CN116882574A - Carbon emission prediction method and system based on neural network model - Google Patents
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Abstract
The invention relates to a carbon emission prediction method and a system based on a neural network model, comprising the following steps: carrying out data cleaning on the historical carbon emission data of the industrial park to obtain carbon emission data after data cleaning; clustering the carbon emission data after data cleaning to obtain a plurality of clustering groups; determining the number of input layer nodes and the number of hidden layer nodes of the BP neural network according to the number of clustering groups to obtain a preset neural network model; inputting the carbon emission data subjected to data cleaning into a preset neural network model as a training sample to train to obtain a carbon emission prediction model; and determining the carbon emission amount of the target industrial park by using the carbon emission prediction model. According to the invention, the carbon emission data after data cleaning is clustered, and the number of the nodes of the input layer and the number of the nodes of the hidden layer of the BP neural network are determined based on the number of clustered groups, so that the convergence of the neural network is quickened, and the prediction precision of the carbon emission of a target industrial park can be greatly improved.
Description
Technical Field
The invention relates to the technical field of carbon emission prediction, in particular to a carbon emission prediction method and system based on a neural network model.
Background
In recent years, with the increase in global climate change, control of carbon emissions has become an important concern for governments and enterprises in various countries. In order to better develop the carbon emission reduction policy, predicting the carbon emission amount becomes an important research topic. Carbon emissions are a critical environmental issue in industrial parks, where effective control is required by scientific predictive methods. However, to make a scientific, viable emission abatement plan, it is first necessary to accurately predict future carbon emissions. Traditional carbon emission prediction methods are mainly based on experience and statistical methods and lack accuracy and reliability.
Disclosure of Invention
In order to solve the above problems, an object of an embodiment of the present invention is to provide a method and a system for predicting carbon emission based on a neural network model.
A carbon emission prediction method based on a neural network model comprises the following steps:
step 1: acquiring historical carbon emission data of an industrial park in a preset period;
step 2: performing data cleaning on the historical carbon emission data to obtain data-cleaned carbon emission data;
step 3: clustering the carbon emission data after data cleaning to obtain a plurality of clustering groups;
step 4: determining the number of input layer nodes and the number of hidden layer nodes of the BP neural network according to the number of clustering groups to obtain a preset neural network model;
step 5: inputting the carbon emission data after data cleaning into a preset neural network model as a training sample to train to obtain a carbon emission prediction model;
step 6: and determining the carbon emission amount of the target industrial park by using the carbon emission prediction model.
Preferably, the step 2: performing data cleaning on the historical carbon emission data to obtain data cleaned carbon emission data, wherein the data cleaned carbon emission data comprises:
step 2.1: calculating average value coefficients among the historical carbon emission data of the same preset period under different years;
step 2.2: judging whether the value of the mean value coefficient is in a preset range or not;
step 2.3: if the value of the mean value coefficient is not in the preset range, removing the historical carbon emission data of the corresponding preset period;
step 2.4: if the value of the mean value coefficient is in the preset range, the historical carbon emission data of the corresponding preset period is reserved until all the historical carbon emission data are traversed, and the carbon emission data after data cleaning are obtained.
Preferably, the step 2.1: calculating the mean value coefficient between the historical carbon emission data of the same preset period under different years comprises the following steps:
the formula is adopted:
calculating average value coefficients among the historical carbon emission data of the same preset period under different years; wherein p is X,Y As a mean value coefficient, cov (X, Y) represents a covariance between the historical carbon emission data of the preset period in the current year and the historical carbon emission data of the preset period in the previous year, alpha X Mean value alpha of historical carbon emission data representing preset period in current year Y Mean of historical carbon emission data representing a pre-set period of the previous year.
Preferably, the step 3: clustering the carbon emission data after data cleaning to obtain a plurality of clustering groups, including:
step 3.1: constructing an objective function according to the number of the clustering centers; the objective function is:
wherein v is i Represents the ith cluster center, m represents the blur threshold,representing data point x j Membership degree of i-th cluster center, d ij ＝||x j -v i The expression data point x j Distance from the ith cluster center;
step 3.2: solving the objective function to obtain a cluster center updating function;
step 3.3: and clustering the carbon emission data after data cleaning by using the cluster center updating function to obtain a plurality of cluster groups.
