User carbon cost emission reduction determination method and system based on power consumption behaviors

Abstract

The invention discloses a method and a system for determining the carbon cost emission reduction of a user based on a power utilization behavior, and the method comprises the steps: judging the single-person daily average carbon emission BE1 of the power utilization of a region where the user is located based on the power utilization behavior data of the user, taking BE1 as a corresponding carbon emission reference if the single-person daily average carbon emission BE1 is lower than the power utilization behavior data, and taking BE1 as a corresponding carbon emission reference if the actual carbon emission of the user is higher than BE1 in seven consecutive days; if yes, the single-person daily average carbon emission BE2 of the user 7 days before the carbon emission reduction calculation day serves as the corresponding carbon emission standard, and then the carbon emission reduction of the user is calculated according to the carbon emission standard corresponding to the power consumption behavior. According to the method, the rationality and accuracy of a user carbon emission reduction calculation result are effectively ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of carbon emission reduction measurement in power grids, and specifically relates to a method and system for determining a user's carbon emission reduction amount based on electricity usage behavior. Background Art

[0002] Carbon inclusiveness is a system design for carbon emission reduction for small and medium-sized enterprises, households and individuals, which is of great significance for promoting green and low-carbon development of the whole society. At present, my country's personal carbon accounts are still in their infancy, without a mature structure and policy guidance, and there are still major problems and limited role to play. As a new thing, personal carbon accounts still have shortcomings in the scientificity and uniformity of data accounting. Emission reduction scenarios are not rich enough, and the enthusiasm of public participation needs to be more mobilized. In addition, user information security is also an issue that needs to be urgently protected. Compared with the production end, carbon emissions at the consumer end are dispersed and involve a wide range of areas. There is no relatively unified accounting standard and method, and there are problems such as repeated calculations. At present, carbon emission reduction at the consumer end has not yet been included in the scope of national carbon market transactions, so the personal carbon asset trading mechanism has not yet been established. In the process of promoting personal carbon accounts, all regions have generally encountered these bottlenecks.

[0003] At present, there are several problems for residential users to participate in carbon credit:

[0004] First, the measurement standards for electricity carbon credit data are inconsistent, and it is difficult to calculate emission reductions. Residents' participation in electricity-related carbon credit interactions such as peak-shifting electricity consumption, electricity substitution, and low-carbon travel faces problems such as inconsistent standards, repeated calculations of emission reductions, and lack of third-party verification. my country has a large population, and the accounting system is complex and costly. There may also be many problems such as leaking public privacy.

[0005] Second, it is difficult to coordinate the participation of residential users in electricity carbon benefits. Although residents have great potential to participate in electricity demand response, it is difficult to effectively coordinate and aggregate them due to the relatively scattered geographical locations and personalized electricity consumption behaviors of residential users. In addition, residents' peak electricity consumption behavior is not due to their own electricity saving needs, and effective guidance and incentives are needed to enable them to actively participate.

[0006] Third, it is necessary to activate the environmental value of carbon emission reduction in electricity and promote the linkage of electricity and carbon markets. Although the universalization of electricity carbon can activate the environmental value of carbon emission reduction in electricity, the CCER market has not yet been opened because the electricity and carbon markets have not yet been substantially coordinated and linked, and the channel of "saving electricity-saving carbon-profit" has not been opened. Summary of the invention

[0007] The purpose of the present invention is to provide a reasonable and accurate method and system for determining user carbon credit emission reduction based on electricity consumption behavior in response to the above-mentioned problems existing in the prior art.

[0008] To achieve the above objectives, the technical solution of the present invention is as follows:

[0009] In a first aspect, the present invention proposes a method for determining a user's carbon credit emission reduction amount based on electricity consumption behavior, comprising:

[0010] S1. Determine the carbon emission benchmark corresponding to the electricity consumption behavior based on the user's electricity consumption behavior data;

[0011] S2. Calculate the user's carbon emission reduction based on the carbon emission benchmark corresponding to the electricity consumption behavior.

