CN117371765B - Comprehensive optimization operation method and system based on energy-saving carbon-reduction intelligent energy - Google Patents
Comprehensive optimization operation method and system based on energy-saving carbon-reduction intelligent energy Download PDFInfo
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Abstract
The invention relates to the technical field of intelligent energy optimization, in particular to a comprehensive optimization operation method and system based on energy-saving and carbon-reduction intelligent energy. The method comprises the following steps: carrying out data monitoring, acquisition and processing and potential influence detection analysis on the intelligent energy system to obtain energy-saving potential influence factors and carbon reduction potential influence factors; performing allocation optimization adjustment according to the energy-saving potential influence factors, and performing carbon reduction optimization adjustment according to the carbon reduction potential influence factors to obtain energy-saving allocation optimization data and energy carbon reduction optimization data; performing characteristic association analysis on the energy conservation allocation optimization data and the energy carbon reduction optimization data to establish an energy comprehensive optimization operation model; performing energy optimization prediction processing on the intelligent energy operation data through an energy comprehensive optimization operation model to obtain an energy optimization operation prediction result; and executing a corresponding comprehensive optimization operation strategy according to the energy optimization operation prediction result. The invention can realize comprehensive optimization of energy conservation and carbon reduction.
Description
Technical Field
The invention relates to the technical field of intelligent energy optimization, in particular to a comprehensive optimization operation method and system based on energy-saving and carbon-reduction intelligent energy.
Background
With the development of society and the increasing demand for energy, energy use efficiency and carbon emission have become global concerns. The traditional intelligent energy system has the problems of energy waste, high carbon emission and the like, and needs comprehensive optimization operation to realize energy conservation and carbon reduction. The existing intelligent energy system is usually focused on monitoring and data acquisition, and lacks of deep comprehensive optimization for energy conservation and carbon reduction.
Disclosure of Invention
Based on the above, the present invention is needed to provide a comprehensive optimization operation method based on energy-saving and carbon-reducing intelligent energy, so as to solve at least one of the above technical problems.
In order to achieve the purpose, the comprehensive optimization operation method based on the energy-saving carbon reduction intelligent energy source comprises the following steps:
step S1: performing data monitoring and acquisition processing on the intelligent energy system to obtain intelligent energy operation data; performing potential influence detection analysis on the intelligent energy operation data to obtain energy-saving potential influence factors and carbon reduction potential influence factors;
step S2: performing energy use monitoring analysis on the intelligent energy operation data to obtain energy use condition data; performing energy conservation allocation analysis on the energy use condition data to obtain energy conservation allocation initial data; performing allocation optimization adjustment on the energy-saving allocation initial data according to the energy-saving potential influence factors to obtain energy-saving allocation optimization data;
Step S3: performing carbon emission evaluation analysis on the intelligent energy operation data to obtain carbon emission level evaluation data; performing energy carbon reduction treatment on the carbon emission level evaluation data to obtain energy carbon reduction initial data; performing carbon reduction optimization adjustment on the energy carbon reduction initial data according to the potential influence factors of carbon reduction to obtain energy carbon reduction optimization data;
step S4: performing feature association analysis on the energy conservation allocation optimization data and the energy carbon reduction optimization data to obtain energy conservation carbon reduction optimization features; establishing an energy comprehensive optimization operation model according to the energy-saving carbon reduction optimization characteristics;
step S5: performing energy optimization prediction processing on the intelligent energy operation data through an energy comprehensive optimization operation model to obtain an energy optimization operation prediction result; and executing a corresponding comprehensive optimization operation strategy according to the energy optimization operation prediction result.
According to the intelligent energy system, the intelligent energy system is subjected to data monitoring and acquisition processing to obtain the operation data of the intelligent energy system, so that key information such as energy use condition, efficiency and carbon emission of the intelligent energy system can be known in real time. Subsequently, a potential impact detection analysis is performed on the data, from which potential energy savings and carbon emission reduction opportunities can be identified, the effect of this step being to provide data support, to help system administrators and engineers better understand the current state of the system, and to realize potential performance improvement room. Secondly, by performing energy usage monitoring analysis on the intelligent energy operation data, deep knowledge of actual energy usage can be provided, which helps identify areas of energy waste and inefficiency. And energy conservation allocation analysis is carried out on the energy use condition data obtained by analysis, and the generated energy conservation allocation initial data can provide guidance for system operation so as to optimize resource allocation and reduce energy consumption. Meanwhile, the energy-saving allocation initial data is optimized and adjusted by considering the energy-saving potential influence factors so as to obtain a more effective energy-saving allocation strategy, so that the energy efficiency of the system is improved, the energy cost is reduced, and the energy-saving allocation strategy is a key advantage of system operation. Then, by performing carbon emission assessment analysis on the intelligent energy operation data, important information on the system environmental impact can be provided, which helps the system manager to know the carbon emission level of the system, thereby following sustainability and environmental goals. And by subjecting the carbon emission level evaluation data to energy carbon reduction processing, the generated energy carbon reduction initial data can provide a starting point for reducing carbon emissions. By considering potential influence factors of carbon reduction, the generated energy carbon reduction initial data are adjusted and optimized to formulate a more targeted carbon emission reduction strategy, so that carbon emission is reduced more effectively. And then, by carrying out characteristic association analysis on the energy conservation allocation optimization data and the energy carbon reduction optimization data, key characteristics and correlations of energy conservation and carbon emission reduction optimization are determined, which is beneficial to establishing a comprehensive optimization operation model, and the model can comprehensively consider a plurality of factors including energy efficiency, carbon emission, cost and the like so as to realize comprehensive optimization operation of the system. Through the model, a system manager can make a more strategic and comprehensive decision so as to realize long-term benefit and improve the overall performance of the system. Finally, the intelligent energy operation data is subjected to energy optimization prediction processing through the established comprehensive energy optimization operation model, so that a prediction result of future optimization operation of the intelligent energy system can be provided, the performance of the system can be planned and predicted in advance, and the system can meet changing requirements and environments more responsively and flexibly. According to the prediction results, a corresponding comprehensive optimization operation strategy can be executed so as to realize maximum energy conservation and carbon emission reduction to reduce benefits, reduce operation cost and improve the sustainability of the system, thereby realizing deep comprehensive optimization of energy conservation and carbon reduction.
Preferably, the invention also provides a comprehensive optimization operation system based on the energy-saving and carbon-reduction intelligent energy, which is used for executing the comprehensive optimization operation method based on the energy-saving and carbon-reduction intelligent energy, and comprises the following steps:
the influence factor detection and analysis module is used for carrying out data monitoring, acquisition and processing on the intelligent energy system to obtain intelligent energy operation data; performing potential influence detection analysis on the intelligent energy operation data so as to obtain energy-saving potential influence factors and carbon reduction potential influence factors;
the energy-saving optimization adjustment module is used for carrying out energy use monitoring analysis on the intelligent energy operation data to obtain energy use condition data; performing energy conservation allocation analysis on the energy use condition data to obtain energy conservation allocation initial data; performing allocation optimization adjustment on the energy-saving allocation initial data according to the energy-saving potential influence factors to obtain energy-saving allocation optimization data;
the carbon reduction optimization adjustment module is used for carrying out carbon emission evaluation analysis on the intelligent energy operation data to obtain carbon emission level evaluation data; performing energy carbon reduction treatment on the carbon emission level evaluation data to obtain energy carbon reduction initial data; performing carbon reduction optimization adjustment on the energy carbon reduction initial data according to the potential influence factors of carbon reduction to obtain energy carbon reduction optimization data;
The comprehensive optimization model construction module is used for carrying out feature association analysis on the energy conservation allocation optimization data and the energy carbon reduction optimization data to obtain energy conservation carbon reduction optimization features; establishing an energy comprehensive optimization operation model according to the energy-saving carbon reduction optimization characteristics;
the model prediction processing module is used for carrying out energy optimization prediction processing on the intelligent energy operation data through the energy comprehensive optimization operation model so as to obtain an energy optimization operation prediction result; and executing a corresponding comprehensive optimization operation strategy according to the energy optimization operation prediction result.
In summary, the invention provides a comprehensive optimization operation system based on energy-saving carbon reduction smart energy, which consists of an influence factor detection analysis module, an energy-saving optimization adjustment module, a carbon reduction optimization adjustment module, a comprehensive optimization model construction module and a model prediction processing module, and can realize any comprehensive optimization operation method based on energy-saving carbon reduction smart energy.
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Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of a non-limiting implementation, made with reference to the accompanying drawings in which:
FIG. 1 is a schematic flow chart of steps of a comprehensive optimization operation method based on energy-saving and carbon-reduction intelligent energy sources;
FIG. 2 is a detailed step flow chart of step S1 in FIG. 1;
fig. 3 is a detailed step flow chart of step S15 in fig. 2.
Detailed Description
The following is a clear and complete description of the technical method of the present patent in conjunction with the accompanying drawings, and it is evident that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, are intended to fall within the scope of the present invention.