Preferably, the step 3.2: solving the objective function to obtain a cluster center updating function, comprising:
solving the objective function by using a Lagrangian multiplier method to obtain a cluster center updating function; the cluster center updating function is as follows:
wherein d kj Representing data point x j Distance to the kth cluster center.
Preferably, the step 4: determining the number of input layer nodes and the number of hidden layer nodes of the BP neural network according to the number of clustering packets to obtain a preset neural network model, wherein the method comprises the following steps:
step 4.1: keeping the number of the nodes of the input layer of the BP neural network consistent with the number of the clustering groups;
step 4.2: determining the number of hidden layer nodes by adopting an empirical formula; wherein, the empirical formula is:
wherein h represents the number of hidden layer nodes, m represents the number of input layer nodes, n represents the number of output layer nodes, and t represents the adjustment constant.
Preferably, in the step 5, the loss function in the training process is:
wherein n represents the number of training samples, θ represents the set of convolution kernel weights and offset values of a predetermined neural network model, F (Y) i The method comprises the steps of carrying out a first treatment on the surface of the θ) represents the model Y of the neural network after the preset i Is the actual output of X i Representation and Y i And a corresponding target output set.
Preferably, in the step 5, the gradient optimization model is used for performing optimization training on the loss function to obtain a carbon emission prediction model; wherein, the gradient optimization model is:
wherein delta is i Represents the convolution kernel weight, W, through the ith iteration i l Represents the bias value of the first layer neural network, eta represents the learning rate,representing the partial derivative of the loss function with the convolution kernel weights.
The invention also provides a carbon emission prediction system based on the neural network model, which comprises:
the sample acquisition module is used for acquiring historical carbon emission data of the industrial park in a preset period;
the data cleaning module is used for performing data cleaning on the historical carbon emission data to obtain data-cleaned carbon emission data;
the clustering module is used for clustering the carbon emission data after the data cleaning to obtain a plurality of clustering groups;
the neural network structure determining module is used for determining the number of input layer nodes and the number of hidden layer nodes of the BP neural network according to the number of clustering packets to obtain a preset neural network model;
the training module is used for inputting the carbon emission data after data cleaning into a preset neural network model as a training sample to train to obtain a carbon emission prediction model;
and the carbon emission prediction module is used for determining the carbon emission of the target industrial park by using the carbon emission prediction model.
The present invention also provides a computer readable storage medium having a computer program stored thereon, wherein the computer program when executed by a processor implements the steps of a neural network model-based carbon emission prediction method described above.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
compared with the prior art, the method and the system for predicting the carbon emission based on the neural network model have the advantages that the clustering is carried out on the carbon emission data after data cleaning, and the number of nodes of the input layer and the number of nodes of the hidden layer of the BP neural network are determined based on the number of clustered groups, so that the convergence of the neural network is accelerated, and the prediction precision of the carbon emission of a target industrial park can be greatly improved.
In order to make the above objects, features and advantages of the present invention more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart of a carbon emission prediction method based on a neural network model in an embodiment provided by the invention;
fig. 2 is a schematic diagram of a carbon emission prediction system based on a neural network model according to an embodiment of the present invention.
Detailed Description
In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
In the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," "secured," and the like are to be construed broadly and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
Referring to fig. 1, a carbon emission prediction method based on a neural network model includes:
step 1: acquiring historical carbon emission data of an industrial park in a preset period;
step 2: performing data cleaning on the historical carbon emission data to obtain data-cleaned carbon emission data;
further, step 2 includes:
step 2.1: calculating average value coefficients among the historical carbon emission data of the same preset period under different years; in the invention, the mean value coefficient calculation formula is:
wherein p is X,Y As a mean value coefficient, cov (X, Y) represents a covariance between the historical carbon emission data of the preset period in the current year and the historical carbon emission data of the preset period in the previous year, alpha X Mean value alpha of historical carbon emission data representing preset period in current year Y Mean of historical carbon emission data representing a pre-set period of the previous year.
Step 2.2: judging whether the value of the mean value coefficient is in a preset range or not;
step 2.3: if the value of the mean value coefficient is not in the preset range, removing the historical carbon emission data of the corresponding preset period;
step 2.4: if the value of the mean value coefficient is in the preset range, the historical carbon emission data of the corresponding preset period is reserved until all the historical carbon emission data are traversed, and the carbon emission data after data cleaning are obtained.
