CN115496286B - Neural network carbon emission prediction method based on big data environment and application - Google Patents
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Abstract
The invention provides a neural network carbon emission prediction method based on a big data environment, which comprises the following steps: s1, acquiring multi-channel carbon emission data through an Internet of things platform network, forming the multi-channel emission data into a corresponding emission data set, and carrying out normalization processing; s2, measuring and calculating offset of carbon emission data, dynamically correcting the data, calculating a corresponding mapping transformation function through a TCN time convolution neural network, S3, acquiring multi-channel carbon emission data fusion through the mapping transformation function, judging a carbon emission prediction interval through a prediction accuracy model, and uploading the carbon emission prediction interval to an Internet of things platform network.
Description
Technical Field
The invention relates to the field of big data analysis, in particular to a neural network carbon emission prediction method and application based on big data environment.
Background
Due to global warming and greenhouse effect, research on carbon emissions and carbon absorption in various fields has been conducted in the society today, so that achieving carbon neutralization and carbon peak to provide theoretical support, how to effectively predict carbon emission mechanisms presents great challenges to those skilled in the art. According to the current situation of research, the use scheduling in the electric power system and the gas field is centralized control type, the response speed to carbon emission is slow through the traditional neural network learning, the total amount of carbon emission cannot be effectively predicted through theoretical data, and the result of carbon emission can be obtained through centralized control type scheduling, but the constraint conditions of external use time, idle state, occupied state and load are less, so that an accurate carbon emission prediction model is difficult to establish. There is a need for a person skilled in the art to solve the corresponding technical problems.
Disclosure of Invention
The invention aims at least solving the technical problems in the prior art, and particularly creatively provides a neural network carbon emission prediction method based on a big data environment.
In order to achieve the above object of the present invention, the present invention provides a neural network carbon emission prediction method based on a big data environment, comprising the steps of:
s1, acquiring multi-channel carbon emission data through an Internet of things platform network, forming the multi-channel emission data into a corresponding emission data set, and carrying out normalization processing;
s2, calculating the offset of the carbon emission data, dynamically correcting the data, calculating a corresponding mapping transformation function through a TCN time convolution neural network,
and S3, acquiring multi-channel carbon emission data fusion through a mapping transformation function, judging a carbon emission prediction interval through a prediction accuracy model, and uploading the carbon emission prediction interval to an Internet of things platform network.
According to the above technical solution, the step S1 preferably includes:
s1-1, acquiring corresponding electric power carbon emission data and gas carbon emission data in an Internet of things platform network, performing objective function calculation according to the carbon emission data,
obtaining a carbon emission objective function P (i) of the output power of the electric power in unit time and the gas power in unit time, training a network node of the electric power and the gas carbon emission through the calculated result,
wherein u is the electric power carbon emission regulating coefficient, A elec Is the power used by the electric power in unit time, v is the carbon emission regulating coefficient of the fuel gas, A gas The power is used for the electric power in unit time, and t is the unit time; and obtaining pre-stored data of carbon emission data according to the network node.
According to the above technical solution, preferably, the S1 further includes:
s1-2, determining probability distribution of carbon emission data according to an objective function,
first, according to the electric power carbon emission data vector b= (B 1 ,b 2 ,...,b m ) T Gas carbon emission data vector c= (C) 1 ,c 2 ,...,c m ) T A probability distribution calculation is performed, wherein the superscript T is transposed,
then, the probability distribution formula obtained is:
the carbon emission probability distribution number k, exp (x) is an exponential function, F (B, C) is a carbon emission energy function, β is a target adjustment coefficient, and L (k) is a distribution scale of a carbon emission target.
According to the above technical solution, the S2 preferably includes:
s2-1, carrying out scale division on the carbon emission data according to probability distribution to form an offset formula of the carbon emission data, and according to the scale formula,
E＝a·E 0 +M j lambda, wherein E is a carbon emission scale calculation expression formula, a is a carbon emission scale adjustment coefficient, E 0 For pre-stored corresponding period history carbon emission data, M j Is the real-time carbon emission data accumulated value of j weeks, lambda is the carbon emission factor,
s2-2, calculating a displacement formula of the carbon emission probability distribution through a scale formula,
D＝E+ΔE 0 according to the calculation result of the scale formula, the change value delta E of the carbon emission data is recorded through the pre-stored corresponding period history 0 Obtaining a displacement value of carbon emission data;
the offset of the carbon emission data can be obtained according to the displacement value, the offset is calculated by an offset formula,
According to the above technical solution, preferably, the S2 further includes:
s2-3, the offset calculated according to the offset formula of the j axis is required to be readjusted in the dynamic property and the constraint property, and the dynamic constraint is carried out through the constraint condition of the carbon emission data, so that the readjustment of the offset is achieved;
the dynamic formula of the carbon emission data is that,
G＝G j +η·P j +Z, where G j Is the energy consumption in the carbon emission data of j weeks, eta is a dynamic regulating factor, P j As an overall variation of the j weeks carbon emission data,
when eta is more than 0, the expression of the dynamic formula of the carbon emission data can reflect the actual numerical value of the dynamic carbon emission data, and when eta is less than or equal to 0, the dynamic formula does not relate to the total variation of the carbon emission data.
