon emissions, and a program stored in a computer-readable recording medium to perform the same.

Another technical problem to be solved by the present invention is a carbon emissions trading device and method for generating recommended seller information in consideration of environmental factors, social factors, and management and operational factors, and a program stored in a computer-readable recording medium to perform the same. is to provide.

The technical problems to be solved by the present invention are not limited to those described above.

A carbon emissions trading device according to an embodiment of the present invention receives first energy use data for a preset aggregation period of a first user and extracts second energy use data from the previous year corresponding to the aggregation period, Calculating an energy increase or decrease using the first energy use data and the second energy use data, using the energy increase or decrease to set buyers and sellers of carbon credits, and generating recommended seller information for the buyer. server; And it may include a database that stores the first and second energy usage data.

In one embodiment, the server converts the unit usage of each energy source into unit heat, calculates tons of oil equivalent for each energy source using the energy usage and the unit heat, calculates carbon dioxide emissions per unit usage of each energy source, and Carbon emissions can be calculated using energy usage and carbon dioxide emissions per unit usage, and the energy increase or decrease can be calculated using carbon emissions.

As an example, the server may use the energy increase/decrease amount to set a user whose energy use has increased as the buyer, and a user whose energy use has decreased can be set as the seller.

In one embodiment, the server receives third energy usage data of the first user from an external server, compares the first energy usage data and the third energy usage data, and compares the first energy usage data with the third energy usage data. If it is within the threshold of the third energy use data, the energy increase or decrease is calculated using the first energy use data, and if the first energy use data is greater than the threshold of the third energy use data, the third energy use data is used. The energy increase or decrease amount can be calculated using energy usage data.

As an example, the server may generate the recommended seller information in order of the lowest selling price of energy.

In one embodiment, the server may generate the recommended seller information by considering environmental factors, social factors, and management and operational factors.

In one embodiment, the server considers the environmental factors by converting the correlation between average temperature and energy usage into a first weight, and converts the increase/decrease trend of energy usage by country/industry/region into a second weight to calculate the Social factors are taken into consideration, and the above management and operational factors can be considered using a third weight according to government policy support/regulation/industrial site conditions.

In one embodiment, the server may generate the recommended seller information according to a user request.

One or more processors according to an embodiment of the present invention; and one or more memories storing instructions that cause the one or more processors to perform operations when executed by the one or more processors. The method of trading carbon emissions using a carbon emissions trading device includes, by the one or more processors, a first Receiving first energy usage data for a user's preset aggregation period; extracting, by the one or more processors, second energy use data for the previous year corresponding to the aggregation period; calculating, by the one or more processors, an energy increase or decrease amount using the first energy use data and the second energy use data; Setting buyers and sellers of carbon emissions credits by using the energy increase or decrease amount, by the one or more processors; and generating recommended seller information for the buyer by the one or more processors.

One or more processors according to an embodiment of the present invention; And a computer program stored on a computer-readable recording medium so that it can be executed by a computer including one or more memories storing instructions that cause the one or more processors to perform an operation when executed by the one or more processors, receiving first energy usage data for a preset aggregation period of the first user; extracting, by the one or more processors, second energy use data for the previous year corresponding to the aggregation period; calculating, by the one or more processors, an energy increase or decrease amount using the first energy use data and the second energy use data; Setting buyers and sellers of carbon emissions credits by using the energy increase or decrease amount, by the one or more processors; and generating recommended seller information for the buyer by the one or more processors.

The carbon emissions trading device and method according to an embodiment of the present invention, and the program stored in a computer-readable recording medium to perform the same, can provide a system that helps facilitate transactions between users by utilizing actual energy use data. .

In addition, the carbon emissions trading device and method according to an embodiment of the present invention, and the program stored in a computer-readable recording medium to perform the same, automatically calculate and aggregate data measured and collected by safety inspection technicians, experts, and suppliers. It can be linked.

Figure 1 is an exemplary diagram showing the configuration of a carbon emissions trading environment according to an embodiment of the present invention.
Figure 2 is a conceptual diagram of a carbon emissions trading environment according to an embodiment of the present invention.
Figure 3 is an example of carbon emissions trading features and screens according to an embodiment of the present invention.
Figure 4 is a carbon emissions trading process and conceptual diagram according to an embodiment of the present invention.
Figure 5 is an exemplary diagram showing the configuration of a carbon emissions trading device according to an embodiment of the present invention.
Figure 6 is a diagram for explaining learning of a neural network according to an embodiment of the present invention.
Figure 7 is a flowchart showing the procedures of the carbon emissions trading method according to an embodiment of the present invention.

Hereinafter, embodiments will be described in detail with reference to the attached drawings. However, various changes can be made to the embodiments, so the scope of the patent application is not limited or limited by these embodiments. It should be understood that all changes, equivalents, or substitutes for the embodiments are included in the scope of rights.