According to the method, a mean coefficient calculation formula is built through covariance and mean, and then historical carbon emission data which does not meet requirements can be removed based on the mean coefficient calculation formula, so that the authenticity of the data is guaranteed.
Step 3: clustering the carbon emission data after data cleaning to obtain a plurality of clustering groups;
further, the step 3 includes:
step 3.1: constructing an objective function according to the number of the clustering centers; the objective function is:
wherein v is i Represents the ith cluster center, m represents the blur threshold,representing data point x j Membership degree of i-th cluster center, d ij ＝||x j -v i The expression data point x j Distance from the ith cluster center;
step 3.2: solving the objective function to obtain a cluster center updating function;
specifically, in the present invention, step 3.2 may be: solving the objective function by using a Lagrangian multiplier method to obtain a cluster center updating function; the cluster center updating function is as follows:
wherein d kj Representing data point x j Distance to the kth cluster center.
Step 3.3: and clustering the carbon emission data after data cleaning by using the cluster center updating function to obtain a plurality of cluster groups.
Step 4: determining the number of input layer nodes and the number of hidden layer nodes of the BP neural network according to the number of clustering groups to obtain a preset neural network model;
the BP neural network (BP) algorithm is a learning method based on mathematical and statistical types, has stronger learning capacity, and can carry out model supervision through error reverse transfer. The BP neural network is composed of an input layer, one or more hidden layers and an output layer. In general, the input layer is the input of the model, determined by the vector dimensions of the input data. The invention utilizes the quantity of the clustering groups to quantify the dimension of the input data, and can greatly improve the training precision of the neural network.
Further, step 4 includes:
step 4.1: keeping the number of the nodes of the input layer of the BP neural network consistent with the number of the clustering groups;
step 4.2: determining the number of hidden layer nodes by adopting an empirical formula; wherein, the empirical formula is:
wherein h represents the number of hidden layer nodes, m represents the number of input layer nodes, n represents the number of output layer nodes, and t represents the adjustment constant.
When the number of hidden layer neurons is increased, overfitting is easy to cause, namely the training error of the model is reduced, and the test error of the model is increased, so that the hidden layer node number is determined by adopting an empirical formula, and the overfitting phenomenon can be avoided as much as possible.
Step 5: inputting the carbon emission data after data cleaning into a preset neural network model as a training sample to train to obtain a carbon emission prediction model;
in the step 5, the loss function in the training process is as follows:
wherein n represents the number of training samples, θ represents the set of convolution kernel weights and offset values of a predetermined neural network model, F (Y) i The method comprises the steps of carrying out a first treatment on the surface of the θ) represents the model Y of the neural network after the preset i Is the actual output of X i Representation and Y i And a corresponding target output set.
In practical application, the invention utilizes a gradient optimization model to carry out optimization training on the loss function to obtain a carbon emission prediction model; wherein, the gradient optimization model is:
wherein delta is i Represents the convolution kernel weight, W, through the ith iteration i l Represents the bias value of the first layer neural network, eta represents the learning rate,representing the partial derivative of the loss function with the convolution kernel weights.
Step 6: and determining the carbon emission amount of the target industrial park by using the carbon emission prediction model.
According to the invention, the BP neural network fitting time and the mapping relation between the carbon emission of the target industrial park are utilized, and the gradient optimization model is utilized to optimize the loss function, so that the convergence speed of the neural network can be accelerated to the greatest extent, and the prediction precision of the carbon emission of the target industrial park is improved.
Referring to fig. 2, the present invention further provides a carbon emission prediction system based on a neural network model, including:
the sample acquisition module is used for acquiring historical carbon emission data of the industrial park in a preset period;
the data cleaning module is used for performing data cleaning on the historical carbon emission data to obtain data-cleaned carbon emission data;
the clustering module is used for clustering the carbon emission data after the data cleaning to obtain a plurality of clustering groups;
the neural network structure determining module is used for determining the number of input layer nodes and the number of hidden layer nodes of the BP neural network according to the number of clustering packets to obtain a preset neural network model;
the training module is used for inputting the carbon emission data after data cleaning into a preset neural network model as a training sample to train to obtain a carbon emission prediction model;
and the carbon emission prediction module is used for determining the carbon emission of the target industrial park by using the carbon emission prediction model.