[0012] The S1 includes:

[0013] Statistics are collected on the daily electricity consumption of users during the power-saving period, and the actual carbon emissions of users on each day are calculated. If the actual carbon emissions of a user on a certain day are lower than the average daily carbon emissions per person in the user's area BE1, BE1 is used as the corresponding carbon emission benchmark; if the actual carbon emissions of a user are higher than BE1 for 7 consecutive days, the average daily carbon emissions per person BE2 of the user 7 days before the carbon reduction calculation date is used as the corresponding carbon emission benchmark;

[0014] The BE1 is calculated according to the following formula:

[0015]

[0016]

[0017] In the above formula, BF is the total carbon emissions of electricity consumption in the user's area on that day, N R is the total number of households in the user’s area, is the average number of people per household, y is the guidance coefficient, EF i,j is the carbon emission factor of the power grid in the i-th sub-region in the user’s area during period j, EC i,j is the electricity consumption of the ith sub-area in time period j, m and n are the number of time periods in a day and the number of sub-areas in the area where the user is located, respectively;

[0018] The BE2 is calculated according to the following formula:

[0019]

[0020] In the above formula, EF j is the grid carbon emission factor of the user in period j, EC j，t-k is the daily electricity consumption of the user in time period j, t is the carbon emission reduction calculation day, k = 1, 2, ..., 7, and R is the number of users.

[0021] The actual carbon emissions of the user on each day are calculated according to the following formula:

[0022]

[0023] In the above formula, PE t is the actual carbon emissions of the user on day t, EF j is the grid carbon emission factor of the user in period j, EC j,t is the actual electricity consumption of the user in period j on day t;

[0024] The EF i,j Calculated according to the following formula:

[0025]

[0026] In the above formula, F 1j is the direct carbon emissions from power plants in the ith sub-region during period j, F 2j is the indirect carbon emission of injected flow in the i-th sub-area in period j, W 1j is the outflow load of the ith sub-area in period j, W 2j is the outflow flow of the ith sub-area in time period j.

[0027] In S2, the user's carbon emission reduction ER is calculated according to the following formula:

[0028]

[0029]

[0030] In the above formula, PE t is the actual carbon emissions of the user on day t, K p is the peak factor, EF p is the peak power grid carbon emission factor, and EF is the power grid carbon emission factor.

[0031] The S1 also includes preprocessing the user's electricity usage behavior data as follows:

[0032] If the transcript is not continuous, fill in the missing data as follows:

[0033] Today's meter reading = today's minimum meter reading + (T1 + T2 + ... + T10) / 10 / current multiplier

[0034] Wherein, T is today, T1 = (electricity recorded on T-1) – (electricity recorded on T-2), T2 = (electricity recorded on T-2) – (electricity recorded on T-3), and so on;

[0035] If the meter reading is reversed, the electricity reading is determined based on the following formula:

[0036] Reading power = (today's reading + 10 表位数 - the previous day's reading) × comprehensive ratio;

[0037] If the meter reading is reversed, the power reading is determined based on the following formula:

[0038] Reading power = (previous day's reading - today's reading) × comprehensive multiple;

[0039] If the meter reading is reversed and flipped, the power reading is determined based on the following formula:

[0040] Reading power = (today's reading + 10 表位数 - previous day's reading) × comprehensive multiplier.

[0041] In a second aspect, the present invention proposes a system for determining user carbon emission reduction based on electricity consumption behavior, including a carbon emission benchmark determination module and a user carbon emission reduction calculation module;

[0042] The carbon emission benchmark determination module is used to determine the carbon emission benchmark corresponding to the electricity consumption behavior based on the user's electricity consumption behavior data;

[0043] The user carbon emission reduction calculation module is used to calculate the user's carbon emission reduction according to the carbon emission benchmark corresponding to the electricity consumption behavior.