Specific structural or functional descriptions of the embodiments are disclosed for illustrative purposes only and may be modified and implemented in various forms. Accordingly, the embodiments are not limited to the specific disclosed form, and the scope of the present specification includes changes, equivalents, or substitutes included in the technical spirit.

Terms such as first or second may be used to describe various components, but these terms should be interpreted only for the purpose of distinguishing one component from another component. For example, a first component may be named a second component, and similarly, the second component may also be named a first component.

When a component is referred to as being “connected” to another component, it should be understood that it may be directly connected or connected to the other component, but that other components may exist in between.

The terms used in the examples are for descriptive purposes only and should not be construed as limiting. Singular expressions include plural expressions unless the context clearly dictates otherwise. In this specification, terms such as “comprise” or “have” are intended to designate the presence of features, numbers, steps, operations, components, parts, or combinations thereof described in the specification, but are not intended to indicate the presence of one or more other features. It should be understood that this does not exclude in advance the possibility of the existence or addition of elements, numbers, steps, operations, components, parts, or combinations thereof.

Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by a person of ordinary skill in the technical field to which the embodiments belong. Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning in the context of the related technology, and unless explicitly defined in the present application, should not be interpreted in an ideal or excessively formal sense. No.

In addition, when describing with reference to the accompanying drawings, identical components will be assigned the same reference numerals regardless of the reference numerals, and overlapping descriptions thereof will be omitted. In describing the embodiments, if it is determined that detailed descriptions of related known technologies may unnecessarily obscure the gist of the embodiments, the detailed descriptions are omitted.

Embodiments may be implemented in various types of products such as personal computers, laptop computers, tablet computers, smart phones, televisions, smart home appliances, intelligent vehicles, kiosks, and wearable devices.

Figure 1 is an exemplary diagram showing the configuration of a carbon emissions trading environment according to an embodiment of the present invention.

As shown in FIG. 1, the carbon emissions trading environment 100 may include a plurality of user terminals 110-1,..., 110-n, a carbon emissions trading device 120, and a database 130. . According to one embodiment, the database 130 is shown as being configured separately from the carbon emissions trading device 120, but the present invention is not limited thereto, and the database 130 may be provided within the carbon emissions trading device 120. For example, the carbon emissions trading device 120 may include multiple artificial intelligence models for performing machine learning algorithms. According to one embodiment, a plurality of user terminals 110-1,..., 110-n, a carbon emissions trading device 120, and a database 130 may be connected to communicate with each other through a network N.

The carbon emissions trading device 120 may receive first energy use data for a preset aggregation period of the first user. According to one embodiment, the carbon emissions trading device 120 collects a preset aggregation period from the user terminal (for example, 110-1) of the first user among the plurality of user terminals 110-1,..., 110-n. First energy usage data for (for example, 1 month, 3 months, 6 months, 1 year, etc.) may be received. For example, a plurality of user terminals 110-1,..., 110-n may be installed in a user's building/facility, etc. to collect energy usage data.

The carbon emissions trading device 120 may extract the second energy use data of the previous year corresponding to the aggregation period. According to one embodiment, the carbon emissions trading device 120 collects the second energy use data from January to March 2022 when the aggregation period of the first energy use data received from the first user is from January to March 2023. Can be extracted from the database 130.

The carbon emissions trading device 120 may calculate the amount of energy increase or decrease using the first energy use data and the second energy use data. According to one embodiment, the carbon emissions trading device 120 may calculate whether the energy used has increased or decreased compared to the previous year by using the difference between the first energy use data and the second energy use data.

The carbon emissions trading device 120 can set buyers and sellers of carbon emissions rights using the energy increase or decrease amount. According to one embodiment, the carbon emissions trading device 120 may use the calculation result of the energy increase/decrease amount to set a user whose energy use has increased compared to the previous year as a buyer, and a user whose energy use has decreased can be set as a seller.

The carbon emissions trading device 120 may generate recommended seller information for buyers. According to one embodiment, the carbon emissions trading device 120 may generate recommended seller information in descending order of energy sales price.

According to another embodiment, the carbon emissions trading device 120 may receive a user request to search for a seller under specific conditions from the user terminal 110-1 according to a user request and generate recommended seller information.

[Table 1]

Table 1 shows the flow of electricity trading.

The carbon emissions trading device 120 may be capable of relaying not only carbon emissions rights but also power transactions of power generation companies. According to one embodiment, the main target of electricity trading may include renewable energy generation, electricity produced by private power generation companies, etc. Power trading is very simple, and the only business operator in Korea that can trade power with consumers is Korea Electric Power Corporation, so until now, all power generation business operators have had to sell electricity to KEPCO, and KEPCO purchases all power supplied domestically and returns it to consumers. It plays a supply role. In other words, in domestic electricity transactions, the entity that supplies electricity is KEPCO, but there are cases where the entity that supplies electricity is not KEPCO, which is the case when the electricity you use is produced directly. KEPCO is not involved in the use of electricity produced directly for self-sufficiency. The transaction concept of electricity trading in Table 1 is that you trade with KEPCO for the remaining electricity you use. All power generation companies can directly use or sell the generated electricity, and they decide whether it would be profitable to sell or directly use it, considering the rate of return.

