CN116562444A - Intelligent carbon emission adjusting method and equipment for industrial park - Google Patents
Intelligent carbon emission adjusting method and equipment for industrial park Download PDFInfo
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Abstract
The invention discloses an intelligent carbon emission regulating method and equipment for an industrial park. The data acquisition unit is used for collecting the running power of each device, the historical electricity consumption data of each device and the carbon dioxide emission of the whole industrial park; the carbon emission strategy design unit firstly predicts the carbon dioxide emission amount of each device by adopting an Elman neural network based on the data obtained by the data acquisition unit, and then carries out carbon emission adjustment strategy design based on the predicted data; the carbon emission control unit is used for adjusting carbon emission of the industrial park based on a designed adjustment strategy; the alarm unit monitors and alarms the running condition of the emission equipment; the carbon emission device performs carbon emission according to the emission instruction. The invention can regulate the carbon emission of the industrial park, avoid environmental pollution caused by overdischarge, reasonably plan the operation period of regulating equipment and maximize the environmental benefit and economic benefit.
Description
Technical Field
The invention belongs to the technical field of carbon emission regulation, and particularly relates to an intelligent carbon emission regulation method and equipment for an industrial park.
Background
The "two carbon" strategy advocates a green, environmentally friendly, low carbon lifestyle. The method accelerates the reduction of carbon emission steps, is beneficial to guiding green technical innovation and improves the global competitiveness of industry and economy. The China continuously advances the industrial structure and energy structure adjustment, the renewable energy is greatly developed, the planning and construction of large-scale wind power photovoltaic base projects are quickened in desert, gobi and desert areas, and the economic development and the green transformation are synchronously carried out.
The existing carbon emission regulating equipment predicts regional carbon dioxide emission based on historical carbon dioxide emission data, then carries out carbon dioxide emission regulation based on prediction data, and once external force influences equipment operation, prediction data are inaccurate and the actual condition of the equipment cannot be synchronized.
Another type of carbon emission regulating equipment predicts carbon dioxide emission based on regional fuel loss, the running condition of the equipment cannot be accurately predicted according to the fuel loss, the specific running condition of the equipment changes along with the actual condition, the prediction result is not accurate enough, and finally the emission equipment cannot synchronize the actual condition.
There is therefore a need for a new type of carbon emission control device that predicts carbon emissions based on specific campus device power and matches the corresponding emission device power.
Disclosure of Invention
The invention aims to: aiming at the problems pointed out in the background art, the invention provides the intelligent carbon emission regulating method and equipment for the industrial park, which are used for predicting the carbon emission based on the acquired data according to the Elman neural network model and then carrying out reasonable emission strategy design according to the prediction result.
The technical scheme is as follows: the invention provides an intelligent carbon emission adjusting method for an industrial park, which comprises the following steps:
step 1: collecting power data of carbon emission equipment operated in an industrial park, detecting carbon dioxide concentration in the industrial park, and establishing a data set comprising equipment power data, historical working condition data and historical carbon dioxide emission data of all equipment;
step 2: constructing an Elman neural network model, carrying out parameter optimization on the Elman neural network model by adopting a STOA optimization algorithm, obtaining a STOA-Elman prediction model, and predicting the carbon dioxide emission of an industrial park by taking the data detected in the step 1 as input;
step 3: and (3) designing an industrial park emission strategy according to the carbon dioxide emission predicted in the step (2) to perform power adjustment on each operating carbon emission device in the industrial park.
Further, the specific operation of predicting the carbon dioxide emission of the industrial park by using the STOA-Elman prediction model in the step 2 is as follows:
step 2.1: inputting data affecting the carbon dioxide emission of each device, and inputting the collected device power data, the historical working condition data and the carbon dioxide emission data into a prediction model;
step 2.2: preprocessing data, and carrying out normalization processing on input data;
step 2.3: dividing the data set into a training set and a testing set according to the ratio of 8:2;
step 2.4: optimizing Elman part parameters by adopting a STOA algorithm, wherein the parameters comprise initial weights and thresholds;
step 2.5: constructing an Elman neural network model using optimized parameters, and performing model training by using training set data;
step 2.6: testing the test set data by the trained Elman model, and calculating errors;
step 2.7: outputting a carbon dioxide emission prediction result of each device;
step 2.8: and (5) carrying out inverse normalization on the prediction result, and outputting final predicted carbon dioxide emission data.