[0044] The carbon emission benchmark determination module is used to count the daily electricity consumption of users during the power saving period and calculate the actual carbon emissions of users on each day. If the actual carbon emissions of a user on a certain day are lower than the average daily carbon emissions per person BE1 of electricity consumption in the user's area, BE1 is used as the corresponding carbon emission benchmark; if the actual carbon emissions of a user for 7 consecutive days are higher than BE1, the average daily carbon emissions per person BE2 of the user 7 days before the carbon emission reduction calculation date is used as the corresponding carbon emission benchmark; wherein,

[0045] The BE1 is calculated according to the following formula:

[0046]

[0047]

[0048] In the above formula, BF is the total carbon emissions of electricity consumption in the user's area on that day, N R is the total number of households in the user’s area, is the average number of people per household, y is the guidance coefficient, EF i,j is the carbon emission factor of the power grid in the i-th sub-region in the user’s area during period j, EC i,j is the electricity consumption of the ith sub-area in time period j, m and n are the number of time periods in a day and the number of sub-areas in the area where the user is located, respectively;

[0049] The BE2 is calculated according to the following formula:

[0050]

[0051] In the above formula, EF j is the grid carbon emission factor of the user in period j, EC j，t-k is the daily electricity consumption of the user in time period j, t is the carbon emission reduction calculation day, k = 1, 2, ..., 7, and R is the number of users.

[0052] The actual carbon emissions of the user on each day are calculated according to the following formula:

[0053]

[0054] In the above formula, PE t is the actual carbon emissions of the user on day t, EF j is the grid carbon emission factor of the user in period j, EC j,t is the actual electricity consumption of the user in period j on day t;

[0055] The EF i,j Calculated according to the following formula:

[0056]

[0057] In the above formula, F 1j is the direct carbon emissions from power plants in the ith sub-region during period j, F 2j is the indirect carbon emission of injected flow in the i-th sub-area in period j, W 1j is the outflow load of the ith sub-area in period j, W 2j is the outflow flow of the ith sub-area in time period j.

[0058] The user carbon emission reduction calculation module calculates the user's carbon emission reduction ER according to the following formula:

[0059]

[0060]

[0061] In the above formula, PE t is the actual carbon emissions of the user on day t, K p is the peak factor, EF p is the peak power grid carbon emission factor, and EF is the power grid carbon emission factor.

[0062] The system further includes a data preprocessing module, which is used to preprocess the user's electricity usage behavior data as follows:

[0063] If the transcript is not continuous, fill in the missing data as follows:

[0064] Today's meter reading = today's minimum meter reading + (T1 + T2 + ... + T10) / 10 / current multiplier

[0065] Wherein, T is today, T1 = (electricity recorded on T-1) – (electricity recorded on T-2), T2 = (electricity recorded on T-2) – (electricity recorded on T-3), and so on;

[0066] If the meter reading is reversed, the electricity reading is determined based on the following formula:

[0067] Reading power = (today's reading + 10 表位数 - the previous day's reading) × comprehensive ratio;

[0068] If the meter reading is reversed, the power reading is determined based on the following formula:

[0069] Reading power = (previous day's reading - today's reading) × comprehensive multiple;

[0070] If the meter reading is reversed and flipped, the power reading is determined based on the following formula:

[0071] Reading power = (today's reading + 10 表位数 - previous day's reading) × comprehensive multiplier.

[0072] Compared with the prior art, the present invention has the following beneficial effects:

[0073] 1. The present invention proposes a method for determining user carbon emission reduction based on electricity usage behavior, which determines the carbon emission benchmark corresponding to the electricity usage behavior based on the user's electricity usage behavior data, and then calculates the user's carbon emission reduction according to the carbon emission benchmark corresponding to the electricity usage behavior. This method effectively ensures the rationality and accuracy of the user's carbon emission reduction calculation results by setting corresponding carbon emission benchmarks for different electricity usage data.

[0074] 2. The method for determining user carbon emission reduction based on electricity consumption behavior proposed in the present invention takes into account the possible power shortage during the peak periods in summer and winter, and the fact that power supply is mainly supported by thermal power (especially coal-fired power) during the peak period, which significantly increases the carbon emissions of electricity during the peak period. Power saving behavior during this period will produce greater emission reductions. The peak coefficient is introduced in the calculation process of user carbon emission reduction, making the calculation result of carbon emission reduction more reasonable.