Breaking away from the above power trading method, power sales targeting electric vehicle users by electric vehicle charging businesses combined with ESS (Energy Storage System) and new and renewable energy may emerge. In principle, electric vehicle charging businesses must also use electricity purchased from KEPCO for the electricity they sell at their charging stations. However, compared to the general electricity rate of 100 to 200 won/kWh, the rapid charging station electricity rate of about 350 won/kWh is a high price burden, so using the exception rule for self-use of self-produced electricity, the unit production price is 100 won. By combining with renewable power generation such as solar power or ESS of less than /kWh, electricity for charging stations can be directly produced.

In order for electricity users to directly produce electricity, it is very difficult to obtain a power generation business permit and a building permit, and even if they want to generate electricity, it is not easy because the procedures, including off-site impact assessment, are very strict. Additionally, obtaining permission to establish large-scale power plants is becoming more difficult over time. On the other hand, the installation of new and renewable power generation facilities utilizing idle land within the business sites of prospective power generation business operators will be encouraged over time in line with the craze for ESG management. The basic policy of installing power generation facilities with ease and support is to directly use the produced power, but there is bound to be a difference between the amount of energy predicted in advance at the site and the actual power consumed after use, and in the end, the demand for selling surplus power is limited. will increase. The carbon emissions trading environment 100 according to an embodiment of the present invention is intended to enable easy trading of surplus power in consideration of such market conditions and background. The types of items subject to trading are as follows.

1. Amount of energy saved

Because the saved energy is an intangible asset that does not exist in the form of actual energy, it is only used for trading carbon emissions rights.

2. Surplus power/renewable power generation and self-generated power, etc.

Since the energy is a tangible asset that exists in the form of actual energy and can be traded, it can be traded for either carbon emissions rights or electricity sales purposes, depending on the business operator's judgment.

3. Extraction of transaction data

1) Basically, the inspection history and energy usage status of customers using the safety inspection platform are automatically extracted and transactions are matched through automatic calculation and artificial intelligence analysis.

2) Non-users of the safety inspection platform enter information that can connect usage data from KEPCO iSmart and city gas suppliers to API (application programming interface), and data is collected using data scraping technology and open data and automatically associated. This helps with exchange registration and trading.

Figure 2 is a conceptual diagram of a carbon emissions trading environment according to an embodiment of the present invention.

As shown in FIG. 2, in the carbon emissions trading environment, energy use information can be extracted through the carbon emissions trading environment 100 without sellers and buyers directly inputting energy use data.

Figure 3 is an example of carbon emissions trading features and screen according to an embodiment of the present invention, and Figure 4 is a carbon emissions trading process and conceptual diagram according to an embodiment of the present invention.

The carbon emissions trading device 120 can calculate the carbon emissions trading volume, register transactions, and match transactions. In addition, the carbon emissions trading device 120 calculates the amount of carbon, which is the basis of carbon emissions trading, based on the amount of energy saved in the user's building/facility during the same period compared to the previous year as the basic output, and uses the amount of energy saved compared to the previous year (surplus energy) as the basic output. usage) can be calculated automatically within the program. In addition, the carbon emissions trading device 120 can automatically register on the exchange within the platform depending on whether the seller has registered in advance. The carbon emissions trading device 120 has a similar form to a general asset and futures exchange, but the difference is that it can be used by general buyers, but on the contrary, it is optimal (price, You can select carbon credits (type, period, etc.) and recommend them to buyers. In addition, the carbon emissions trading device 120 can enable buyers to purchase recommended items optimized for them or directly search through search to respond to the transaction.

The network N can perform wireless or wired communication between a plurality of user terminals 110-1,..., 110-n, the carbon emissions trading device 120, the database 130, etc. For example, networks include long-term evolution (LTE), LTE Advanced (LTE-A), code division multiple access (CDMA), wideband CDMA (WCDMA), Wireless BroadBand (WiBro), wireless fidelity (WiFi), and Bluetooth ( Wireless communication can be performed using methods such as Bluetooth), NFC (near field communication), GPS (Global Positioning System), or GNSS (global navigation satellite system). For example, the network (N) is configured to perform wired communication according to methods such as universal serial bus (USB), high definition multimedia interface (HDMI), recommended standard 232 (RS-232), or plain old telephone service (POTS). You may.

The database 130 can store various data. Data stored in the database 130 is data acquired, processed, or used by a plurality of user terminals 110-1,..., 110-n and at least one component of the carbon emissions trading device 120. As, it may include software (e.g., a program). Database 130 may include volatile and/or non-volatile memory. As an embodiment, the database 130 includes first energy use data received from a plurality of user terminals 110-1,..., 110-n, and second energy use extracted by the carbon emissions trading device 120. Data, etc. can be stored.