According to the above technical solution, preferably, the S2 further includes:
s2-4, training the TCN neural network for the real-time carbon emission data according to the obtained dynamic value of the carbon emission data, training and adjusting the dynamic value in the carbon emission process,
the Gaussian function is adopted to carry out the spatial mapping transformation on the neuron basis function in the neural network to the input carbon emission data dynamic value,
the prediction error of the neural network is derived from the mapping transform,
According to the above technical solution, the step S3 preferably includes:
s3-1, according to a sequence formed by the dynamic values of the carbon emission data, carrying out carbon emission data statistics according to a data regression model after obtaining a prediction error, judging a carbon emission dynamic change component by actually generating values for the carbon emission data in a carbon emission data prediction interval, so that N is formed energy Can be predicted in advance; and f (x) is a regression model of the prediction process, a training sample is extracted from the carbon emission dynamic value, and the minimum variance standard formed in the carbon emission process is obtained for estimating the prediction precision through prediction error judgment.
According to the above technical solution, preferably, the S3 further includes:
s3-2, wherein the prediction precision formula is as follows
after calculation according to the prediction accuracy, a prediction confidence interval of the corresponding carbon emission data is given, and energy consumption early warning is carried out on the carbon emission data within the interval range, so that the distribution situation of the carbon emission data is predicted, and the prediction interval is adjusted in real time according to the trained accuracy value.
The invention also discloses an application of the neural network carbon emission prediction method based on the big data environment, which comprises the following steps: prediction of carbon emission data for use in fossil fuel combustion, biomass combustion, petroleum and natural gas system escape, paddy methane, agricultural nitrous oxide, animal intestinal fermentation, solid waste treatment, wastewater treatment processes by the method of claim 1.
In summary, due to the adoption of the technical scheme, the beneficial effects of the invention are as follows:
in a certain time period, calculating a mapping transformation function through carbon emission data, and acquiring the correlation degree of carbon emission, so that the deviation of the carbon emission is known, and the dependence on the preference of the carbon emission type under the condition of acquiring the carbon emission data according to the variation degree of the carbon emission data of multiple channels; and obtaining the predicted intensity of carbon emission and the predicted emission frequency of the carbon emission type according to the prediction model, continuously training convergence values through a neural network, and performing the later prediction work of the carbon emission through the prediction accuracy model so as to provide data reference for carbon neutralization.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
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The foregoing and/or additional aspects and advantages of the invention will become apparent and may be better understood from the following description of embodiments taken in conjunction with the accompanying drawings in which:
FIG. 1 is a general schematic of the present invention;
fig. 2 is a schematic diagram of an embodiment of the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the invention.
As shown in fig. 1 and 2, the invention discloses a neural network carbon emission prediction method based on a big data environment, comprising the following steps:
s1, acquiring multi-channel carbon emission data through an Internet of things platform network, forming the multi-channel emission data into a corresponding emission data set, and carrying out normalization processing;
s2, calculating the offset of the carbon emission data, dynamically correcting the data, calculating a corresponding mapping transformation function through a TCN time convolution neural network,
and S3, acquiring multi-channel carbon emission data fusion through a mapping transformation function, judging a carbon emission prediction interval through a prediction accuracy model, and uploading the carbon emission prediction interval to an Internet of things platform network.
The internet of things platform can be connected to corresponding electric power and gas and other data acquisition equipment through a wired or wireless network to perform related data acquisition operation.