Compared with the prior art, the carbon emission prediction system based on the neural network model has the same beneficial effects as the carbon emission prediction method based on the neural network model in the technical scheme, and the description is omitted here.
The present invention also provides a computer readable storage medium having a computer program stored thereon, wherein the computer program when executed by a processor implements the steps of a neural network model-based carbon emission prediction method described above.
Compared with the prior art, the beneficial effects of the computer readable storage medium provided by the invention are the same as those of the carbon emission prediction method based on the neural network model described in the technical scheme, and are not repeated here.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art can easily think about variations or alternatives within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A neural network model-based carbon emission prediction method, comprising:
step 1: acquiring historical carbon emission data of an industrial park in a preset period;
step 2: performing data cleaning on the historical carbon emission data to obtain data-cleaned carbon emission data;
step 3: clustering the carbon emission data after data cleaning to obtain a plurality of clustering groups;
step 4: determining the number of input layer nodes and the number of hidden layer nodes of the BP neural network according to the number of clustering groups to obtain a preset neural network model;
step 5: inputting the carbon emission data after data cleaning into a preset neural network model as a training sample to train to obtain a carbon emission prediction model;
step 6: and determining the carbon emission amount of the target industrial park by using the carbon emission prediction model.
2. The method for predicting carbon emissions based on neural network model according to claim 1, wherein the step 2: performing data cleaning on the historical carbon emission data to obtain data cleaned carbon emission data, wherein the data cleaned carbon emission data comprises:
step 2.1: calculating average value coefficients among the historical carbon emission data of the same preset period under different years;
step 2.2: judging whether the value of the mean value coefficient is in a preset range or not;
step 2.3: if the value of the mean value coefficient is not in the preset range, removing the historical carbon emission data of the corresponding preset period;
step 2.4: if the value of the mean value coefficient is in the preset range, the historical carbon emission data of the corresponding preset period is reserved until all the historical carbon emission data are traversed, and the carbon emission data after data cleaning are obtained.
3. The method for predicting carbon emissions based on neural network model according to claim 2, wherein the step 2.1: calculating the mean value coefficient between the historical carbon emission data of the same preset period under different years comprises the following steps:
the formula is adopted:
calculating average value coefficients among the historical carbon emission data of the same preset period under different years; wherein p is X,Y As a mean value coefficient, cov (X, Y) represents a covariance between the historical carbon emission data of the preset period in the current year and the historical carbon emission data of the preset period in the previous year, alpha X Mean value alpha of historical carbon emission data representing preset period in current year Y Mean of historical carbon emission data representing a pre-set period of the previous year.
4. A method for predicting carbon emissions based on a neural network model as claimed in claim 3, wherein said step 3: clustering the carbon emission data after data cleaning to obtain a plurality of clustering groups, including:
step 3.1: constructing an objective function according to the number of the clustering centers; the objective function is:
wherein v is i Represents the ith cluster center, m represents the blur threshold,representing data point x j Membership degree of i-th cluster center, d ij ＝||x j -v i The expression data point x j Distance from the ith cluster center;
step 3.2: solving the objective function to obtain a cluster center updating function;
step 3.3: and clustering the carbon emission data after data cleaning by using the cluster center updating function to obtain a plurality of cluster groups.
5. The method for predicting carbon emissions based on neural network model of claim 4, wherein said step 3.2: solving the objective function to obtain a cluster center updating function, comprising:
solving the objective function by using a Lagrangian multiplier method to obtain a cluster center updating function; the cluster center updating function is as follows:
wherein d kj Representing data point x j Distance to the kth cluster center.
6. The method for predicting carbon emissions based on neural network model of claim 5, wherein said step 4: determining the number of input layer nodes and the number of hidden layer nodes of the BP neural network according to the number of clustering packets to obtain a preset neural network model, wherein the method comprises the following steps:
step 4.1: keeping the number of the nodes of the input layer of the BP neural network consistent with the number of the clustering groups;
step 4.2: determining the number of hidden layer nodes by adopting an empirical formula; wherein, the empirical formula is:
wherein h represents the number of hidden layer nodes, m represents the number of input layer nodes, n represents the number of output layer nodes, and t represents the adjustment constant.