In the present invention, artificial intelligence (AI) refers to technology that imitates human learning ability, reasoning ability, perception ability, etc. and implements this with a computer, and includes concepts such as machine learning and symbolic logic. may include. Machine Learning (ML) is an algorithmic technology that classifies or learns the characteristics of input data on its own. Artificial intelligence technology is a machine learning algorithm that analyzes input data, learns the results of the analysis, and makes judgments or predictions based on the results of the learning. Additionally, technologies that mimic the functions of the human brain, such as cognition and judgment, using machine learning algorithms can also be understood as the category of artificial intelligence. For example, technical fields such as verbal understanding, visual understanding, reasoning/prediction, knowledge representation, and motion control may be included.

Machine learning can refer to the process of training a neural network model using experience processing data. Machine learning can mean that computer software improves its own data processing capabilities. A neural network model is built by modeling the correlation between data, and the correlation can be expressed by a plurality of parameters. A neural network model extracts and analyzes features from given data to derive correlations between data. Repeating this process to optimize the parameters of the neural network model can be called machine learning. For example, a neural network model can learn the mapping (correlation) between input and output for data given as input-output pairs. Alternatively, a neural network model may learn the relationships by deriving regularities between given data even when only input data is given.

An artificial intelligence learning model or neural network model may be designed to implement the human brain structure on a computer, and may include a plurality of network nodes with weights that simulate neurons of a human neural network. A plurality of network nodes may have a connection relationship with each other by simulating the synaptic activity of neurons in which neurons exchange signals through synapses. In an artificial intelligence learning model, multiple network nodes are located in layers of different depths and can exchange data according to convolutional connection relationships. The artificial intelligence learning model may be, for example, an artificial neural network model (Artificial Neural Network), a convolution neural network (CNN) model, etc. As an example, an artificial intelligence learning model may be machine-learned according to methods such as supervised learning, unsupervised learning, and reinforcement learning. Machine learning algorithms for performing machine learning include Decision Tree, Bayesian Network, Support Vector Machine, Artificial Neural Network, and Ada-boost. , Perceptron, Genetic Programming, Clustering, etc. can be used.

Among them, CNN is a type of multilayer perceptrons designed to use minimal preprocessing. CNN consists of one or several convolution layers and general artificial neural network layers on top of them, and additionally utilizes weights and pooling layers. Thanks to this structure, CNN can fully utilize input data with a two-dimensional structure. Compared to other deep learning structures, CNN shows good performance in both video and audio fields. CNNs can also be trained via standard back propagation. CNNs have the advantage of being easier to train and using fewer parameters than other feedforward artificial neural network techniques.

Convolutional networks are neural networks that contain sets of nodes with bound parameters. The increasing size of available training data and the availability of computational power, combined with algorithmic advances such as piecewise linear unit and dropout training, have led to significant improvements in many computer vision tasks. For extremely large data sets, such as those available for many tasks today, overfitting is not critical, and increasing the size of the network improves test accuracy. Optimal use of computing resources becomes a limiting factor. For this purpose, distributed, scalable implementations of deep neural networks can be used.

Figure 5 is an exemplary diagram showing the configuration of a carbon emissions trading device according to an embodiment of the present invention.

As shown in FIG. 5, the carbon emissions trading device 120 may include one or more processors 122, one or more memories 124, and a transceiver 126. As an example, at least one of these components of the carbon emissions trading device 120 may be omitted, or other components may be added to the carbon emissions trading device 120. Additionally or alternatively, some components may be integrated and implemented, or may be implemented as a single or plural entity. At least some of the internal and external components of the carbon emissions trading device 120 include a system bus, general purpose input/output (GPIO), serial peripheral interface (SPI), or mobile industry processor interface (MIPI). ), etc., can be connected to each other to exchange data and/or signals.

One or more processors 122 may control at least one component of the carbon emissions trading device 120 connected to the processor 122 by running software (eg, commands, programs, etc.). Additionally, the processor 122 can perform various operations related to the present invention, such as calculation, processing, data generation, and processing. Additionally, the processor 122 may load data, etc. from one or more memories 124 or store them in one or more memories 124 .

One or more processors 122 may receive first energy usage data for a preset aggregation period of the first user. According to one embodiment, the processor 122 collects a preset aggregation period (e.g., For example, the first energy usage data for 1 month, 3 months, 6 months, 1 year, etc.) may be received through the transceiver 126.

One or more processors 122 may extract second energy use data for the previous year corresponding to the aggregation period. According to one embodiment, if the aggregation period of the first energy usage data received from the user terminal 110-1 of the first user is January to March 2023, the processor 122 collects the data from January to March 2022. Second energy usage data may be extracted from one or more memories 124 .