According to the above technical solution, the step S1 preferably includes:
s1-1, acquiring corresponding electric power carbon emission data and fuel gas carbon emission data in a cloud network, performing objective function calculation according to the carbon emission data,
obtaining a carbon emission objective function P (i) of the output power of the electric power in unit time and the gas power in unit time, training a network node of the electric power and the gas carbon emission through the calculated result,
wherein u is the electric power carbon emission regulating coefficient, A elec Is the power used by the electric power in unit time, v is the carbon emission regulating coefficient of the fuel gas, A gas The power is used for the electric power in unit time, and t is the unit time; obtaining pre-stored data of carbon emission data according to the network node, so as to divide the discrete data linearly;
s1-2, determining probability distribution of carbon emission data according to an objective function,
first, according to the electric power carbon emission data vector b= (B 1 ,b 2 ,...,b m ) T Gas carbon emission data vector c= (C) 1 ,c 2 ,...,c m ) T A probability distribution calculation is performed, wherein the superscript T is transposed,
then, the probability distribution formula obtained is:
the carbon emission probability distribution sequence number k, exp (x) is an exponential function, F (B, C) is a carbon emission energy function, beta is a target adjustment coefficient, L (k) is a distribution scale of a carbon emission target, the distribution scale can carry out distribution quantification on the data trend of carbon emission, the distribution scale is subjected to parameter adjustment through the target adjustment coefficient, and then normalization processing is carried out, so that the carbon emission data can be converged,
according to the above technical solution, the S2 preferably includes:
s2-1, carrying out scale division on the carbon emission data according to probability distribution to form an offset formula of the carbon emission data, and according to the scale formula,
E＝a·E 0 +M j lambda, wherein E is a carbon emission scale calculation expression formula, a is a carbon emission scale adjustment coefficient, E 0 For pre-stored corresponding period history carbon emission data, M j Is the real-time carbon emission data accumulated value of j weeks, lambda is the carbon emission factor,
s2-2, calculating a displacement formula of the carbon emission probability distribution through a scale formula,
D＝E+ΔE 0 according to the calculation result of the scale formula, the change value delta E of the carbon emission data is recorded through the pre-stored corresponding period history 0 Obtaining a displacement value of carbon emission data;
the offset of the carbon emission data can be obtained according to the displacement value, the offset is calculated by an offset formula,
the period of data acquisition can be extended to one month, one quarter or one year, and timely adjustment is carried out according to the accuracy degree of the data by a user, so that different data prediction requirements are met.
S2-3, the offset calculated according to the offset formula of the j axis is required to be readjusted in the dynamic property and the constraint property, and the dynamic constraint is carried out through the constraint condition of the carbon emission data, so that the readjustment of the offset is achieved;
the dynamic formula of the carbon emission data is that,
G＝G j +η·P j +Z, where G j The energy consumption in the carbon emission data of j weeks comprises comprehensive data of electric energy and gas energy, and the total value of the carbon emission in theory can be known through the comprehensive data, so that the carbon emission data actually obtained in j weeks is taken as the basis and source of dynamic regulation, eta is a dynamic regulating factor and P j As an overall variation of the j weeks carbon emission data,
when eta is more than 0, the expression of the dynamic formula of the carbon emission data can represent the actual numerical value of the dynamic carbon emission data, when eta is less than or equal to 0, the dynamic formula does not hold,
s2-4, training the TCN neural network for the real-time carbon emission data according to the acquired carbon emission data dynamic value, training and adjusting the dynamic value in the carbon emission process, so as to prepare for predicting the carbon emission data of the next week,
the Gaussian function is adopted to carry out the spatial mapping transformation on the neuron basis function in the neural network to the input carbon emission data dynamic value,
the prediction error of the neural network is derived from the mapping transform,
after the collected carbon emission data of one week is adjusted according to the dynamic value, the carbon emission data of the previous week is compared with the carbon emission data of the previous week, and the corresponding difference value is subjected to component sequence representation to be used as preparation for carbon emission data prediction;
according to the above technical solution, the step S3 preferably includes:
s3-1, according to a sequence formed by the dynamic values of the carbon emission data, carrying out carbon emission data statistics according to a data regression model after obtaining a prediction error, judging a carbon emission dynamic change component by actually generating values for the carbon emission data in a carbon emission data prediction interval, so that N is formed energy Can be predicted in advance; the regression model of the prediction process is f (x), a training sample is extracted from the carbon emission dynamic value, and the minimum variance standard formed in the carbon emission process is obtained for estimating the prediction precision through prediction error judgment;
s3-2, wherein the prediction precision formula is as follows
after calculation according to the prediction accuracy, a prediction confidence interval of the corresponding carbon emission data is given, and energy consumption early warning is carried out on the carbon emission data within the interval range, so that the distribution situation of the carbon emission data is predicted, and the prediction interval is adjusted in real time according to the trained accuracy value.
The carbon emission data of electric power and fuel gas are considered for carbon emission, and the carbon emission data in the processes of fossil fuel combustion, biomass combustion, petroleum and natural gas system escape, paddy methane, agricultural nitrous oxide, animal intestinal fermentation, solid waste treatment and wastewater treatment can be acquired, corresponding data content is learned through a neural network, and carbon emission prediction is performed.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the spirit and scope of the invention as defined by the appended claims and their equivalents.