Further, in the step 2.4, the parameter of the Elman part is optimized by using the STOA algorithm, including the initial weight and the threshold specifically:
step 3.1: initializing algorithm parameters including iteration times and population quantity;
step 3.2: calculating an initial weight and a threshold value, and recording the optimal initial weight and the threshold value and the corresponding individual position;
step 3.3: the gull carries out migration operation, global exploration is carried out, and the individual position is updated;
S A ＝C f -(T*(C f /Max ite ))
c st ＝S A *P st (T)
C B ＝0.5*R rand
m st ＝C B *(p bst (T)-p st (T))
d st ＝c st +m st
wherein S is A To calculate the position after collision avoidance, C f To adjust S A T represents the current iteration number, max ite For maximum number of iterations c st In the position where the ball does not collide with other gulls, P st C is the current position of Wuyangull B Is a random variable, p bst For the position of the current optimal solution, m st D, moving toward the optimal solution for Wuyangull st Updating the trajectory for the location;
step 3.4: the Uighur carries out attack operation, local exploration is carried out, and the individual position is updated;
x'＝R*sin(i)
y'＝R*cos(i)
z'＝R*i
R＝ue kv
p st (T)＝(d st *(x'+y'+z'))*p bst (T)
wherein R is the radius of each spiral, i is a variable between [0,2 pi ], and u and v are constants determining the shape of the spiral;
step 3.5: updating the initial weight and the threshold position;
step 3.6: calculating the initial weight and the threshold again, and recording the optimal initial weight and the value of the threshold and the corresponding individual position;
step 3.7: judging that the maximum iteration times are reached, if so, outputting an optimal solution, namely the optimal parameters selected by Elman, and if not, jumping to the step 3.2.
Further, the industrial park emission strategy in the step 3 specifically includes:
setting two carbon emission thresholds A, B according to the prediction result, starting the machine to operate, allowing continuous operation for a period of time if the carbon dioxide emission reaches a threshold A, and adjusting the power of the operating carbon emission equipment to reduce the carbon dioxide emission; if the carbon dioxide emission reaches the threshold B, stopping operating all the carbon emission devices, wherein the threshold A and the threshold B are predicted and adjusted according to the carbon dioxide emission:
C n ＝P n ·η n
wherein n is a device, C n Carbon emission of nth plant, P n Power, η, of the nth device n Is the carbon emission factor of the nth plant.
The invention also discloses intelligent carbon emission regulating equipment based on the industrial park, which comprises a data acquisition unit, a carbon emission strategy design unit, a carbon emission regulating unit, carbon emission equipment and an alarm unit; the data acquisition unit comprises an equipment power collection unit and a carbon dioxide sensor, and the carbon emission strategy design unit comprises a prediction module and a strategy design module; the equipment power collection module in the data acquisition unit and the carbon dioxide sensor are connected with the prediction module in the carbon emission strategy design unit; the prediction module in the carbon emission strategy design unit is connected with the strategy design module in the carbon emission strategy design unit; the strategy design module in the carbon emission strategy design unit is connected with the carbon emission regulation unit; the carbon emission adjusting unit is connected with the carbon emission device; the carbon emission device is connected with the alarm unit;
the equipment power collecting unit and the carbon dioxide sensor are respectively used for collecting power data of carbon emission equipment operated in each industrial park and carbon dioxide concentration in the industrial park;
the prediction module predicts the carbon dioxide emission of the industrial park by adopting a STOA-Elman prediction model based on the data collected by the data collection unit; the strategy design module designs an industrial park emission strategy according to the carbon dioxide emission data predicted by the prediction module;
the carbon emission regulating unit is used for regulating the power of each carbon emission device running in the industrial park according to the emission strategy of the industrial park;
the alarm unit alarms according to the carbon dioxide emission amount of the operating carbon emission device.
The beneficial effects are that:
1. according to the method, the carbon emission is predicted according to the running power of the specific equipment, so that the result is more accurate and the actual situation is more met. Setting two threshold points according to an emission strategy designed by a prediction result, reasonably expanding the running duration of specific equipment, and setting the running starting point of the emission equipment reasonably. STOA is used for optimizing weight and threshold values at the initial stage of training an Elman network, the optimal solution is applied to the Elman neural network, the Elman neural network is trained, and the effectiveness of the Elman network in predicting when the power change of equipment is large is improved.