One or more processors 122 may calculate the amount of energy increase or decrease using the first energy usage data and the second energy usage data. According to one embodiment, the processor 122 converts the unit usage of each energy source into unit heat, calculates tons of oil equivalent for each energy source using the energy usage and unit heat, and calculates carbon dioxide emissions per unit usage of each energy source, Carbon emissions are calculated using energy usage and carbon dioxide emissions per unit usage, and energy increase or decrease can be calculated using carbon emissions. For example, the processor 122 can calculate the total amount of energy use by unifying all energy sources into tons of oil equivalent (toe). The processor 122 can convert the unit usage of each energy source into calories (kcal). For example, electrical energy can be converted into a calorific value of 2,290 kcal per 1 kWh. The relationship between unit usage and calorific value for each energy source can be shown in Table 2.

[Table 2]

The processor 122 can calculate tons of oil equivalent for each energy source using energy usage and unit heat. According to one embodiment, the processor 122 can calculate tons of oil equivalent using Equation 1 below. For example, when 100 kwh of electrical energy is used, the processor 122 can calculate the ton of oil equivalent (0.0229 toe) by multiplying the unit heat (0.000229) and the energy usage (100 kwh).

[Equation 1]

The processor 122 can calculate carbon dioxide emissions per unit usage of each energy source. According to one embodiment, the processor 122 may calculate carbon dioxide emissions per unit usage of each energy source using Equation 2 below. For example, carbon dioxide emissions per unit usage of electrical energy can be calculated as 0.4583tCO ₂ using the energy unit amount (1kwh), net calorific value conversion coefficient (0.229toe/kwh), and carbon emission coefficient (0.546tC/toe). . The net calorific value conversion coefficient for each energy source can be shown in Table 3, and the carbon emission coefficient can be shown in Table 4.

[Equation 2]

[Table 3]

[Table 4]

The processor 122 can calculate carbon emissions using energy usage and carbon dioxide emissions per unit usage. According to one embodiment, when 100 kwh of electrical energy is used, the processor 122 can calculate carbon emissions (45.83 kg) by multiplying energy usage (100 kwh) by carbon dioxide emissions per unit usage (0.4583). Carbon emissions per unit usage of each energy source can be shown in Table 5.

[Table 5]

The processor 122 can calculate the amount of energy increase or decrease using carbon emissions. According to one embodiment, the processor 122 may calculate the energy increase or decrease using the carbon emission amount according to the first energy use data and the carbon emission amount according to the second energy use data. For example, the processor 122 calculates the carbon emissions as 45.83 kg when the electric energy usage according to the first energy usage data is 100 kwh, and when the electric energy usage according to the second energy usage data is 120 kwh, the carbon emissions are calculated as 54.99 kg. Calculated in kg, the energy increase or decrease can be calculated as an increase of 9.16 kg.

One or more processors 122 may set buyers and sellers of carbon emissions rights using the energy increase or decrease amount. According to one embodiment, the processor 122 receives the third energy usage data of the first user from an external server (e.g., KEPCO energy usage data server, district heating corporation server, city gas supplier server, etc.), and 1 Compare the energy usage data and the third energy usage data, and if the first energy usage data is within the threshold (e.g., 10%) of the third energy usage data, the amount of energy increase or decrease is calculated using the first energy usage data. If the first energy usage data exceeds the threshold (eg, 10%) of the third energy usage data, the energy increase/decrease amount may be calculated using the third energy usage data. For example, the processor 122 calculates the amount of energy increase or decrease by prioritizing the internal data of the carbon emissions trading environment 100 according to an embodiment of the present invention, but the internal data is calculated using external data received from an external server. If it is within a certain threshold (e.g., 10%) compared to the external data, the energy increase/decrease amount is calculated using the internal data, and the internal data is compared to the external data and is within a certain threshold (e.g., 10%). If it is exceeded, the energy increase or decrease can be calculated using external data.

One or more processors 122 may generate recommended seller information, recommended buyer information, and matching information by considering environmental factors, social factors, and management and operational factors. According to one embodiment, the processor 122 considers environmental factors by converting the correlation between average temperature and energy usage into a first weight, and converts the increase/decrease trend in energy usage by country/industry/region into a second weight. Social factors are taken into consideration, and management and operational factors can be considered using third weights based on government policy support/regulation/industrial site conditions. Because energy use can change depending on various environmental, social, and management variables, simply comparing energy use with the previous year's energy use can cause confusion in terms of financial management. To determine the suitability of carbon emissions trading, environmental factors, social factors, and management and operational factors can be additionally reviewed to increase matching success rate and accuracy.

The processor 122 may reflect environmental factors, and the factor that has the greatest impact on energy usage is the climate factor. If the average temperature is higher than usual, carbon emissions have increased compared to the previous year, but it does not necessarily mean that carbon credits must be purchased.