Furthermore, the drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. The functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor methods and/or microcontroller methods.
It will be understood that, although the terms "first," "second," etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of example embodiments. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
In order to achieve the above objective, referring to fig. 1 to 3, the present invention provides a comprehensive optimization operation method based on energy-saving and carbon-reducing intelligent energy, which comprises the following steps:
step S1: performing data monitoring and acquisition processing on the intelligent energy system to obtain intelligent energy operation data; performing potential influence detection analysis on the intelligent energy operation data to obtain energy-saving potential influence factors and carbon reduction potential influence factors;
step S2: performing energy use monitoring analysis on the intelligent energy operation data to obtain energy use condition data; performing energy conservation allocation analysis on the energy use condition data to obtain energy conservation allocation initial data; performing allocation optimization adjustment on the energy-saving allocation initial data according to the energy-saving potential influence factors to obtain energy-saving allocation optimization data;
Step S3: performing carbon emission evaluation analysis on the intelligent energy operation data to obtain carbon emission level evaluation data; performing energy carbon reduction treatment on the carbon emission level evaluation data to obtain energy carbon reduction initial data; performing carbon reduction optimization adjustment on the energy carbon reduction initial data according to the potential influence factors of carbon reduction to obtain energy carbon reduction optimization data;
step S4: performing feature association analysis on the energy conservation allocation optimization data and the energy carbon reduction optimization data to obtain energy conservation carbon reduction optimization features; establishing an energy comprehensive optimization operation model according to the energy-saving carbon reduction optimization characteristics;
step S5: performing energy optimization prediction processing on the intelligent energy operation data through an energy comprehensive optimization operation model to obtain an energy optimization operation prediction result; and executing a corresponding comprehensive optimization operation strategy according to the energy optimization operation prediction result.
In the embodiment of the present invention, please refer to fig. 1, which is a schematic diagram illustrating steps of a comprehensive optimization operation method based on an energy-saving and carbon-reduction smart energy source according to the present invention, in this example, the steps of the comprehensive optimization operation method based on the energy-saving and carbon-reduction smart energy source include:
step S1: performing data monitoring and acquisition processing on the intelligent energy system to obtain intelligent energy operation data; performing potential influence detection analysis on the intelligent energy operation data to obtain energy-saving potential influence factors and carbon reduction potential influence factors;
According to the embodiment of the invention, the intelligent energy system is monitored and processed through the integrated multi-mode sensor (such as energy production, transmission, storage and other sensors) so as to monitor various types of data in the intelligent energy system, such as energy consumption, environmental conditions, equipment states and other data, and the data obtained from different sensors are integrated into a unified data set, so that intelligent energy operation data are obtained. And then, analyzing the intelligent energy operation data by using a data mining and data analysis model to explore influence factors on energy consumption and carbon emission in the intelligent energy operation data, and finally obtaining the energy-saving potential influence factors and the carbon reduction potential influence factors.
Step S2: performing energy use monitoring analysis on the intelligent energy operation data to obtain energy use condition data; performing energy conservation allocation analysis on the energy use condition data to obtain energy conservation allocation initial data; performing allocation optimization adjustment on the energy-saving allocation initial data according to the energy-saving potential influence factors to obtain energy-saving allocation optimization data;
the embodiment of the invention visualizes intelligent energy operation data into a flow chart of intelligent energy operation by using data visualizes tools such as charts, graphs and flow charts so as to display the flow path and key nodes of the energy, analyzes and identifies the energy behavior patterns, the flow trend and key characteristics in the intelligent energy operation flow chart, including normal operation, high load time periods, abnormal events and the like by analyzing and identifying the drawn flow chart, automatically identifies and classifies different behavior patterns by using a pattern identification algorithm, and determines the actual energy consumption condition, thereby obtaining the energy use condition data. Then, a load optimization strategy is formulated by analyzing the energy use condition data, including strategies such as energy transfer, equipment adjustment, operation scheduling and the like, and relevant configuration of the energy use condition data is adjusted according to the load optimization strategy, so that energy waste is reduced, the energy use efficiency of an intelligent energy system is improved, and energy-saving allocation initial data is obtained. And finally, performing further allocation optimization analysis on the energy-saving allocation initial data by using the energy-saving potential influence factors to analyze and acquire a better energy optimal allocation strategy, and performing energy-saving allocation optimization and adjustment on the energy-saving allocation initial data according to the energy optimal allocation strategy to finally obtain energy-saving allocation optimization data.
Step S3: performing carbon emission evaluation analysis on the intelligent energy operation data to obtain carbon emission level evaluation data; performing energy carbon reduction treatment on the carbon emission level evaluation data to obtain energy carbon reduction initial data; performing carbon reduction optimization adjustment on the energy carbon reduction initial data according to the potential influence factors of carbon reduction to obtain energy carbon reduction optimization data;
according to the embodiment of the invention, the carbon emission monitoring equipment is used for monitoring emission data in the intelligent energy operation data so as to monitor and evaluate the carbon emission level condition of the intelligent energy system, so that carbon emission level evaluation data is obtained. And then, determining a carbon emission level target and a carbon emission requirement, namely the carbon emission level expected to be achieved, according to the carbon emission level target and the carbon emission requirement, formulating a corresponding carbon reduction strategy according to the carbon emission target and the carbon emission requirement, including reducing energy consumption, adopting a low-carbon technology and the like, and performing carbon reduction treatment on the carbon emission level evaluation data according to the carbon reduction strategy so as to reduce carbon emission of the intelligent energy system, thereby obtaining energy carbon reduction initial data. Finally, analyzing the energy carbon reduction initial data by using the carbon reduction potential influence factors to further analyze the optimization opportunities of the energy carbon reduction initial data according to various factors such as technical improvement, policy formulation, economic incentive and the like, and formulating corresponding optimization strategies according to the carbon reduction optimization opportunities obtained by analysis to correspondingly adjust the energy carbon reduction initial data so as to further reduce carbon emission, and finally obtaining the energy carbon reduction optimization data.
Step S4: performing feature association analysis on the energy conservation allocation optimization data and the energy carbon reduction optimization data to obtain energy conservation carbon reduction optimization features; establishing an energy comprehensive optimization operation model according to the energy-saving carbon reduction optimization characteristics;
according to the embodiment of the invention, firstly, the energy conservation allocation optimization data and the energy carbon reduction optimization data are cleaned to process missing values, abnormal values and repeated data, the cleaned data are processed by using a time-frequency domain analysis method, characteristic information related to the energy conservation optimization and the carbon reduction optimization, including statistical characteristics, time sequence characteristics, frequency domain characteristics and the like, is extracted, and then the related characteristics are combined into a new characteristic by using methods of weighted average, PCA (principal component analysis) and the like, so that the energy conservation carbon reduction optimization characteristic is obtained. Finally, by following a preset division ratio 7:2: and 1, dividing the energy-saving carbon reduction optimization features into a training set, a verification set and a test set, and performing model training, verification and test by using a random forest algorithm according to the division result to construct a model capable of accurately realizing comprehensive energy optimization, so as to finally obtain a comprehensive energy optimization operation model.
Step S5: performing energy optimization prediction processing on the intelligent energy operation data through an energy comprehensive optimization operation model to obtain an energy optimization operation prediction result; and executing a corresponding comprehensive optimization operation strategy according to the energy optimization operation prediction result.
According to the embodiment of the invention, the intelligent energy operation data is predicted by using the established comprehensive energy optimization operation model to predict the future optimization operation result of the intelligent energy system, so that the energy optimization operation prediction result is obtained, and finally, the corresponding comprehensive optimization operation strategy (comprising the energy consumption optimization operation strategy and the carbon emission optimization operation strategy) is executed according to the energy optimization operation prediction result, so that the aims of energy conservation and carbon emission reduction are fulfilled.