2. The invention can regulate the carbon emission of the industrial park, avoid environmental pollution caused by overdischarge, reasonably plan and regulate the operation period of each device and maximize the environmental benefit of the industrial park.
Drawings
FIG. 1 is a schematic diagram of a hardware architecture of the present invention;
FIG. 2 is a flow chart of the STOA-Elman algorithm employed in the present invention;
FIG. 3 is a logic diagram of the operation of the present invention;
FIG. 4 is a graph comparing the operation time of the device of the present invention;
fig. 5 is a graph of carbon emissions versus the present invention.
Detailed Description
The invention is described in further detail below with reference to the accompanying drawings.
The invention discloses an intelligent carbon emission regulating method and equipment for an industrial park.
The data acquisition unit comprises an equipment power collection unit and a carbon dioxide sensor, the carbon emission strategy design unit comprises a prediction module and a strategy design module, the equipment power collection module and the carbon dioxide sensor in the data acquisition unit are connected with the prediction module in the carbon emission strategy design unit, and the prediction module in the carbon emission strategy design unit is connected with the strategy design module in the carbon emission strategy design unit. The strategy design module in the carbon emission strategy design unit is connected with the carbon emission adjustment unit. The carbon emission adjusting unit is connected with the carbon emission equipment, and the carbon emission equipment is connected with the alarm unit.
The data acquisition unit comprises an equipment power acquisition module and a carbon dioxide sensor, wherein the equipment power acquisition module is used for collecting power data of carbon emission equipment operated in each industrial park, the carbon dioxide sensor is used for detecting carbon dioxide concentration in the industrial park, and the data are transmitted to the carbon emission strategy design unit through the data acquisition unit.
The carbon emission strategy design unit comprises a prediction module and a strategy design module, wherein the prediction module predicts the carbon dioxide emission of the industrial park by adopting an Elman neural network model based on the data collected by the data collection unit. The strategy design module designs an emission strategy according to the carbon dioxide emission data predicted by the prediction module.
The Elman neural network model predicts the carbon dioxide emission based on the collected equipment power data, the historical working condition data and the historical carbon dioxide emission data, wherein the historical working condition data is the historical electricity utilization data corresponding to each equipment. Parameter optimization is carried out on an Elman neural network model by adopting a STOA optimization algorithm, the selected optimization parameters are weight and threshold of the Elman, and the specific implementation process of predicting the carbon dioxide emission by using a STOA-Elman prediction model is as follows:
step 1: and inputting data influencing the carbon dioxide emission of each device, and inputting the device electricity consumption data, the historical working condition data and the historical carbon dioxide emission data acquired by the data acquisition unit into a prediction model.
Step 2: the data preprocessing is carried out, and input data is preprocessed by normalization.
Step 3: the data set is divided into a training set and a testing set according to the ratio of 8:2.
Step 4: the STOA algorithm is adopted to optimize the Elman part parameters, which comprise initial weights and thresholds, and the specific steps are as follows:
step 4.1: the algorithm parameters are initialized, including iteration times, population numbers and the like.
Step 4.2: and calculating an initial weight and a threshold value, and recording the optimal initial weight and the threshold value and the corresponding individual position.
Step 4.3: the Uighur carries out migration operation, global exploration is carried out, and the individual position is updated:
S A ＝C f -(T*(C f /Max ite ))
c st ＝S A *P st (T)
C B ＝0.5*R rand
m st ＝C B *(p bst (T)-p st (T))
d st ＝c st +m st
wherein S is A To calculate the position after collision avoidance, C f To adjust S A T represents the current iteration number, max ite For maximum number of iterations c st In the position where the ball does not collide with other gulls, P st C is the current position of Wuyangull B Is a random variable, p bst For the position of the current optimal solution, m st D, moving toward the optimal solution for Wuyangull st The track is updated for the location.
Step 4.4: the Uighur carries out attack operation, local exploration is carried out, and the individual position is updated:
x'＝R*sin(i)
y'＝R*cos(i)
z'＝R*i
R＝ue kv
p st (T)＝(d st *(x'+y'+z'))*p bst (T)
where R is the radius of each spiral, i is a variable between [0,2 pi ], and u and v are constants that determine the shape of the spiral.
Step 4.5: the initial weight and the threshold position are updated.
Step 4.6: and calculating the initial weight and the threshold again, and recording the optimal initial weight and the value of the threshold and the corresponding individual position.