[0075] 3. The present invention proposes a method for determining user carbon credit emission reduction based on electricity usage behavior, which pre-processes the user's electricity usage behavior data for abnormal conditions such as discontinuous meter reading numbers, meter reading code flipping, and meter reading reversal, which is conducive to the accurate determination of carbon emission benchmarks and calculation of carbon emissions in the later stage. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1This is a flow chart of the method described in Example 1.

[0077] Figure 2 Schematic diagram of solving the carbon emission factor of the power grid in Example 1.

[0078] Figure 3 This is a structural diagram of the system described in Example 2. DETAILED DESCRIPTION

[0079] The present invention is further described in detail below in conjunction with specific implementations and drawings.

[0080] Embodiment 1:

[0081] like Figure 1 As shown, a method for determining the carbon credit emission reduction amount of a user based on electricity consumption behavior is implemented in the following steps:

[0082] 1. Obtain the user's electricity consumption behavior data through the electricity consumption information collection platform and the customer service information platform, including user number, user name, user type, electricity consumption category, voltage level, electricity consumption address, historical daily electricity consumption, electricity consumption date, daily freeze stop code, etc.

[0083] 2. Preprocess the abnormal power data in the user's power consumption behavior data as follows:

[0084] If the transcript is not continuous, fill in the missing data as follows:

[0085] Today's meter reading = today's minimum meter reading + (T1 + T2 + ... + T10) / 10 / current multiplier

[0086] Wherein, T is today, T1 = (power reading on T-1) – (power reading on T-2), T2 = (power reading on T-2) – (power reading on T-3), and so on; wherein, power reading = (today's reading – previous day's reading) × comprehensive multiple;

[0087] If the meter reading is reversed, the electricity reading is determined based on the following formula:

[0088] Reading power = (today's reading + 10 表位数 - the previous day's reading) × comprehensive ratio;

[0089] If the meter reading is reversed, the power reading is determined based on the following formula:

[0090] Reading power = (previous day's reading - today's reading) × comprehensive multiple;

[0091] If the meter reading is reversed and flipped, the power reading is determined based on the following formula:

[0092] Reading power = (today's reading + 10 表位数- previous day's reading) × comprehensive multiplier.

[0093] 3. Based on the user's electricity consumption behavior data, the user's daily electricity consumption during the power saving period is counted, and the user's actual carbon emissions on each day are calculated according to the following formula:

[0094]

[0095] In the above formula, PE t is the actual carbon emissions of the user on day t, EF j is the grid carbon emission factor of the user in period j, EC j,t is the actual electricity consumption of the user in time period j on day t.

[0096] 4. In view of the different indirect carbon emissions contained in electricity in different regions and at different times, the residential electricity consumption area A is divided into A1, A2, ..., A according to the grid structure and geographical location. n There are n regional nodes, and the time value range (24 hours) is discretized into m intervals by equal width discretization method with interval T. Based on a certain area A i The residential electricity consumption EC in the jth time interval i,jj Total carbon emissions for the day BF i,j The information set is:

[0097] A i ,j,EC i,j ,i∈(1,n),j∈(1,m).

[0098] Determine whether the user's actual carbon emissions on a certain day are lower than the average daily carbon emissions per person in the user's area BE1. If so, BE1 is used as the corresponding carbon emission benchmark; if the user's actual carbon emissions for 7 consecutive days are higher than BE1, the user's average daily carbon emissions per person BE2 for 7 days before the carbon emission reduction calculation date is used as the corresponding carbon emission benchmark; wherein BE1 is calculated according to the following formula:

[0099]

[0100]

[0101] In the above formula, BF is the total carbon emissions of electricity consumption in the user's area on that day, N R is the total number of households in the user’s area, is the average number of households, y is the guiding coefficient, and the expected target value that can be achieved by adopting a more effective energy-saving lifestyle under the premise of ensuring the normal electricity consumption of residents, EF i,j is the carbon emission factor of the power grid in the i-th sub-region in the user’s area during period j, EC i,jis the electricity consumption of the ith sub-area in time period j, m and n are the number of time periods in a day and the number of sub-areas in the area where the user is located, respectively;