According to the intelligent energy system, the intelligent energy system is subjected to data monitoring and acquisition processing to obtain the operation data of the intelligent energy system, so that key information such as energy use condition, efficiency and carbon emission of the intelligent energy system can be known in real time. Subsequently, a potential impact detection analysis is performed on the data, from which potential energy savings and carbon emission reduction opportunities can be identified, the effect of this step being to provide data support, to help system administrators and engineers better understand the current state of the system, and to realize potential performance improvement room. Secondly, by performing energy usage monitoring analysis on the intelligent energy operation data, deep knowledge of actual energy usage can be provided, which helps identify areas of energy waste and inefficiency. And energy conservation allocation analysis is carried out on the energy use condition data obtained by analysis, and the generated energy conservation allocation initial data can provide guidance for system operation so as to optimize resource allocation and reduce energy consumption. Meanwhile, the energy-saving allocation initial data is optimized and adjusted by considering the energy-saving potential influence factors so as to obtain a more effective energy-saving allocation strategy, so that the energy efficiency of the system is improved, the energy cost is reduced, and the energy-saving allocation strategy is a key advantage of system operation. Then, by performing carbon emission assessment analysis on the intelligent energy operation data, important information on the system environmental impact can be provided, which helps the system manager to know the carbon emission level of the system, thereby following sustainability and environmental goals. And by subjecting the carbon emission level evaluation data to energy carbon reduction processing, the generated energy carbon reduction initial data can provide a starting point for reducing carbon emissions. By considering potential influence factors of carbon reduction, the generated energy carbon reduction initial data are adjusted and optimized to formulate a more targeted carbon emission reduction strategy, so that carbon emission is reduced more effectively. And then, by carrying out characteristic association analysis on the energy conservation allocation optimization data and the energy carbon reduction optimization data, key characteristics and correlations of energy conservation and carbon emission reduction optimization are determined, which is beneficial to establishing a comprehensive optimization operation model, and the model can comprehensively consider a plurality of factors including energy efficiency, carbon emission, cost and the like so as to realize comprehensive optimization operation of the system. Through the model, a system manager can make a more strategic and comprehensive decision so as to realize long-term benefit and improve the overall performance of the system. Finally, the intelligent energy operation data is subjected to energy optimization prediction processing through the established comprehensive energy optimization operation model, so that a prediction result of future optimization operation of the intelligent energy system can be provided, the performance of the system can be planned and predicted in advance, and the system can meet changing requirements and environments more responsively and flexibly. According to the prediction results, a corresponding comprehensive optimization operation strategy can be executed so as to realize maximum energy conservation and carbon emission reduction to reduce benefits, reduce operation cost and improve the sustainability of the system, thereby realizing deep comprehensive optimization of energy conservation and carbon reduction.
Preferably, step S1 comprises the steps of:
step S11: the intelligent energy system is subjected to data monitoring fusion processing through an integrated multi-mode sensor to obtain intelligent energy initial information data;
step S12: establishing a distributed data acquisition network by using a block chain technology, and performing distributed acquisition processing on intelligent energy initial information data by using the distributed data acquisition network to obtain intelligent energy distributed operation data;
step S13: the intelligent energy distribution operation data are obtained by deploying edge calculation nodes in a distributed data acquisition network and carrying out real-time data preprocessing on the intelligent energy distribution operation data in the edge calculation nodes;
step S14: carrying out influence factor mining analysis on the intelligent energy operation data to obtain intelligent energy influence factors;
step S15: performing energy-saving influence detection analysis on intelligent energy operation data by utilizing intelligent energy influence factors to obtain energy-saving potential influence factors;
step S16: and performing carbon reduction influence detection analysis on the intelligent energy operation data by utilizing the intelligent energy influence factors to obtain the carbon reduction potential influence factors.
As an embodiment of the present invention, referring to fig. 2, a detailed step flow chart of step S1 in fig. 1 is shown, in which step S1 includes the following steps:
Step S11: the intelligent energy system is subjected to data monitoring fusion processing through an integrated multi-mode sensor to obtain intelligent energy initial information data;
according to the embodiment of the invention, the intelligent energy system is monitored and processed through the integrated multi-mode sensor (such as energy production, transmission, storage and other sensors) so as to monitor various types of data in the intelligent energy system, such as energy consumption, environmental conditions, equipment states and other data, and the data obtained from different sensors are integrated into a unified data set so as to create initial information data of the intelligent energy system, and finally the initial information data of the intelligent energy system is obtained.
Step S12: establishing a distributed data acquisition network by using a block chain technology, and performing distributed acquisition processing on intelligent energy initial information data by using the distributed data acquisition network to obtain intelligent energy distributed operation data;
the embodiment of the invention deploys and configures a distributed data acquisition network by using a blockchain technology, comprising the steps of creating blockchain nodes and intelligent contracts, ensuring the safety and the non-falsifiability of data, and then uploading intelligent energy initial information data to the distributed data acquisition network for distributed acquisition of the data so as to acquire relevant intelligent energy operation data, and finally obtaining intelligent energy distributed operation data.
Step S13: the intelligent energy distribution operation data are obtained by deploying edge calculation nodes in a distributed data acquisition network and carrying out real-time data preprocessing on the intelligent energy distribution operation data in the edge calculation nodes;
according to the embodiment of the invention, the edge computing nodes are deployed at proper positions in the distributed data acquisition network, and then the acquired intelligent energy distributed operation data are processed in real time in the edge computing nodes, wherein the operations comprise data cleaning, data conversion, data aggregation and the like, so that the intelligent energy operation data are finally obtained.
Step S14: carrying out influence factor mining analysis on the intelligent energy operation data to obtain intelligent energy influence factors;
according to the embodiment of the invention, the intelligent energy operation data is analyzed by using the data mining and data analysis model, so that the factors influencing the intelligent energy operation in the intelligent energy operation data are explored, and finally the intelligent energy influence factors are obtained.
Step S15: performing energy-saving influence detection analysis on intelligent energy operation data by utilizing intelligent energy influence factors to obtain energy-saving potential influence factors;
according to the embodiment of the invention, the intelligent energy operation data is analyzed by using the intelligent energy influence factors obtained through analysis, so that the influence of each intelligent energy influence factor on energy consumption is analyzed, potential energy saving opportunities and influence strategies are identified from the influence factors, and finally the energy saving potential influence factors are obtained.
Step S16: and performing carbon reduction influence detection analysis on the intelligent energy operation data by utilizing the intelligent energy influence factors to obtain the carbon reduction potential influence factors.
According to the embodiment of the invention, the intelligent energy operation data is analyzed by using the intelligent energy influence factors obtained through analysis, so that the influence of each intelligent energy influence factor on carbon emission is analyzed, a potential strategy and opportunity for reducing carbon emission are identified, and finally the potential influence factors for reducing carbon are obtained.
According to the intelligent energy system monitoring system, the integrated multi-mode sensor is used for carrying out monitoring data fusion processing on the intelligent energy system through the sensors such as energy production, transmission and storage, so that comprehensive real-time data acquisition can be realized, the comprehensive performance and accuracy of the intelligent energy system monitoring data are improved, and meanwhile, the intelligent energy system monitoring system helps to identify problems, abnormalities or potential threats in operation of the intelligent energy system in real time. The use of integrated multimodal sensors allows for more diversification of the monitored data and can be used for analysis and decision making in different aspects. By using blockchain technology to build a distributed data collection network, security, trustworthiness, and non-tamper ability of the collected data can be ensured, which is beneficial to prevent data from being tampered, or unauthorized access, while providing transparency and traceability of the data. The creation of a distributed data collection network can also reduce the risk of a single data source, making the data more reliable and available. And secondly, by disposing edge computing nodes in the established distributed data acquisition network, the data can be preprocessed and analyzed in real time at the source, so that the delay of data transmission can be reduced, and the instantaneity and response speed of the data are improved. And the edge computing node can perform preliminary data cleaning, filtering and analysis on intelligent energy distribution operation data, so that potential problems can be identified in advance, and the burden of data transmission and storage is reduced. Then, through carrying out influence factor mining analysis on the processed intelligent energy operation data, the intelligent energy system can identify potential influence factors such as weather, load change, equipment state and the like, which is beneficial to better understanding the back mechanism and influence factors of the intelligent energy system operation and provides basic data for subsequent energy conservation and carbon reduction analysis. By knowing these influencing factors, the system can more precisely formulate policies to cope with different situations. And then, performing energy-saving influence detection analysis on intelligent energy operation data by using the intelligent energy influence factors obtained by analysis to identify potential energy saving opportunities and influence strategies, which is beneficial to reducing energy cost, reducing resource waste, improving energy efficiency and supporting sustainable energy management, so that the follow-up energy-saving optimization processing process is helped to take influence correction measures more pertinently, and the energy saving target can be better realized in daily operation. Finally, the intelligent energy influence factors obtained through analysis are used for carrying out carbon reduction influence detection analysis on intelligent energy operation data so as to identify potential strategies and opportunities for reducing carbon emission, which are helpful for reducing environmental influence thereof, and can also improve the sustainability of the environment so as to meet environmental regulations and sustainable development targets. By analyzing potential carbon reduction opportunities in the smart energy system, the carbon footprint can be better managed and sustainable actions can be taken, contributing positive impact to the environment, thereby helping subsequent carbon reduction optimization processes.
Preferably, step S15 comprises the steps of:
step S151: carrying out energy-saving effect identification analysis on the intelligent energy source influence factors to obtain energy-saving related influence factors;
step S152: performing energy consumption space-time difference analysis on the intelligent energy operation data to obtain energy consumption difference data;
step S153: carrying out factor refinement analysis on the energy-saving related influence factors according to the energy consumption difference data to obtain energy-saving influence refinement factors;
step S154: carrying out evaluation, detection and calculation on energy-saving influence refinement factors by using an energy-saving influence degree calculation formula to obtain the energy-saving influence degree of the factors;
step S155: screening and judging the factor energy-saving influence degree according to a preset factor energy-saving influence threshold, and eliminating the energy-saving influence refinement factor corresponding to the factor energy-saving influence degree when the factor energy-saving influence degree does not exceed the preset factor energy-saving influence threshold; when the factor energy-saving influence degree exceeds a preset factor energy-saving influence threshold, potential factor identification analysis is performed on energy-saving influence refinement factors corresponding to the factor energy-saving influence degree, and energy-saving potential influence factors are obtained.