Step 4.7: judging that the maximum iteration times are reached, if so, outputting an optimal solution, namely the optimal parameters selected by Elman, and if not, jumping to the step 4.2.
Step 5: an Elman neural network model using optimized parameters is constructed, and model training is performed using training set data.
Step 6: and testing the test set data by using the trained Elman model, and calculating errors.
Step 7: and outputting a carbon dioxide emission prediction result of each device.
Step 8: and carrying out inverse normalization on the predicted result, and outputting predicted carbon dioxide emission data.
The carbon emission regulation unit carries out carbon emission regulation according to the emission strategy designed by the strategy design unit, the emission strategy designed by the strategy design unit sets two carbon emission thresholds A, B according to the prediction result, the emission regulation strategy is designed, the machine starts to operate, if the carbon dioxide emission reaches the threshold A, the operation is allowed to continue for a period of time, and the power of the equipment is regulated to reduce the carbon dioxide emission; if the carbon dioxide emission reaches the threshold B, stopping running all the machine equipment by the vertical horse, predicting and adjusting the set threshold A and B according to the carbon dioxide emission, and reasonably setting the threshold.
The carbon emission equipment is adjusted in real time according to an adjusting instruction given by an adjusting strategy designed by the carbon emission adjusting unit, and the carbon emission of the machine is reasonably controlled, so that the aim of carbon neutralization is fulfilled, and the effect of maximizing economic benefit is achieved.
And the alarm unit is used for normally operating the machine if the carbon dioxide emission does not reach the threshold A, immediately alarming if the carbon dioxide emission exceeds the threshold A and monitoring the condition of the emission equipment in real time. And if the carbon dioxide emission exceeds the threshold B, immediately alarming and issuing a machine stop operation instruction. If the carbon dioxide emission does not reach the threshold A or the threshold B, but the machine which is supposed to normally operate does not normally operate, the alarm is immediately given. And if abnormal conditions occur in the discharge equipment, immediately alarming and stopping the machine.
As shown in fig. 4, in the comparison chart of the one-day operation time of the device of the invention and other devices of the invention, the other devices of the invention refer to devices for adjusting the carbon dioxide emission based on regional fuel loss, and the carbon emission adjusting device of the invention is adopted to ensure that the operation time of each device of an industrial park is properly prolonged under the condition that the carbon emission is not out of standard, so that the overall economic benefit of the industrial park is improved.
As shown in fig. 5, in the carbon emission comparison chart of the invention and other inventions, the intelligent carbon emission regulating device of the invention enables the park to reach the carbon dioxide emission standard more slowly, reasonably plans the operation period of each device, and maximizes the environmental benefit and economic benefit of the park.
The foregoing embodiments are merely illustrative of the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the present invention and to implement the same, not to limit the scope of the present invention. All equivalent changes or modifications made according to the spirit of the present invention should be included in the scope of the present invention.
Claims (5)
1. An intelligent carbon emission adjusting method for an industrial park is characterized by comprising the following steps:
step 1: collecting power data of carbon emission equipment operated in an industrial park, detecting carbon dioxide concentration in the industrial park, and establishing a data set comprising equipment power data, historical working condition data and historical carbon dioxide emission data of all equipment;
step 2: constructing an Elman neural network model, carrying out parameter optimization on the Elman neural network model by adopting a STOA optimization algorithm, obtaining a STOA-Elman prediction model, and predicting the carbon dioxide emission of an industrial park by taking the data detected in the step 1 as input;
step 3: and (3) designing an industrial park emission strategy according to the carbon dioxide emission predicted in the step (2) to perform power adjustment on each operating carbon emission device in the industrial park.
2. The industrial park intelligent carbon emission control method according to claim 1, wherein the specific operation of predicting the carbon dioxide emission amount of the industrial park by using the STOA-Elman prediction model in the step 2 is as follows:
step 2.1: inputting data affecting the carbon dioxide emission of each device, and inputting the collected device power data, the historical working condition data and the carbon dioxide emission data into a prediction model;
step 2.2: preprocessing data, and carrying out normalization processing on input data;
step 2.3: dividing the data set into a training set and a testing set according to the ratio of 8:2;
step 2.4: optimizing Elman part parameters by adopting a STOA algorithm, wherein the parameters comprise initial weights and thresholds;
step 2.5: constructing an Elman neural network model using optimized parameters, and performing model training by using training set data;
step 2.6: testing the test set data by the trained Elman model, and calculating errors;
step 2.7: outputting a carbon dioxide emission prediction result of each device;
step 2.8: and (5) carrying out inverse normalization on the prediction result, and outputting final predicted carbon dioxide emission data.