[0102] EF i,j Take subarea A i is the node, obtained based on the power flow, is node A i Indirect carbon emissions corresponding to unit electricity consumption, such as Figure 2 As shown, the carbon flow and tidal current have time-varying properties, and the calculation formula is as follows:

[0103]

[0104] In the above formula, F 1j is the direct carbon emissions from power plants in the ith sub-region during period j, F 2j is the indirect carbon emission of injected flow in the i-th sub-area in period j, W 1j is the outflow load of the ith sub-area in period j, W 2j is the outflow flow of the ith sub-area in period j;

[0105] The BE2 is calculated according to the following formula:

[0106]

[0107] In the above formula, EF j is the grid carbon emission factor of the user in period j, EC j，t-k is the daily electricity consumption of the user in time period j, t is the carbon emission reduction calculation day, k = 1, 2, ..., 7, and R is the number of users.

[0108] 5. Calculate the user's carbon emission reduction ER according to the following formula:

[0109]

[0110]

[0111] In the above formula, PE t is the actual carbon emissions of the user on day t, K p is the peak factor, EF p is the peak power grid carbon emission factor, and EF is the power grid carbon emission factor.

[0112] Embodiment 2:

[0113] like Figure 3 As shown, a system for determining user carbon emission reduction based on electricity consumption behavior includes a data preprocessing module, a carbon emission benchmark determination module, and a user carbon emission reduction calculation module.

[0114] The data preprocessing module is used to preprocess the abnormal power data in the user's power consumption behavior data as follows:

[0115] If the transcript is not continuous, fill in the missing data as follows:

[0116] Today's meter reading = today's minimum meter reading + (T1 + T2 + ... + T10) / 10 / current multiplier

[0117] Wherein, T is today, T1 = (electricity recorded on T-1) – (electricity recorded on T-2), T2 = (electricity recorded on T-2) – (electricity recorded on T-3), and so on;

[0118] If the meter reading is reversed, the electricity reading is determined based on the following formula:

[0119] Reading power = (today's reading + 10 表位数 - the previous day's reading) × comprehensive ratio;

[0120] If the meter reading is reversed, the power reading is determined based on the following formula:

[0121] Reading power = (previous day's reading - today's reading) × comprehensive multiple;

[0122] If the meter reading is reversed and flipped, the power reading is determined based on the following formula:

[0123] Reading power = (today's reading + 10 表位数 - previous day's reading) × comprehensive multiplier.

[0124] The carbon emission benchmark determination module is used to:

[0125] The daily electricity consumption of users during the power-saving period is counted, and the actual carbon emissions of users on each day are calculated according to the following formula:

[0126]

[0127] In the above formula, PE t is the actual carbon emissions of the user on day t, EF j is the grid carbon emission factor of the user in period j, EC j,t is the actual electricity consumption of the user in period j on day t;

[0128] Determine whether the user's actual carbon emissions on a certain day are lower than the average daily carbon emissions per person in the user's area BE1. If so, BE1 is used as the corresponding carbon emissions benchmark:

[0129]

[0130]

[0131]

[0132] In the above formula, BF is the total carbon emissions of electricity consumption in the user's area on that day, N R is the total number of households in the user’s area, is the average number of people per household, y is the guidance coefficient, EF i,j is the carbon emission factor of the power grid in the i-th sub-region in the user’s area during period j, EC i,j is the power consumption of the ith sub-area in time period j, m and n are the number of time periods in a day and the number of sub-areas in the area where the user is located, respectively. 1j is the direct carbon emissions from power plants in the ith sub-region during period j, F 2j is the indirect carbon emission of injected flow in the i-th sub-area in period j, W 1j is the outflow load of the ith sub-area in period j, W 2j is the outflow flow of the ith sub-area in period j;

[0133] If the actual carbon emissions of a user for 7 consecutive days are higher than BE1, the user's average daily carbon emissions per person BE2 for 7 days before the carbon reduction calculation date will be used as the corresponding carbon emission benchmark:

[0134]

[0135] In the above formula, EF j is the grid carbon emission factor of the user in period j, EC j，t-k is the daily electricity consumption of the user in time period j, t is the carbon emission reduction calculation day, k=1,2,...,7, and R is the number of users.