As an embodiment of the present invention, referring to fig. 3, a detailed step flow chart of step S15 in fig. 2 is shown, in which step S15 includes the following steps:
Step S151: carrying out energy-saving effect identification analysis on the intelligent energy source influence factors to obtain energy-saving related influence factors;
according to the embodiment of the invention, the intelligent energy influence factors are analyzed by using a data analysis method (such as statistical analysis, data mining or machine learning algorithm) so as to identify the influence factors related to energy conservation in the intelligent energy influence factors, such as temperature, humidity, load and the like, and finally the energy conservation related influence factors are obtained.
Step S152: performing energy consumption space-time difference analysis on the intelligent energy operation data to obtain energy consumption difference data;
according to the embodiment of the invention, intelligent energy operation data are segmented according to time and space, corresponding time sequences and space data sets are created, then energy consumption analysis is carried out on the intelligent energy operation data in each time period and space period respectively, so that energy consumption differences at different time points or places are compared, and finally energy consumption difference data are obtained.
Step S153: carrying out factor refinement analysis on the energy-saving related influence factors according to the energy consumption difference data to obtain energy-saving influence refinement factors;
according to the embodiment of the invention, the energy consumption difference data are used for carrying out more detailed analysis on the primarily identified energy-saving related influence factors so as to further analyze and determine how each energy-saving related influence factor influences the difference of energy consumption, and finally energy-saving influence refinement factors are obtained.
Step S154: carrying out evaluation, detection and calculation on energy-saving influence refinement factors by using an energy-saving influence degree calculation formula to obtain the energy-saving influence degree of the factors;
according to the embodiment of the invention, a proper energy-saving influence degree calculation formula is formed by combining integral range parameters, energy-saving power, maximum energy-saving power, energy-saving temperature, maximum energy-saving temperature, influence control parameters, influence attenuation parameters and related parameters of the energy-saving influence refinement factors, so that the energy-saving influence degree of the factors is finally obtained. In addition, the energy-saving influence degree calculation formula can also use any influence degree measuring method in the field to replace the energy-saving influence evaluation, detection and calculation process so as to evaluate and quantify the influence degree of each energy-saving influence refinement factor on energy consumption, and is not limited to the energy-saving influence degree calculation formula.
Step S155: screening and judging the factor energy-saving influence degree according to a preset factor energy-saving influence threshold, and eliminating the energy-saving influence refinement factor corresponding to the factor energy-saving influence degree when the factor energy-saving influence degree does not exceed the preset factor energy-saving influence threshold; when the factor energy-saving influence degree exceeds a preset factor energy-saving influence threshold, potential factor identification analysis is performed on energy-saving influence refinement factors corresponding to the factor energy-saving influence degree, and energy-saving potential influence factors are obtained.
According to the embodiment of the invention, the calculated factor energy-saving influence degree is judged by using the preset factor energy-saving influence threshold, if the factor energy-saving influence degree does not exceed the factor energy-saving influence threshold, the energy-saving influence refinement factor corresponding to the factor energy-saving influence degree is smaller in influence degree on energy consumption, the energy-saving influence refinement factor corresponding to the factor energy-saving influence degree is removed, if the factor energy-saving influence degree exceeds the factor energy-saving influence threshold, the energy-saving influence refinement factor corresponding to the factor energy-saving influence degree is larger in influence degree on energy consumption, the energy-saving influence refinement factor corresponding to the factor energy-saving influence degree is analyzed in more detail, and how the corresponding energy-saving influence refinement factor influences energy conservation is determined so as to find out potential influence factors, and finally the energy-saving potential influence factors are obtained.
According to the method, the energy-saving effect recognition analysis is performed on the intelligent energy effect factors to recognize potential energy-saving related effect factors. The intelligent energy influence factors are identified and analyzed through deep mining, so that factors such as temperature, humidity and load, which can influence energy consumption, can be determined, the comprehensive understanding of intelligent energy system operation is established, the determination of which factors can possibly have important influence on energy efficiency is facilitated, and basic data is provided for follow-up energy saving measures. Secondly, by performing energy consumption space-time difference analysis on the intelligent energy operation data, the space-time difference condition of energy consumption can be particularly focused, which is helpful for determining the energy consumption difference of different time and place, so as to identify the high energy consumption period and the low energy consumption period and the difference of the energy use modes on different positions or devices, and further provide insight about the energy use modes and help for further energy saving measures. The identified energy conservation related impact factors are then refined based on the analyzed energy consumption difference data, including further factoring each factor to more specifically understand how it affects energy consumption. For example, if temperature is a factor, it may be analyzed how fluctuations in temperature affect energy usage, which helps to more specifically identify potential energy conservation opportunities. Then, the energy-saving influence refinement factors are evaluated, detected and calculated by using a proper energy-saving influence degree calculation formula to evaluate the influence degree of each energy-saving influence refinement factor on energy consumption, so that the influence of each factor can be quantified to determine which factors have larger influence on energy efficiency, and guidance is provided for focusing attention. And finally, screening the influence degree of each factor according to a preset factor energy-saving influence threshold, and if the influence degree of a certain factor does not reach the threshold, not considering the influence degree as an important energy-saving factor and rejecting the important energy-saving factor. However, if the extent of influence of a factor exceeds a threshold, further analysis will be performed to identify potential influencing factors, which helps reveal deeper energy consumption patterns and energy conservation opportunities.
Preferably, the energy saving influence degree calculation formula in step S154 is specifically:
；
wherein E (x) i ) Refinement factor x for the ith energy saving impact i The factor energy-saving influence degree of (2), n is the quantity of energy-saving influence refinement factors, x i A is the ith energy saving influencing refinement factor, a i Is the firsti lower integral range limit of energy-saving influence refinement factors b i Upper limit of integration range for the ith energy saving influencing refinement factor, P (x i ) To save power under the influence of the ith energy saving influencing refinement factor, P max (x i ) For maximum energy saving power under the influence of the ith energy saving influencing refinement factor, alpha 1 Control parameter alpha for influencing energy-saving power 2 To influence the attenuation parameter, T (x i ) To save energy temperature under the influence of the ith energy saving influencing refinement factor, T max (x i ) To the maximum energy saving temperature under the influence of the ith energy saving influencing refinement factor, beta 1 Control parameter beta for influencing energy-saving temperature 2 And mu is a correction value of the energy-saving influence degree of the factor for influencing the attenuation parameter of the energy-saving temperature.
The invention constructs an energy-saving influence degree calculation formula for evaluating, detecting and calculating energy-saving influence refinement factors, comprehensively considering the influence of different factors on energy consumption and integrating the energy consumption within a specified integration range, thereby providing a comprehensive evaluation for helping to determine which energy-saving influence refinement factors have the most obvious influence on energy saving. In the subsequent processing process, the factors are screened according to a preset energy-saving influence threshold value to determine whether the influence of the potential factors needs to be further analyzed. When the extent of the factor energy savings impact exceeds a threshold, further analysis may be required to understand how these factors impact energy consumption, which facilitates system optimization and more efficient energy conservation strategy formulation. The formula fully considers the ith energy-saving influence refinement factor x i The degree of influence of energy conservation by factors E (x i ) The number n of energy-saving influencing refinement factors, the ith energy-saving influencing refinement factor x i The integral range lower limit a of the ith energy saving influencing refinement factor i The upper limit b of the integration range of the ith energy saving influencing refinement factor i The energy saving power P (x) under the influence of the ith energy saving influence refinement factor i ) Maximum energy saving power P under the influence of ith energy saving influence refinement factor max (x i ) Influence of energy saving Power control parameter alpha 1 Shadow of energy-saving powerSound attenuation parameter alpha 2 Energy saving temperature T (x) under the influence of the ith energy saving influencing refinement factor i ) Maximum energy saving temperature T under the influence of the ith energy saving influencing refinement factor max (x i ) Influence of energy saving temperature control parameter beta 1 Influence of energy saving temperature attenuation parameter beta 2 A correction value mu of the factor energy-saving influence degree refines the factor x according to the ith energy-saving influence i The degree of influence of energy conservation by factors E (x i ) The interrelationship between the parameters constitutes a functional relationship:
；
the formula can realize the evaluation, detection and calculation process of the energy-saving influence refinement factor, and meanwhile, the introduction of the correction value mu of the energy-saving influence degree of the factor can be adjusted according to the occurrence error in the calculation process, so that the accuracy and the applicability of the energy-saving influence degree calculation formula are improved.
Preferably, step S2 comprises the steps of:
step S21: performing energy flow visualization on the intelligent energy operation data to obtain an intelligent energy operation flow graph;
the embodiment of the invention visualizes the intelligent energy operation data into the intelligent energy operation flow chart by using the data visualization tools such as charts, graphs and flow charts so as to display the flow paths and key nodes of the energy and finally obtain the intelligent energy operation flow chart.