Although the average temperature has increased compared to the previous year, low energy usage can be evaluated as efficient management of energy, and although the average temperature has decreased compared to the previous year, high energy usage can be evaluated as overconsumption of energy. Accordingly, when the average temperature increases, the evaluation weight of emission permits can be set so that the smaller the amount of energy used, the higher the emission permit evaluation weight. Additionally, when the average temperature decreases, the evaluation weight can be set so that the higher the energy usage, the lower the evaluation weight. By reflecting these various climate and environmental factors as the first weight, benefits for sellers can be provided. For example, the processor 122 may set the first weight so that the 15% difference can be preserved when the industry average energy increase rate is 20% and the increase is less than 5%.

The processor 122 can reflect social factors. As time goes by, energy usage continues to increase, and the amount of emission permits allocated changes depending on various social factors such as country/industry/region. In other words, depending on the conditions of the site, the rate of increase or decrease in energy usage changes relatively, and the average energy usage, increase/decrease trend, and emission permit price flow for each element are checked and the user's usage status is comparatively analyzed with other targets. The appropriateness of buying, selling, holding, etc. can be converted to a second weight and recommended to the seller. For example, the processor 122 can recommend the optimal seller and buyer through comparison of domestic and overseas energy usage trends/industries.

The processor 122 can reflect management and operational elements. Unlike general commerce, where the same standards without discrimination are applied to everyone, it is difficult to apply common standards in the energy-related industry because there is a lot of government intervention, and it is difficult to apply common standards and things change and reflect from time to time. Since the standards applied vary depending on government policy support or regulations, or on-site conditions, the value of the emission rights held may reflect an increase or decrease depending on the on-site response taking this into account. Therefore, various external factors are needed to ensure the benefit of users. can be checked from time to time and reflected as a third weight. It is difficult for general sellers and buyers to check these conditions one by one. For example, when the processor 122 temporarily replaces an old transformer in numbers 1 to 5, the carbon emissions saved through this are increased by 1.5 times for the next 3 years, or the carbon emissions recognized when purchasing carbon credits sold by an eco-friendly certified business operator. The amount of carbon emissions can be calculated by applying a third weight of 1.5 times the amount of emissions credits.

One or more memories 124 include the above-described first to third energy usage data, unit usage for each energy source, unit heat, tons of oil equivalent for each energy source, carbon dioxide emissions per unit usage for each energy source, carbon dioxide emissions per unit usage, carbon emissions, Energy increase/decrease amount, first to third weights, data in Tables 2 to 5, etc. can be stored. Additionally, one or more memories 124 may store instructions that, when executed by one or more processors 122, cause one or more processors 122 to perform operations.

According to one embodiment, the carbon emissions trading device 120 may further include a transceiver 126. The transceiver 126 includes the carbon emissions trading device 120 and various external servers (e.g., KEPCO energy usage data server, district heating corporation server, city gas supplier server, etc.), databases, client devices, and/or other It can perform wireless or wired communication between other devices. For example, the transceiver 126 may support enhanced Mobile Broadband (eMBB), Ultra Reliable Low-Latency Communications (URLLC), Massive Machine Type Communications (MMTC), long-term evolution (LTE), LTE Advance (LTE-A), UMTS (Universal Mobile Telecommunications System), GSM (Global System for Mobile communications), CDMA (code division multiple access), WCDMA (wideband CDMA), WiBro (Wireless Broadband), WiFi (wireless fidelity), Bluetooth, NFC ( Wireless communication can be performed using methods such as near field communication, GPS (Global Positioning System), or GNSS (global navigation satellite system). For example, the transceiver 126 may perform wired communication according to a method such as universal serial bus (USB), high definition multimedia interface (HDMI), recommended standard 232 (RS-232), or plain old telephone service (POTS). there is.

According to one embodiment, one or more processors 122 may control the transceiver 126 to obtain information from various external servers and databases 130. Information obtained from various external servers and databases 130 may be stored in one or more memories 124.

According to one embodiment, the carbon emissions trading device 120 may be of various types. For example, the carbon emissions trading device 120 may be a portable communication device, a computer device, or a device based on one or a combination of one or more of the foregoing devices. The carbon emissions trading device 120 of the present invention is not limited to the devices described above.

Various embodiments of the carbon emissions trading device 120 according to the present invention may be combined with each other. Each embodiment can be combined depending on the number of cases, and an embodiment of the carbon emission trading device 120 created by combining the embodiments also falls within the scope of the present invention. In addition, the internal and external components of the carbon emissions trading device 120 according to the present invention described above may be added, changed, replaced, or deleted depending on the embodiment. Additionally, the internal and external components of the carbon emissions trading device 120 described above may be implemented as hardware components.

Figure 6 is a diagram for explaining learning of a neural network according to an embodiment of the present invention.

As shown in FIG. 6, the learning device can train the neural network 128 to calculate the amount of energy increase or decrease from energy usage data. According to one embodiment, the learning device may be a separate entity from the carbon emissions trading device 120, but is not limited thereto.