3. The industrial park intelligent carbon emission control method according to claim 2, wherein the step 2.4 optimizes Elman part parameters by using STOA algorithm, and the method comprises the following steps:
step 3.1: initializing algorithm parameters including iteration times and population quantity;
step 3.2: calculating an initial weight and a threshold value, and recording the optimal initial weight and the threshold value and the corresponding individual position;
step 3.3: the gull carries out migration operation, global exploration is carried out, and the individual position is updated;
S A ＝C f -(T*(C f /Max ite ))
c st ＝S A *P st (T)
C B ＝0.5*R rand
m st ＝C B *(p bst (T)-p st (T))
d st ＝c st +m st
wherein S is A To calculate the position after collision avoidance, C f To adjust S A T represents the current iteration number, max ite For maximum number of iterations c st In the position where the ball does not collide with other gulls, P st C is the current position of Wuyangull B Is a random variable, p bst For the position of the current optimal solution, m st D, moving toward the optimal solution for Wuyangull st Updating the trajectory for the location;
step 3.4: the Uighur carries out attack operation, local exploration is carried out, and the individual position is updated;
x'＝R*sin(i)
y'＝R*cos(i)
z'＝R*i
R＝ue kv
p st (T)＝(d st *(x'+y'+z'))*p bst (T)
wherein R is the radius of each spiral, i is a variable between [0,2 pi ], and u and v are constants determining the shape of the spiral;
step 3.5: updating the initial weight and the threshold position;
step 3.6: calculating the initial weight and the threshold again, and recording the optimal initial weight and the value of the threshold and the corresponding individual position;
step 3.7: judging that the maximum iteration times are reached, if so, outputting an optimal solution, namely the optimal parameters selected by Elman, and if not, jumping to the step 3.2.
4. The industrial park intelligent carbon emission control method according to claim 1, wherein the industrial park emission strategy in the step 3 is specifically:
setting two carbon emission thresholds A, B according to the prediction result, starting the machine to operate, allowing continuous operation for a period of time if the carbon dioxide emission reaches a threshold A, and adjusting the power of the operating carbon emission equipment to reduce the carbon dioxide emission; if the carbon dioxide emission reaches the threshold B, stopping operating all the carbon emission devices, wherein the threshold A and the threshold B are predicted and adjusted according to the carbon dioxide emission:
C n ＝P n ·η n
wherein n is a device, C n Carbon emission of nth plant, P n Power, η, of the nth device n Is the carbon emission factor of the nth plant.
5. An industrial park intelligent carbon emission control device based on any one of claims 1 to 4, characterized by comprising a data acquisition unit (1), a carbon emission strategy design unit (2), a carbon emission control unit (3), a carbon emission device (4) and an alarm unit (5); the data acquisition unit (1) comprises a device power collection unit and a carbon dioxide sensor, and the carbon emission strategy design unit (2) comprises a prediction module and a strategy design module; the equipment power collection module and the carbon dioxide sensor in the data acquisition unit (1) are connected with the prediction module in the carbon emission strategy design unit (2); the prediction module in the carbon emission strategy design unit (2) is connected with the strategy design module in the carbon emission strategy design unit (2); the strategy design module in the carbon emission strategy design unit (2) is connected with the carbon emission regulation unit (3); the carbon emission control unit (3) is connected to the carbon emission device (4); the carbon emission device (4) is connected with the alarm unit (5);
the equipment power collecting unit and the carbon dioxide sensor are respectively used for collecting power data of carbon emission equipment operated in each industrial park and carbon dioxide concentration in the industrial park;
the prediction module predicts the carbon dioxide emission of the industrial park by adopting a STOA-Elman prediction model based on the data collected by the data collection unit (1); the strategy design module designs an industrial park emission strategy according to the carbon dioxide emission data predicted by the prediction module;
the carbon emission regulating unit (3) is used for carrying out power regulation on each carbon emission device (4) running in the industrial park according to the emission strategy of the industrial park;
the alarm unit (5) alarms according to the carbon dioxide emission of the operating carbon emission device (4).
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