Step S22: performing behavior pattern recognition analysis on the intelligent energy operation flow graph to obtain an intelligent energy behavior pattern;
according to the embodiment of the invention, the intelligent energy operation flow diagram is analyzed and drawn so as to analyze and identify the energy behavior mode, the flow trend and the key characteristics in the intelligent energy operation flow diagram, including normal operation, high-load time period, abnormal events and the like, and different behavior modes are automatically identified and classified by using a mode identification algorithm, so that the intelligent energy behavior mode is finally obtained.
Step S23: performing energy use detection analysis on the intelligent energy operation data according to the intelligent energy behavior mode to obtain energy use condition data;
according to the embodiment of the invention, the intelligent energy behavior mode is used for detecting and monitoring the energy use condition of the corresponding intelligent energy operation data in real time so as to determine the actual energy consumption condition and finally obtain the energy use condition data.
Step S24: carrying out flow network modeling processing on the energy use condition data to obtain an energy use flow network;
the embodiment of the invention firstly integrates the energy use condition data into a data format required by an energy source flow network, wherein the data format comprises energy nodes and connection information, and then constructs the flow network by using a network modeling tool, wherein the nodes represent energy sources, conversion and consumption, and the edges represent the flow paths of the energy sources, so that the energy source use flow network is finally obtained.
Step S25: carrying out energy use load analysis on each energy node in the energy use flow network to obtain energy use load condition data;
according to the embodiment of the invention, through analyzing each energy node in the energy use flow network, including the energy consumption, output and storage conditions of the energy node, the energy use load condition of each energy node including the size, change and efficiency of the use load is calculated according to the analysis result, and finally the energy use load condition data is obtained.
Step S26: carrying out energy-saving load allocation analysis on the energy use condition data according to the energy use load condition data to obtain energy-saving allocation initial data;
According to the embodiment of the invention, the load optimization strategy is formulated by analyzing the energy use load condition data, including the strategies of energy transfer, equipment adjustment, operation scheduling and the like, and the relevant configuration of the energy use condition data is adjusted according to the load optimization strategy, so that the energy waste is reduced, the energy use efficiency of an intelligent energy system is improved, and finally the energy-saving allocation initial data is obtained.
Step S27: and carrying out allocation optimization adjustment on the energy-saving allocation initial data according to the energy-saving potential influence factors so as to obtain energy-saving allocation optimization data.
According to the embodiment of the invention, the energy-saving potential influence factors are used for further optimizing and analyzing the energy-saving dispatching initial data, so that a better energy optimizing and configuring strategy is obtained through analysis, the energy-saving dispatching initial data is optimized and adjusted according to the energy optimizing and configuring strategy, and finally the energy-saving dispatching optimized data is obtained.
According to the intelligent energy system energy flow visualization method, the intelligent energy system energy flow visualization is performed on intelligent energy operation data, so that the flow use condition from the source of energy to the terminal of the intelligent energy system can be intuitively known, the path of energy transmission, the source of energy and main energy consumption points in the intelligent energy system are revealed, comprehensive visual understanding is provided, potential energy waste and inefficient areas can be identified, and the energy saving strategy formulation is guided. And through behavior pattern recognition analysis on the intelligent energy operation flow diagram, different operation modes of energy in the system, including normal operation, high-load time period, abnormal events and the like, can be recognized, so that the operation characteristics of the energy can be better known, and the intelligent energy operation flow diagram can be used for recognizing potential problems or finding energy saving opportunities in operation in advance. For example, the discovery of abnormal behavior may suggest equipment failure or irregular operation, and further action may be taken to reduce energy waste. Meanwhile, the intelligent energy operation data is subjected to energy use detection analysis according to the identified intelligent energy behavior mode, and the energy use condition can be detected and monitored in real time according to the energy behavior mode, so that the actual energy consumption condition including which energy is used and the use amount can be determined. Through real-time monitoring and analysis, high energy consumption time periods and high energy consumption equipment or system parts can be identified, and measures are taken to reduce energy consumption, so that energy efficiency is improved. Second, by performing a flow network modeling process on the energy usage data, a suitable energy usage flow network can be established that facilitates quantifying energy flow relationships between different energy portions, which facilitates correlations between different nodes and devices in the system. Through the flow network, energy transmission, distribution and consumption can be quantified, providing a more detailed understanding of the system operation. Then, by analyzing each energy node in the energy usage flow network, the energy usage load condition of different nodes can be known, which is beneficial to determining which nodes or devices consume a large amount of energy, and is beneficial to identifying high energy consumption points in the system, so that the corresponding energy node can be taken as an object of important attention so as to take energy saving measures. Next, depending on the energy usage load, the energy usage may be redistributed to optimize the energy efficiency of the system, which may include reducing the energy consumption of the high energy consuming nodes, increasing the efficiency of the low energy consuming nodes, or adjusting the energy distribution pattern. Energy waste can be reduced to the greatest extent through energy-saving load allocation, and effective utilization of resources is improved. Finally, optimizing and adjusting the energy-saving allocation initial data according to the energy-saving potential influence factors obtained by the identification analysis, wherein the energy-saving allocation initial data may comprise automatically adjusting the energy use strategy according to the corresponding energy-saving potential influence factors, such as weather and load demands. By this step, energy efficiency can be further improved, cost can be reduced, and more sustainable energy management can be realized. The optimization of energy conservation allocation ensures that the system can reduce energy waste to the greatest extent under different conditions, improves the energy utilization rate, and is beneficial to sustainability and environmental protection.
Preferably, step S27 comprises the steps of:
step S271: performing energy-saving simulation pre-modeling processing on the intelligent energy system through the energy-saving allocation initial data to obtain energy-saving simulation result data;
according to the embodiment of the invention, the intelligent energy system is subjected to energy-saving simulation previewing by using the relevant configuration of the energy-saving allocation initial data so as to establish a simulation model of the intelligent energy system, and the energy-saving operation conditions of the intelligent energy system under different conditions, including energy allocation, equipment operation and the like, are previewed by setting simulation conditions, so that the energy-saving simulation result data is finally obtained.
Step S272: performing effect calculation on the energy-saving simulation result data by using an energy-saving effect degree value calculation formula to obtain an energy-saving simulation effect degree value;
according to the embodiment of the invention, a proper energy-saving effect degree value calculation formula is formed by combining the time variable calculated by the effect, the energy-saving simulation efficiency coefficient, the initial energy consumption before the implementation of the energy-saving simulation preview, the energy consumption after the implementation of the energy-saving simulation preview, the energy-saving balance factor, the energy consumption control parameter, the energy-saving influence factor, the energy-saving amplitude factor, the energy-saving efficiency factor and the related parameters, so that the energy-saving simulation result data is subjected to effect calculation, and finally the energy-saving simulation effect degree value is obtained. In addition, the energy-saving effect degree value calculation formula can also use any energy-saving effect detection method in the field to replace the energy-saving effect calculation process so as to quantify the effects of different energy-saving strategies, and is not limited to the energy-saving effect degree value calculation formula.
The energy-saving effect degree value calculation formula is as follows:
；
wherein C is the energy-saving simulation effect degree value, t 1 Start time, t, calculated for effect 2 For the termination time of the effect calculation, t is the integral time variable of the effect calculation, ρ (t) is the energy-saving simulation efficiency coefficient of the energy-saving simulation result data at the time t, θ is the initial energy consumption of the energy-saving simulation result data before the implementation of the energy-saving simulation preview,for energy consumption of the energy-saving simulation result data after implementation of energy-saving simulation preview, delta (t) is an energy-saving balance factor of the energy-saving simulation result data at time t, N is the number of energy-saving measures in the energy-saving simulation preview process, and R j (t) is the energy consumption control parameter, σ, of the jth energy saving measure at time t j (t) is the energy-saving influence factor of the jth energy-saving measure at time t, m j (t) is the energy saving magnitude factor of the jth energy saving measure at time t, +.>Energy saving efficiency factor epsilon for the jth energy saving measure at time tA correction value for the energy-saving simulation effect degree value;
the invention constructs an energy-saving effect degree value calculation formula for carrying out effect calculation on energy-saving simulation result data, integrates time variable, energy-saving efficiency, initial and final states of energy consumption, energy-saving measures and other parameters, and is used for quantifying the effect of energy-saving simulation, thereby helping to evaluate the effect of the energy-saving measures in the energy-saving simulation process and guiding energy-saving optimization decision and optimizing an energy-saving scheme. The formula fully considers the energy-saving simulation effect degree value C and the starting time t of effect calculation 1 Termination time t of effect calculation 2 Integration time variable t of effect calculation, energy-saving simulation efficiency coefficient rho (t) of energy-saving simulation result data at time t, initial energy consumption theta of energy-saving simulation result data before energy-saving simulation preview is implemented, and energy consumption of energy-saving simulation result data after energy-saving simulation preview is implementedEnergy-saving balance factor delta (t) of energy-saving simulation result data at time t, number N of energy-saving measures in energy-saving simulation replay process, and energy consumption control parameter R of jth energy-saving measure at time t j (t) energy saving influencing factor sigma of the jth energy saving measure at time t j (t) energy saving amplitude factor m of the jth energy saving measure at time t j (t), energy-saving efficiency factor of the jth energy-saving measure at time t>The correction value epsilon of the energy-saving simulation effect degree value forms a functional relation according to the correlation relation between the energy-saving simulation effect degree value C and the parameters:
；
the formula can realize the effect calculation process of the energy-saving simulation result data, and meanwhile, the introduction of the correction value epsilon of the energy-saving simulation effect degree value can be adjusted according to the occurrence error in the calculation process, so that the accuracy and the applicability of the energy-saving effect degree value calculation formula are improved.