The neural network 128 includes an input layer 127 through which training samples are input and an output layer 129 through which training outputs are output, and can be learned based on the difference between training outputs and labels. Here, labels may be defined based on frequency of occurrence corresponding to personal log data. The neural network 128 is connected to a group of a plurality of nodes, and is defined by weights between connected nodes and an activation function that activates the nodes.

The learning device may learn the neural network 128 using a gradient descent (GD) technique or a stochastic gradient descent (SGD) technique. The learning device can use a loss function designed by the outputs and labels of the neural network.

The learning device can calculate the training error using a predefined loss function. The loss function may be predefined with a label, output, and parameter as input variables, where the parameter may be set by weights in the neural network 128. For example, the loss function may be designed in the form of MSE (Mean Square Error), entropy, etc., and various techniques or methods may be employed in embodiments in which the loss function is designed.

The learning device can use the backpropagation technique to find weights that affect the training error. Here, the weights are relationships between nodes in the neural network 128. The learning device can use the SGD technique using labels and outputs to optimize the weights found through the backpropagation technique. For example, the learning device can update the weights of the loss function defined based on the labels, outputs, and weights using the SGD technique.

According to one embodiment, the learning device may acquire energy use data of the training target and calculate training carbon emissions from the energy use data of the training target. The learning device may acquire pre-labeled information (first labels) for each training carbon emissions, and a first label indicating a predefined amount of energy increase or decrease in the training carbon emissions (e.g., increase or decrease in energy usage, etc.). 1 labels can be obtained.

According to one embodiment, the learning device may generate first training feature vectors based on numeric features and sign (+/-) features of the training carbon emissions. A variety of methods can be employed to extract features of training carbon emissions.

According to one embodiment, the learning device may obtain training outputs by applying the first training feature vectors to the neural network 128. The learning device may train the neural network 128 based on the training outputs and the first labels. The learning device may learn the neural network 128 by calculating training errors corresponding to the training outputs and optimizing the connection relationships of nodes within the neural network 128 to minimize the training errors. The carbon emissions trading device 120 may form an energy increase or decrease amount from the first and second energy usage data using the learned neural network 128.

Figure 7 is a flowchart showing the procedure of the carbon emissions trading method according to an embodiment of the present invention.　　Although the process steps, method steps, algorithms, etc. are depicted in a sequential order in the flow diagram of Figure 7, such processes, methods, and algorithms may be configured to operate in any suitable order. In other words, the steps of the processes, methods and algorithms described in various embodiments of the invention do not need to be performed in the order described herein. Additionally, although some steps are described as being performed asynchronously, in other embodiments, some such steps may be performed concurrently. Additionally, illustration of a process by depiction in the drawings does not imply that the illustrated process excludes other variations and modifications thereto, and that any of the illustrated process or steps thereof may be used in various embodiments of the invention. It does not imply that more than one is required, nor does it imply that the illustrated process is preferred.

As shown in Figure 7, in step S710, first energy usage data is received. 　　For example, referring to Figures 1 to 6, the processor 122 of the carbon emissions trading device 120 may receive first energy use data for a preset aggregation period of the first user. According to one embodiment, the carbon emissions trading device 120 collects a preset aggregation period from the user terminal (for example, 110-1) of the first user among the plurality of user terminals 110-1,..., 110-n. First energy usage data for (for example, 1 month, 3 months, 6 months, 1 year, etc.) may be received.

In step S720, second energy usage data is extracted.　　For example, referring to Figures 1 to 6, the processor 122 of the carbon emissions trading device 120, if the aggregation period of the first energy use data received in step S710 is January to March 2023, 2022 Second energy usage data from January to March can be extracted from the database 130.

In step S730, the amount of energy increase or decrease is calculated. 　For example, referring to Figures 1 to 6, the processor 122 of the carbon emissions trading device 120 determines the difference between the first energy use data received in step S710 and the second energy use data extracted in step S720. You can use to calculate whether the energy used has increased or decreased compared to the previous year.

In step S740, buyers and sellers are set.　　For example, referring to Figures 1 to 6, the processor 122 of the carbon emissions trading device 120 uses the calculation result of the energy increase or decrease amount in step S730 to select users whose energy use has increased compared to the previous year as buyers. Users whose energy usage has decreased can be set as sellers.

In step S750, recommended seller information is generated. For example, referring to Figures 1 through 6, the processor 122 of the carbon emissions trading device 120 may generate recommended seller information in the order of the lowest selling price of energy. According to another embodiment, the processor 122 may receive a user request from the user terminal 110-1 according to the user request and generate recommended seller information.

Although the method has been described through specific embodiments, the method can also be implemented as computer-readable code on a computer-readable recording medium. Computer-readable recording media include all types of recording devices that store data that can be read by a computer system. Examples of computer-readable recording media include ROM, RAM, CD-ROM, magnetic tape, floppy disk, and optical data storage devices. Additionally, computer-readable recording media can be distributed across computer systems connected to a network, so that computer-readable code can be stored and executed in a distributed manner. And, functional programs, codes, and code segments for implementing the above embodiments can be easily deduced by programmers in the technical field to which the present invention pertains.