[0136] The user carbon emission reduction calculation module is used to calculate the user's carbon emission reduction ER according to the carbon emission benchmark corresponding to the electricity consumption behavior:

[0137]

[0138]

[0139] In the above formula, PE t is the actual carbon emissions of the user on day t, K p is the peak factor, EF p is the peak power grid carbon emission factor, and EF is the power grid carbon emission factor.

Claims

1. A method for determining user carbon credit emission reduction based on electricity consumption behavior, characterized in that: The method comprises: S1. Determine the carbon emission benchmark corresponding to the electricity consumption behavior based on the user's electricity consumption behavior data; S2. Calculate the user's carbon emission reduction based on the carbon emission benchmark corresponding to the electricity consumption behavior.

2. According to a method for determining user carbon credit emission reduction based on electricity consumption behavior according to claim 1, it is characterized in that: The S1 includes: Statistics are collected on the daily electricity consumption of users during the power-saving period, and the actual carbon emissions of users on each day are calculated. If the actual carbon emissions of a user on a certain day are lower than the average daily carbon emissions per person in the user's area BE1, BE1 is used as the corresponding carbon emission benchmark; if the actual carbon emissions of a user are higher than BE1 for 7 consecutive days, the average daily carbon emissions per person BE2 of the user 7 days before the carbon reduction calculation date is used as the corresponding carbon emission benchmark; The BE1 is calculated according to the following formula: In the above formula, BF is the total carbon emissions of electricity consumption in the user's area on that day, N R is the total number of households in the user’s area, is the average number of people per household, y is the guidance coefficient, EF i,j is the carbon emission factor of the power grid in the i-th sub-region in the user’s area during period j, EC i,j is the electricity consumption of the ith sub-area in time period j, m and n are the number of time periods in a day and the number of sub-areas in the area where the user is located, respectively; The BE2 is calculated according to the following formula: In the above formula, EF j is the grid carbon emission factor of the user in period j, EC j，t-k is the daily electricity consumption of the user in time period j, t is the carbon emission reduction calculation day, k = 1, 2, ..., 7, and R is the number of users.

3. According to a method for determining user carbon credit emission reduction based on electricity consumption behavior according to claim 2, it is characterized in that: The actual carbon emissions of the user on each day are calculated according to the following formula: In the above formula, PE t is the actual carbon emissions of the user on day t, EF j is the grid carbon emission factor of the user in period j, EC j,t is the actual electricity consumption of the user in period j on day t; The EF i,j Calculated according to the following formula: In the above formula, F 1j is the direct carbon emissions from power plants in the ith sub-region during period j, F 2j is the indirect carbon emission of injected flow in the i-th sub-area in period j, W 1j is the outflow load of the ith sub-area in period j, W 2j is the outflow flow of the ith sub-area in time period j.

4. A method for determining user carbon credit emission reduction based on electricity consumption behavior according to claim 2 or 3, characterized in that: In S2, the user's carbon emission reduction ER is calculated according to the following formula: In the above formula, PE t is the actual carbon emissions of the user on day t, K p is the peak factor, EF p is the peak power grid carbon emission factor, and EF is the power grid carbon emission factor.

5. A method for determining user carbon credit emission reduction based on electricity consumption behavior according to any one of claims 1 to 3, characterized in that: The S1 also includes preprocessing the user's electricity usage behavior data as follows: If the transcript is not continuous, fill in the missing data as follows: Today's meter reading = today's minimum meter reading + (T1 + T2 + ... + T10) / 10 / current multiplier Wherein, T is today, T1 = (electricity recorded on T-1) – (electricity recorded on T-2), T2 = (electricity recorded on T-2) – (electricity recorded on T-3), and so on; If the meter reading is reversed, the electricity reading is determined based on the following formula: Reading power = (today's reading + 10 表位数 - the previous day's reading) × comprehensive ratio; If the meter reading is reversed, the power reading is determined based on the following formula: Reading power = (previous day's reading - today's reading) × comprehensive multiple; If the meter reading is reversed and flipped, the power reading is determined based on the following formula: Reading power = (today's reading + 10 表位数 - previous day's reading) × comprehensive multiplier.