Step S273: performing energy-saving difference evaluation analysis on the energy-saving simulation result data according to the energy-saving simulation effect degree value to obtain energy-saving simulation low-efficiency data and energy-saving simulation high-efficiency data;
according to the embodiment of the invention, the calculated energy-saving simulation effect degree value is judged by using the preset energy-saving simulation effect degree threshold value, if the energy-saving simulation effect degree value is larger than or equal to the energy-saving simulation effect degree threshold value, the energy-saving effect of energy-saving measures in the energy-saving simulation result data corresponding to the energy-saving simulation effect degree value is higher, the energy-saving simulation result data corresponding to the energy-saving simulation effect degree value is marked as energy-saving simulation high-efficiency data, and if the energy-saving simulation effect degree value is smaller than the energy-saving simulation effect degree threshold value, the energy-saving effect of energy-saving measures in the energy-saving simulation result data corresponding to the energy-saving simulation effect degree value is lower, and the energy-saving simulation result data corresponding to the energy-saving simulation effect degree value is marked as energy-saving simulation low-efficiency data.
Step S274: hierarchical adjustment processing is carried out on the energy-saving simulation low-efficiency data and the energy-saving simulation high-efficiency data according to the energy-saving potential influence factors, so that energy-saving simulation adjustment data are obtained;
The embodiment of the invention carries out hierarchical adjustment on the energy-saving simulation low-efficiency data and the energy-saving simulation high-efficiency data by using the energy-saving potential influence factors to formulate corresponding adjustment strategies so as to further hierarchically improve the energy-saving effects of the energy-saving simulation low-efficiency data and the energy-saving simulation high-efficiency data and finally obtain the energy-saving simulation adjustment data.
Step S275: and carrying out collaborative optimization processing on the energy-saving allocation initial data by using the energy-saving simulation adjustment data to obtain energy-saving allocation optimization data.
According to the embodiment of the invention, the energy-saving simulation adjustment data are applied to the energy-saving allocation initial data of the intelligent energy system by using optimization algorithms such as genetic algorithm, simulated annealing and the like, so that corresponding collaborative optimization energy-saving allocation is realized, and finally the energy-saving allocation optimization data are obtained.
According to the intelligent energy system energy-saving simulation pre-modeling method, the intelligent energy system is subjected to energy-saving simulation pre-modeling processing through the related configuration of the simulation energy-saving allocation initial data by using a simulation technology, so that the energy-saving operation condition of the intelligent energy system under different conditions can be simulated, including energy allocation, equipment operation and the like. Different energy-saving strategies can be simulated through simulation processing, potential effects of the intelligent energy system can be known, and the intelligent energy system does not need to be practically applied to the intelligent energy system, so that time and resources are saved. And secondly, performing effect calculation on the energy-saving simulation result data by using a proper energy-saving effect degree value calculation formula so as to quantify the effects of different energy-saving strategies, wherein the energy-saving effect degree value is comprehensively calculated by fully considering factors such as energy consumption, energy-saving balance factors, energy-saving influence factors, energy-saving efficiency factors and the like by the calculation formula, the benefit of each energy-saving strategy can be fully quantified, and a decision maker can make an intelligent choice. And then, carrying out energy-saving difference evaluation analysis on the energy-saving simulation result data according to the calculated energy-saving simulation effect degree value so as to evaluate the effect of each energy-saving strategy. By comparing the effect degree values of different energy-saving strategies, the energy-saving simulation result data can be divided into two categories of low efficiency and high efficiency, so that the energy-saving simulation result data are beneficial to identifying which energy-saving strategies perform better under specific situations, and further refinement of strategy selection is facilitated. And then, by considering energy-saving potential influence factors including equipment performance, weather conditions, load demands and the like, the energy-saving simulation low-efficiency data and the energy-saving simulation high-efficiency data obtained by evaluation and analysis are subjected to hierarchical adjustment, so that the energy-saving simulation data can be refined, and the energy-saving simulation data are adjusted according to the data with different effects, so that the energy-saving simulation data are more suitable for practical application, the simulation accuracy is improved, and the energy-saving simulation low-efficiency data are more suitable for the running situation of a practical system. Finally, by applying the energy-saving simulation adjustment data to the energy-saving allocation initial data of the intelligent energy system, corresponding collaborative optimization can be realized, and the step is beneficial to ensuring that the selected energy-saving strategy is better adapted to the actual running condition of the system. The collaborative optimization can help the system to realize the best energy-saving effect under different conditions, so that the energy waste is reduced to the greatest extent, the energy utilization rate is improved, the cost is reduced, and the adverse effect on the environment is reduced.
Preferably, step S274 comprises the steps of:
step S2741: performing energy-saving mining analysis on the energy-saving potential influence factors to obtain an energy-saving potential influence adjustment space;
according to the embodiment of the invention, the energy-saving potential influence factors are analyzed by using a data mining technology (such as a machine learning algorithm or a statistical analysis method) so as to analyze the potential improvement or optimization direction related to energy saving in the energy-saving potential influence factors, and finally, an energy-saving potential influence adjustment space is obtained.
Step S2742: performing first-level adjustment processing on the energy-saving simulation low-efficiency data and the energy-saving simulation high-efficiency data according to the energy-saving potential influence adjustment space to obtain the energy-saving low-efficiency adjustment data and the energy-saving high-efficiency adjustment data;
according to the embodiment of the invention, the energy-saving simulation low-efficiency data and the energy-saving simulation high-efficiency data are subjected to first-level adjustment by using the potential improvement or optimization direction in the energy-saving potential influence adjustment space so as to adjust and compare the effects of different energy-saving strategies, so that the energy efficiency conditions under different energy-saving strategy adjustment levels are reflected, and finally the energy-saving low-efficiency adjustment data and the energy-saving high-efficiency adjustment data are obtained.
Step S2743: the energy-saving deep learning is carried out on the energy-saving potential influence adjustment space by utilizing the energy-saving high-efficiency adjustment data, so that the energy-saving deep influence adjustment space is obtained;
According to the embodiment of the invention, deep learning, neural network or other machine learning technologies are used for deep learning of the energy-saving efficient adjustment data, so that the energy-saving effect adjustment mode of the energy-saving efficient adjustment data with a deeper level is learned, the energy-saving potential effect adjustment space is shifted and learned according to the learned energy-saving effect adjustment mode, so that better energy saving opportunities are accurately identified and quantized, and finally the energy-saving deep effect adjustment space is obtained.
Step S2744: performing second-level adjustment processing on the energy-saving low-efficiency adjustment data according to the energy-saving deep-layer influence adjustment space to obtain low-efficiency deep-layer adjustment data;
according to the embodiment of the invention, the energy-saving low-efficiency adjustment data is subjected to second-level adjustment by using the deeper improved or optimized direction in the energy-saving deep-layer influence adjustment space, so that the energy efficiency of the energy-saving low-efficiency adjustment data is further adjusted and improved, and finally the low-efficiency deep-layer adjustment data is obtained.
Step S2745: and carrying out elastic fusion adjustment on the low-efficiency deep adjustment data and the energy-saving high-efficiency adjustment data to obtain the energy-saving simulation adjustment data.
According to the embodiment of the invention, the low-efficiency deep adjustment data and the energy-saving high-efficiency adjustment data are fused together by using an elastic fusion method, and the energy-saving adjustment information of different layers is comprehensively considered to generate more comprehensive and comprehensive data so as to ensure that the data can meet energy-saving targets and requirements and finally obtain the energy-saving simulation adjustment data.