7. The method for predicting carbon emissions based on neural network model of claim 6, wherein in step 5, the loss function during training is:
wherein n represents the number of training samples, θ represents the set of convolution kernel weights and offset values of a predetermined neural network model, F (Y) i The method comprises the steps of carrying out a first treatment on the surface of the θ) represents the model Y of the neural network after the preset i Is the actual output of X i Representation and Y i And a corresponding target output set.
8. The method for predicting carbon emissions based on a neural network model according to claim 7, wherein in the step 5, the loss function is optimally trained by using a gradient optimization model to obtain a carbon emission prediction model; wherein, the gradient optimization model is:
wherein delta is i Represents the convolution kernel weight, W, through the ith iteration i l Represents the bias value of the first layer neural network, eta represents the learning rate,representing the partial derivative of the loss function with the convolution kernel weights.
9. A neural network model-based carbon emission prediction system, comprising:
the sample acquisition module is used for acquiring historical carbon emission data of the industrial park in a preset period;
the data cleaning module is used for performing data cleaning on the historical carbon emission data to obtain data-cleaned carbon emission data;
the clustering module is used for clustering the carbon emission data after the data cleaning to obtain a plurality of clustering groups;
the neural network structure determining module is used for determining the number of input layer nodes and the number of hidden layer nodes of the BP neural network according to the number of clustering packets to obtain a preset neural network model;
the training module is used for inputting the carbon emission data after data cleaning into a preset neural network model as a training sample to train to obtain a carbon emission prediction model;
and the carbon emission prediction module is used for determining the carbon emission of the target industrial park by using the carbon emission prediction model.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of a neural network model-based carbon emission prediction method as claimed in any one of claims 1-8.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117408394A (en) * | 2023-12-14 | 2024-01-16 | 国网天津市电力公司电力科学研究院 | Carbon emission factor prediction method and device for electric power system and electronic equipment |
| CN117742160A (en) * | 2024-02-09 | 2024-03-22 | 广州市威士丹利智能科技有限公司 | Artificial intelligence-based carbon emission optimization control method and system |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2022077587A1 (en) * | 2020-10-14 | 2022-04-21 | 深圳大学 | Data prediction method and apparatus, and terminal device |
| CN115063252A (en) * | 2022-06-08 | 2022-09-16 | 山东省农业科学院 | Crop fertilizer accurate application method and system based on neural network |
| CN115860188A (en) * | 2022-11-11 | 2023-03-28 | 国网福建省电力有限公司营销服务中心 | Carbon emission prediction method and system, medium and electronic device |
| CN116094068A (en) * | 2023-03-21 | 2023-05-09 | 国网福建省电力有限公司 | Power grid dispatching method, equipment and medium based on carbon emission prediction mechanism |
-
2023
- 2023-07-17 CN CN202310873522.1A patent/CN116882574A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2022077587A1 (en) * | 2020-10-14 | 2022-04-21 | 深圳大学 | Data prediction method and apparatus, and terminal device |
| CN115063252A (en) * | 2022-06-08 | 2022-09-16 | 山东省农业科学院 | Crop fertilizer accurate application method and system based on neural network |
| CN115860188A (en) * | 2022-11-11 | 2023-03-28 | 国网福建省电力有限公司营销服务中心 | Carbon emission prediction method and system, medium and electronic device |
| CN116094068A (en) * | 2023-03-21 | 2023-05-09 | 国网福建省电力有限公司 | Power grid dispatching method, equipment and medium based on carbon emission prediction mechanism |
Non-Patent Citations (1)
| Title |
|---|
| 薛义增等: "基于深度学习的人工智能机器探索与实践", 31 August 2021, 中国原子能出版社, pages: 144 - 145 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117408394A (en) * | 2023-12-14 | 2024-01-16 | 国网天津市电力公司电力科学研究院 | Carbon emission factor prediction method and device for electric power system and electronic equipment |
| CN117408394B (en) * | 2023-12-14 | 2024-05-31 | 国网天津市电力公司电力科学研究院 | Carbon emission factor prediction method and device for electric power system and electronic equipment |
| CN117742160A (en) * | 2024-02-09 | 2024-03-22 | 广州市威士丹利智能科技有限公司 | Artificial intelligence-based carbon emission optimization control method and system |
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