Claims (2)
1. The neural network carbon emission prediction method based on the big data environment is characterized by comprising the following steps of:
s1, acquiring multi-channel carbon emission data through an Internet of things platform network, forming the multi-channel emission data into a corresponding emission data set, and carrying out normalization processing;
s1-1, acquiring corresponding electric power carbon emission data and gas carbon emission data in an Internet of things platform network, performing objective function calculation according to the carbon emission data,
obtaining a carbon emission objective function P (i) of the output power of the electric power in unit time and the gas power in unit time, training a network node of the electric power and the gas carbon emission through the calculated result,
wherein u is the electric power carbon emission regulating coefficient, A elec Is the power used by the electric power in unit time, v is the carbon emission regulating coefficient of the fuel gas, A gas The power is used for the electric power in unit time, and t is the unit time; obtaining pre-stored data of carbon emission data according to the network node;
s1-2, determining probability distribution of carbon emission data according to an objective function,
first, according to the electric power carbon emission data vector b= (B 1 ,b 2 ,...,b m ) T Gas carbon emission data vector c= (C) 1 ,c 2 ,...,c m ) T A probability distribution calculation is performed, wherein the superscript T is transposed,
then, the probability distribution formula obtained is:
the carbon emission probability distribution sequence number k, exp (x) is an exponential function, F (B, C) is a carbon emission energy function, beta is a target adjustment coefficient, L (k) is a distribution scale of a carbon emission target, the distribution scale can carry out distribution quantification on the data trend of carbon emission, parameter adjustment is carried out on the distribution scale through the target adjustment coefficient, and then normalization processing is carried out;
s2, measuring and calculating the offset of the carbon emission data, dynamically correcting the data, and calculating a corresponding mapping transformation function through a TCN time convolution neural network;
s2-1, carrying out scale division on the carbon emission data according to probability distribution to form an offset formula of the carbon emission data, and according to the scale formula,
E＝a·E 0 +M j lambda, wherein E is a carbon emission scale calculation expression formula, a is a carbon emission scale adjustment coefficient, E 0 For pre-stored corresponding period history carbon emission data, M j Is the real-time carbon emission data accumulated value of j weeks, lambda is the carbon emission factor,
s2-2, calculating a displacement formula of the carbon emission probability distribution through a scale formula,
D＝E+ΔE 0 according to the calculation result of the scale formula, the change value delta E of the carbon emission data is recorded through the pre-stored corresponding period history 0 Obtaining a displacement value of carbon emission data;
the offset of the carbon emission data can be obtained according to the displacement value, the offset is calculated by an offset formula,
s2-3, the offset calculated according to the offset formula of the j axis is required to be readjusted in the dynamic property and the constraint property, and the dynamic constraint is carried out through the constraint condition of the carbon emission data, so that the readjustment of the offset is achieved;
the dynamic formula of the carbon emission data is that,
G＝G j +η·P j +Z, where G j Is the energy consumption in the carbon emission data of j weeks, eta is a dynamic regulating factor, P j Total variation for j weeks of carbon emission data;
when eta is more than 0, the expression of the dynamic formula of the carbon emission data can reflect the actual numerical value of the dynamic carbon emission data, and when eta is less than or equal to 0, the dynamic formula does not relate to the total variation of the carbon emission data;
s2-4, training the TCN neural network for the real-time carbon emission data according to the obtained dynamic value of the carbon emission data, training and adjusting the dynamic value in the carbon emission process,
the Gaussian function is adopted to carry out the spatial mapping transformation on the neuron basis function in the neural network to the input carbon emission data dynamic value,
the prediction error of the neural network is derived from the mapping transform,
s3, acquiring multi-channel carbon emission data fusion through a mapping transformation function, judging a carbon emission prediction interval through a prediction accuracy model, and uploading the carbon emission prediction interval to an Internet of things platform network;
s3-1, according to a sequence formed by the dynamic values of the carbon emission data, carrying out carbon emission data statistics according to a data regression model after obtaining a prediction error, judging a carbon emission dynamic change component by actually generating values for the carbon emission data in a carbon emission data prediction interval, so that N is formed energy Can be predicted in advance; the regression model of the prediction process is f (x), a training sample is extracted from the carbon emission dynamic value, and the minimum variance standard formed in the carbon emission process is obtained for estimating the prediction precision through prediction error judgment;
s3-2, wherein the prediction precision formula is as follows
after calculation according to the prediction accuracy, a prediction confidence interval of the corresponding carbon emission data is given, and energy consumption early warning is carried out on the carbon emission data within the interval range, so that the distribution situation of the carbon emission data is predicted, and the prediction interval is adjusted in real time according to the trained accuracy value.
2. The application of the neural network carbon emission prediction method based on the big data environment is characterized by comprising the following steps:
prediction of carbon emission data for use in fossil fuel combustion, biomass combustion, petroleum and natural gas system escape, paddy methane, agricultural nitrous oxide, animal intestinal fermentation, solid waste treatment, wastewater treatment processes by the method of claim 1.
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| CN103425099B (en) * | 2013-07-16 | 2014-08-06 | 广东工业大学 | Embedded carbon emission monitoring and detecting system implemented in vulcanization procedures |
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