The invention firstly carries out energy-saving mining analysis on the energy-saving potential influencing factors, is helpful for identifying and understanding key factors in energy consumption, and has the effect of providing deep understanding of the energy-saving potential factors, thereby providing a foundation for further energy-saving measure formulation. By analyzing these energy savings potential impact factors, potential energy savings opportunities and optimization potential space can be determined, thereby helping the system to improve energy efficiency more targeted. And secondly, performing first-level adjustment processing on the energy-saving simulation low-efficiency data and the energy-saving simulation high-efficiency data according to the energy-saving potential influence adjustment space obtained by analysis, and optimizing the corresponding simulation data to enable the simulation data to reflect the actual situation more accurately. The energy-saving low-efficiency adjustment data and the energy-saving high-efficiency adjustment data obtained after adjustment are beneficial to simulating and comparing the effects of different energy-saving strategies so as to better know which strategies are more effective for energy conservation. Then, the energy-saving and efficient adjustment data are used for carrying out energy-saving deep learning on the energy-saving and potential influence adjustment space so as to better learn and understand the influence adjustment space of the energy-saving and efficient adjustment data, the effect of the step is to improve the understanding of the energy-saving and efficient adjustment data, the energy-saving and efficient adjustment data can be accurately identified and quantized better, the energy-saving and potential-saving and low-efficiency adjustment data can be further adjusted, and a foundation is provided for formulating more targeted energy-saving strategies and measures, so that the larger energy-saving potential is realized. Next, by performing a second hierarchical adjustment process on the energy-saving inefficient adjustment data using the learned energy-saving deep impact adjustment space, it is helpful to more finely optimize the energy-saving inefficient adjustment data, identify and further improve the energy-saving inefficient portion in the smart energy system, and also to provide a more specific improvement direction, thereby achieving greater energy savings. Finally, through carrying out elastic fusion adjustment on the low-efficiency deep adjustment data and the energy-saving high-efficiency adjustment data, the energy-saving adjustment information of different layers can be comprehensively considered to generate more comprehensive and comprehensive data, the data is helpful for supporting decision making, optimizing an energy system and implementing effective energy-saving measures, so that the energy consumption is reduced, the cost is reduced, and the sustainability is improved.
Preferably, step S3 comprises the steps of:
step S31: performing carbon emission monitoring analysis on the intelligent energy operation data to obtain energy carbon emission monitoring data;
according to the embodiment of the invention, the emission data in the intelligent energy operation data is monitored by using the carbon emission monitoring equipment so as to monitor the carbon emission condition of the intelligent energy system, and finally the energy carbon emission monitoring data is obtained.
Step S32: performing hot spot visual analysis on the energy carbon emission monitoring data to obtain an energy carbon emission hot spot distribution map;
according to the embodiment of the invention, the relevant time and place information of the energy carbon emission monitoring data is acquired, and then the energy carbon emission monitoring data is visualized into the hot spot distribution map by using a proper hot spot analysis method (such as a thermodynamic map) so as to display the spatial distribution condition of carbon emission, and finally the energy carbon emission hot spot distribution map is obtained.
Step S33: performing level evaluation analysis on the energy carbon emission hotspot distribution map to obtain carbon emission level evaluation data;
according to the embodiment of the invention, the carbon emission level evaluation data is finally obtained by determining the indexes for evaluating the carbon emission level, such as the indexes of emission intensity, emission concentration and the like, and then evaluating and analyzing each region in the energy carbon emission hot spot distribution map to evaluate and quantify the carbon emission level of different regions in the energy carbon emission hot spot distribution map.
Step S34: performing energy carbon reduction treatment on the carbon emission level evaluation data to obtain energy carbon reduction initial data;
according to the embodiment of the invention, the carbon emission level evaluation data is analyzed to determine the target and the requirement of carbon reduction, namely the carbon emission level which is expected to be achieved, and then, a corresponding carbon reduction strategy is formulated according to the target and the requirement of carbon reduction, wherein the carbon reduction comprises the steps of reducing energy consumption, adopting a low-carbon technology and the like, and the carbon emission level evaluation data is subjected to carbon reduction treatment according to the carbon reduction strategy so as to reduce the carbon emission of an intelligent energy system, and finally, the initial energy carbon reduction data is obtained.
Step S35: performing carbon reduction optimization recognition analysis on the energy carbon reduction initial data according to the potential influence factors of carbon reduction to obtain a potential factor carbon reduction optimization strategy;
according to the embodiment of the invention, the energy carbon reduction initial data is analyzed by using the carbon reduction potential influence factors, so that the optimization opportunities of the energy carbon reduction initial data for carbon reduction are further analyzed according to various factors such as technical improvement, policy formulation, economic incentive and the like, and corresponding optimization strategies are formulated according to the carbon reduction optimization opportunities obtained by analysis, and finally the potential factor carbon reduction optimization strategies are obtained.
Step S36: and dynamically adjusting the energy carbon reduction initial data through a potential factor carbon reduction optimization strategy to obtain energy carbon reduction optimization data.
According to the embodiment of the invention, the potential factor carbon reduction optimization strategy is used for correspondingly adjusting the energy carbon reduction initial data so as to further reduce carbon emission, after the potential factor carbon reduction optimization strategy is implemented, the carbon emission data is continuously monitored, the implementation effect of the strategy is ensured, and the carbon reduction effect is continuously improved through continuous dynamic adjustment and optimization of the carbon reduction strategy, so that the energy carbon reduction optimization data is finally obtained.
According to the invention, the intelligent energy operation data is firstly subjected to carbon emission monitoring analysis to monitor the carbon emission condition of the energy system, so that an accurate carbon emission data base can be established, actual data support is provided for the subsequent energy carbon reduction analysis process, and the comprehensive understanding of the carbon emission level of the energy system is facilitated. And through carrying out the visual analysis of the hot spots on the monitored energy carbon emission monitoring data, the spatial distribution of the carbon emission is displayed in the form of a hot spot diagram, and the high-low hot spot areas of the carbon emission can be clearly displayed, so that the identification of the key areas of the energy carbon emission is facilitated, guidance is provided for the regional carbon emission reduction strategy of the energy system, and a decision maker can take measures to reduce the carbon emission in a targeted manner. And secondly, by carrying out level evaluation analysis on the energy carbon emission hotspot distribution map, the carbon emission levels of different areas can be quantified, and a basis is provided for setting regional carbon emission reduction targets. Evaluating the energy carbon emission hotspot profile helps determine which areas require more stringent carbon emission reduction policies to achieve the overall carbon emission reduction goal. Then, the carbon emission level evaluation data is subjected to energy carbon reduction treatment, including measures such as clean energy, energy utilization efficiency improvement and the like, so as to reduce carbon emission, initial data of energy carbon reduction are generated in the step, a foundation is laid for carbon emission reduction work, the carbon emission of an intelligent energy system is reduced, and a starting point is provided for a carbon emission reduction strategy. And then, carrying out carbon reduction optimization recognition analysis on the energy carbon reduction initial data by using the carbon reduction potential influence factors obtained by the previous detection, and carrying out further carbon reduction optimization analysis according to various factors such as technical improvement, policy formulation, economic incentive and the like, thereby formulating a targeted carbon reduction optimization strategy so as to reduce carbon emission to the greatest extent, which is beneficial to implementing more effective carbon emission reduction measures and improving better carbon reduction effect. Finally, the energy carbon reduction initial data is dynamically adjusted by using a latent factor carbon reduction optimization strategy, which is beneficial to generating more optimized energy carbon reduction data to reflect the actual effect of the carbon reduction strategy. The dynamic adjustment process can update data according to different factors and change conditions, so that more flexible and effective carbon emission reduction work is realized, carbon emission is reduced to the greatest extent, and optimization and sustainable development of an energy system are realized.
Preferably, step S4 comprises the steps of:
step S41: carrying out feature engineering extraction analysis on the energy conservation allocation optimization data and the energy carbon reduction optimization data to obtain energy conservation allocation optimization features and energy carbon reduction optimization features;
according to the embodiment of the invention, firstly, the energy conservation allocation optimization data and the energy carbon reduction optimization data are cleaned to process missing values, abnormal values and repeated data, the cleaned data are processed by using a time-frequency domain analysis method, characteristic information related to the energy conservation optimization and the carbon reduction optimization, including statistical characteristics, time sequence characteristics, frequency domain characteristics and the like, is extracted, and then, the most related characteristics are selected by using a principal component analysis method to reduce characteristic dimensions, so that the energy conservation allocation optimization characteristics and the energy carbon reduction optimization characteristics are finally obtained.
Step S42: carrying out dynamic feature mining analysis on the energy-saving allocation optimization features and the energy carbon reduction optimization features to obtain energy-saving optimization dynamic features and carbon reduction optimization dynamic features;
according to the embodiment of the invention, the corresponding dynamic feature mining feature sets are determined from the energy-saving allocation optimization features and the energy carbon reduction optimization features, meanwhile, the corresponding time windows are set to divide the feature sets, dynamic information of the feature sets, such as mean value, variance, trend and the like, is extracted in each time window, and then the extracted dynamic features are analyzed to know the change conditions of the extracted dynamic features in different time windows, so that the energy-saving optimization dynamic features and the carbon reduction optimization dynamic features are finally obtained.
Step S43: performing association mining analysis on the energy-saving optimization dynamic characteristics and the carbon reduction optimization dynamic characteristics to obtain an energy-saving carbon reduction association relation;
according to the embodiment of the invention, the association relation between the energy-saving optimization dynamic characteristics and the carbon reduction optimization dynamic characteristics is found by analyzing the energy-saving optimization dynamic characteristics and the carbon reduction optimization dynamic characteristics through an association rule mining algorithm (such as Apriori or FP-Growth), and finally the energy-saving carbon reduction association relation is obtained.