6. A system for determining user carbon credit emission reduction based on electricity consumption behavior, characterized in that: The system includes a carbon emission benchmark determination module and a user carbon emission reduction calculation module; The carbon emission benchmark determination module is used to determine the carbon emission benchmark corresponding to the electricity consumption behavior based on the user's electricity consumption behavior data; The user carbon emission reduction calculation module is used to calculate the user's carbon emission reduction according to the carbon emission benchmark corresponding to the electricity consumption behavior.

7. A system for determining user carbon credit emission reduction based on electricity consumption behavior according to claim 6, characterized in that: The carbon emission benchmark determination module is used to count the daily electricity consumption of users during the power saving period and calculate the actual carbon emissions of users on each day. If the actual carbon emissions of a user on a certain day are lower than the average daily carbon emissions per person BE1 of electricity consumption in the user's area, BE1 is used as the corresponding carbon emission benchmark; if the actual carbon emissions of a user for 7 consecutive days are higher than BE1, the average daily carbon emissions per person BE2 of the user 7 days before the carbon emission reduction calculation date is used as the corresponding carbon emission benchmark; wherein, The BE1 is calculated according to the following formula: In the above formula, BF is the total carbon emissions of electricity consumption in the user's area on that day, N R is the total number of households in the user’s area, is the average number of people per household, y is the guidance coefficient, EF i,j is the carbon emission factor of the power grid in the i-th sub-region in the user’s area during period j, EC i,j is the electricity consumption of the ith sub-area in time period j, m and n are the number of time periods in a day and the number of sub-areas in the area where the user is located, respectively; The BE2 is calculated according to the following formula: In the above formula, EF j is the grid carbon emission factor of the user in period j, EC j，t-k is the daily electricity consumption of the user in time period j, t is the carbon emission reduction calculation day, k = 1, 2, ..., 7, and R is the number of users.

8. A system for determining user carbon credit emission reduction based on electricity consumption behavior according to claim 7, characterized in that: The actual carbon emissions of the user on each day are calculated according to the following formula: In the above formula, PE t is the actual carbon emissions of the user on day t, EF j is the grid carbon emission factor of the user in period j, EC j,t is the actual electricity consumption of the user in period j on day t; The EF i,j Calculated according to the following formula: In the above formula, F 1j is the direct carbon emissions from power plants in the ith sub-region during period j, F 2j is the indirect carbon emission of injected flow in the i-th sub-area in period j, W 1j is the outflow load of the ith sub-area in period j, W 2j is the outflow flow of the ith sub-area in time period j.

9. A system for determining user carbon credit emission reduction based on electricity consumption behavior according to claim 7 or 8, characterized in that: The user carbon emission reduction calculation module calculates the user's carbon emission reduction ER according to the following formula: In the above formula, PE t is the actual carbon emissions of the user on day t, K p is the peak factor, EF p is the peak power grid carbon emission factor, and EF is the power grid carbon emission factor.

10. A system for determining user carbon credit emission reduction based on electricity consumption behavior according to any one of claims 6 to 8, characterized in that: The system further includes a data preprocessing module, which is used to preprocess the user's electricity usage behavior data as follows: If the transcript is not continuous, fill in the missing data as follows: Today's meter reading = today's minimum meter reading + (T1 + T2 + ... + T10) / 10 / current multiplier Wherein, T is today, T1 = (electricity recorded on T-1) – (electricity recorded on T-2), T2 = (electricity recorded on T-2) – (electricity recorded on T-3), and so on; If the meter reading is reversed, the electricity reading is determined based on the following formula: Reading power = (today's reading + 10 表位数 - the previous day's reading) × comprehensive ratio; If the meter reading is reversed, the power reading is determined based on the following formula: Reading power = (previous day's reading - today's reading) × comprehensive multiple; If the meter reading is reversed and flipped, the power reading is determined based on the following formula: Reading power = (today's reading + 10 表位数 - previous day's reading) × comprehensive ratio.

Images