Above, the present invention has been described in detail using preferred embodiments, but the scope of the present invention is not limited to the specific embodiments and should be interpreted in accordance with the appended claims. Additionally, those skilled in the art should understand that many modifications and variations are possible without departing from the scope of the present invention.

100: Carbon emissions trading environment 110-1,110-n: User terminal
120: Carbon emissions trading device 130: Database
122: Processor 124: Memory
126: transceiver 127: input layer
128: Neural network 129: Output layer

Claims (10)

Receive first energy use data for a preset aggregation period of a first user, extract second energy use data of the previous year corresponding to the aggregation period, and extract the first energy use data and the second energy use data. a server that calculates an energy increase or decrease amount, uses the energy increase or decrease amount to set buyers and sellers of carbon emissions rights, and generates recommended seller information for the buyers; and
A database storing the first and second energy usage data
Including,
The server is,
Convert the unit usage of each energy source into unit heat, calculate the oil equivalent ton by energy source using the energy usage and the unit heat, calculate the carbon dioxide emissions per unit usage of each energy source, and calculate the carbon dioxide emissions per unit usage of the energy usage and the unit usage. A carbon emissions trading device that calculates carbon emissions using and calculates the energy increase or decrease using carbon emissions. delete According to claim 1,
The server is,
Using the energy increase or decrease, the user whose energy use has increased is set as the buyer, and the user whose energy use has decreased is set as the seller,
Carbon emissions trading device. According to claim 1,
The server is,
Receive third energy usage data of the first user from an external server, compare the first energy usage data with the third energy usage data, and ensure that the first energy usage data is within the threshold of the third energy usage data. In this case, the energy increase or decrease is calculated using the first energy use data, and if the first energy use data exceeds the threshold of the third energy use data, the energy increase is calculated using the third energy use data. Calculating weight loss,
Carbon emissions trading device. According to clause 4,
The server is,
Generating the recommended seller information in order of lowest energy sales price,
Carbon emissions trading device. According to claim 1,
The server is,
Generating the recommended seller information by considering environmental factors, social factors, and management and operational factors,
Carbon emissions trading device. According to clause 6,
The server is,
The correlation between average temperature and energy usage is converted into a first weight to consider the environmental factors, the increase/decrease trends in energy use by country/industry/region are converted to a second weight to consider the social factors, and the government's policy Considering the above management and operational factors using third weights according to support/regulation/industrial site conditions,
Carbon emissions trading device. According to claim 1,
The server is,
Generating the recommended seller information according to user requests,
Carbon emissions trading device. One or more processors; and
A method of trading carbon emissions using a carbon emissions trading device that includes one or more memories storing instructions that cause the one or more processors to perform operations when executed by the one or more processors, comprising:
Receiving, by the one or more processors, first energy usage data for a preset aggregation period of a first user;
extracting, by the one or more processors, second energy use data for the previous year corresponding to the aggregation period;
calculating, by the one or more processors, an energy increase or decrease amount using the first energy use data and the second energy use data;
Setting buyers and sellers of carbon emissions credits by using the energy increase or decrease amount, by the one or more processors; and
Generating, by the one or more processors, recommended seller information for the buyer,
The step of calculating the energy increase or decrease is,
Convert the unit usage of each energy source into unit heat, calculate the oil equivalent ton by energy source using the energy usage and the unit heat, calculate the carbon dioxide emissions per unit usage of each energy source, and calculate the carbon dioxide emissions per unit usage of the energy usage and the unit usage. A carbon emissions trading method including calculating carbon emissions using and calculating the energy increase or decrease using the calculated carbon emissions. One or more processors; And a computer program stored on a computer-readable recording medium so that it can be executed by a computer including one or more memories storing instructions that cause the one or more processors to perform an operation when executed by the one or more processors,
Receiving, by the one or more processors, first energy usage data for a preset aggregation period of a first user;
extracting, by the one or more processors, second energy use data for the previous year corresponding to the aggregation period;
calculating, by the one or more processors, an energy increase or decrease amount using the first energy use data and the second energy use data;
Setting buyers and sellers of carbon emissions credits by using the energy increase or decrease amount, by the one or more processors; and
Generating recommended seller information for the buyer by the one or more processors;
The step of calculating the energy increase or decrease is,
Convert the unit usage of each energy source into unit heat, calculate the oil equivalent ton by energy source using the energy usage and the unit heat, calculate the carbon dioxide emissions per unit usage of each energy source, and calculate the carbon dioxide emissions per unit usage of the energy usage and the unit usage. A computer program stored in a computer-readable recording medium, including calculating carbon emissions using and calculating the energy increase or decrease using the calculated carbon emissions.