Step S44: performing feature fusion analysis on the energy-saving optimization dynamic features and the carbon reduction optimization dynamic features according to the energy-saving carbon reduction association relation to obtain energy-saving carbon reduction optimization features;
according to the embodiment of the invention, the energy-saving and carbon-reduction optimization dynamic characteristics and the carbon-reduction optimization dynamic characteristics are subjected to fusion analysis by using the energy-saving and carbon-reduction incidence relation obtained by mining, so that the characteristic information with strong correlation can be fused together, and then the related characteristics are combined into a new characteristic by using methods such as weighted average, PCA (principal component analysis) and the like, so that the energy-saving and carbon-reduction optimization characteristics are finally obtained.
Step S45: and establishing an energy comprehensive optimization operation model according to the energy-saving carbon reduction optimization characteristics.
The embodiment of the invention firstly uses the method according to the preset dividing ratio 7:2: and 1, dividing the energy-saving carbon reduction optimization features into a training set, a verification set and a test set, and then performing model training, verification and test by using a random forest algorithm according to the division result to construct a model capable of accurately realizing comprehensive energy optimization, so as to finally obtain a comprehensive energy optimization operation model.
According to the invention, the energy conservation allocation optimization data and the energy carbon reduction optimization data are subjected to characteristic engineering extraction analysis, so that the pretreatment and preparation of the data are benefited, and the characteristic data with more information is provided for subsequent modeling. The selection, extraction and processing of features can help to select the most relevant features, thereby reducing the dimensionality of the data, removing noise, deriving new features, processing missing values, and normalizing the data, thereby improving the performance and generalization capability of the model. And secondly, dynamic feature mining analysis is carried out on the energy conservation allocation optimization features and the energy carbon reduction optimization features, so that deep understanding of the time-varying modes of feature data is facilitated, abnormal values are identified, and more accurate dynamic feature information is provided for modeling. In addition, the time dependence of the data can be revealed through time sequence analysis, so that the processing capacity of the model on time-related features is improved, and the robustness of the model is improved. And then, carrying out association mining analysis on the energy-saving optimization dynamic characteristics and the carbon reduction optimization dynamic characteristics, so that the internal relation between the two characteristics is found, and the data is better understood. The method not only can help identify the correlation among the features, but also can provide guidance for model selection, so that the redundancy of the features is reduced, the accuracy and efficiency of the model are improved, and the method is also helpful for deep insight into potential information in data. And then, carrying out feature fusion analysis on the energy-saving optimization dynamic features and the carbon reduction optimization dynamic features by using the energy-saving carbon reduction association relation obtained by mining analysis, so that the associated features can be combined, the dimension of data can be reduced, redundant information is reduced, the simplicity, accuracy and interpretation of the model are improved, the performance of the model is optimized, the model is focused on the most important information, and meanwhile, a clearer visual result can be provided. And finally, establishing a proper comprehensive energy optimizing operation model through the fused energy-saving carbon reduction optimizing characteristics, wherein the model can accurately predict and optimize the operation of an energy system and provide an effective energy management strategy, which is beneficial to saving energy and reducing carbon emission to the greatest extent, can visually display the energy-saving carbon reduction effect, and provides decision support for a decision maker so as to realize sustainable energy management.
Preferably, the invention also provides a comprehensive optimization operation system based on the energy-saving and carbon-reduction intelligent energy, which is used for executing the comprehensive optimization operation method based on the energy-saving and carbon-reduction intelligent energy, and comprises the following steps:
the influence factor detection and analysis module is used for carrying out data monitoring, acquisition and processing on the intelligent energy system to obtain intelligent energy operation data; performing potential influence detection analysis on the intelligent energy operation data so as to obtain energy-saving potential influence factors and carbon reduction potential influence factors;
the energy-saving optimization adjustment module is used for carrying out energy use monitoring analysis on the intelligent energy operation data to obtain energy use condition data; performing energy conservation allocation analysis on the energy use condition data to obtain energy conservation allocation initial data; performing allocation optimization adjustment on the energy-saving allocation initial data according to the energy-saving potential influence factors to obtain energy-saving allocation optimization data;
the carbon reduction optimization adjustment module is used for carrying out carbon emission evaluation analysis on the intelligent energy operation data to obtain carbon emission level evaluation data; performing energy carbon reduction treatment on the carbon emission level evaluation data to obtain energy carbon reduction initial data; performing carbon reduction optimization adjustment on the energy carbon reduction initial data according to the potential influence factors of carbon reduction to obtain energy carbon reduction optimization data;
The comprehensive optimization model construction module is used for carrying out feature association analysis on the energy conservation allocation optimization data and the energy carbon reduction optimization data to obtain energy conservation carbon reduction optimization features; establishing an energy comprehensive optimization operation model according to the energy-saving carbon reduction optimization characteristics;
the model prediction processing module is used for carrying out energy optimization prediction processing on the intelligent energy operation data through the energy comprehensive optimization operation model so as to obtain an energy optimization operation prediction result; and executing a corresponding comprehensive optimization operation strategy according to the energy optimization operation prediction result.
In summary, the invention provides a comprehensive optimization operation system based on energy-saving carbon reduction smart energy, which consists of an influence factor detection analysis module, an energy-saving optimization adjustment module, a carbon reduction optimization adjustment module, a comprehensive optimization model construction module and a model prediction processing module, and can realize any comprehensive optimization operation method based on energy-saving carbon reduction smart energy.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (2)
1. The comprehensive optimization operation method based on the energy-saving carbon reduction intelligent energy is characterized by comprising the following steps of:
step S1: performing data monitoring and acquisition processing on the intelligent energy system to obtain intelligent energy operation data; performing potential influence detection analysis on the intelligent energy operation data to obtain energy-saving potential influence factors and carbon reduction potential influence factors; step S1 comprises the steps of:
Step S11: the intelligent energy system is subjected to data monitoring fusion processing through an integrated multi-mode sensor to obtain intelligent energy initial information data;
step S12: establishing a distributed data acquisition network by using a block chain technology, and performing distributed acquisition processing on intelligent energy initial information data by using the distributed data acquisition network to obtain intelligent energy distributed operation data;
step S13: the intelligent energy distribution operation data are obtained by deploying edge calculation nodes in a distributed data acquisition network and carrying out real-time data preprocessing on the intelligent energy distribution operation data in the edge calculation nodes;
step S14: carrying out influence factor mining analysis on the intelligent energy operation data to obtain intelligent energy influence factors;
step S15: performing energy-saving influence detection analysis on intelligent energy operation data by utilizing intelligent energy influence factors to obtain energy-saving potential influence factors; step S15 includes the steps of:
step S151: carrying out energy-saving effect identification analysis on the intelligent energy source influence factors to obtain energy-saving related influence factors;
step S152: performing energy consumption space-time difference analysis on the intelligent energy operation data to obtain energy consumption difference data;
Step S153: carrying out factor refinement analysis on the energy-saving related influence factors according to the energy consumption difference data to obtain energy-saving influence refinement factors;
step S154: carrying out evaluation, detection and calculation on energy-saving influence refinement factors by using an energy-saving influence degree calculation formula to obtain the energy-saving influence degree of the factors; the energy-saving influence degree calculation formula in step S154 is specifically:
；
wherein E (x) i ) Refinement factor x for the ith energy saving impact i The factor energy-saving influence degree of (2), n is the quantity of energy-saving influence refinement factors, x i A is the ith energy saving influencing refinement factor, a i Lower integral range limit for the ith energy saving influencing refinement factor, b i Upper limit of integration range for the ith energy saving influencing refinement factor, P (x i ) To save power under the influence of the ith energy saving influencing refinement factor, P max (x i ) For maximum energy saving power under the influence of the ith energy saving influencing refinement factor, alpha 1 Control parameter alpha for influencing energy-saving power 2 To influence the attenuation parameter, T (x i ) To save energy temperature under the influence of the ith energy saving influencing refinement factor, T max (x i ) To save energy at the ithInfluence the maximum energy-saving temperature under the influence of a thinning factor beta 1 Control parameter beta for influencing energy-saving temperature 2 The attenuation parameter is the influence of energy-saving temperature, and mu is the correction value of the influence degree of factor energy saving;
Step S155: screening and judging the factor energy-saving influence degree according to a preset factor energy-saving influence threshold, and eliminating the energy-saving influence refinement factor corresponding to the factor energy-saving influence degree when the factor energy-saving influence degree does not exceed the preset factor energy-saving influence threshold; when the factor energy-saving influence degree exceeds a preset factor energy-saving influence threshold, carrying out potential factor identification analysis on energy-saving influence refinement factors corresponding to the factor energy-saving influence degree to obtain energy-saving potential influence factors;
step S16: performing carbon reduction influence detection analysis on intelligent energy operation data by utilizing intelligent energy influence factors to obtain potential carbon reduction influence factors;
step S2: performing energy use monitoring analysis on the intelligent energy operation data to obtain energy use condition data; performing energy conservation allocation analysis on the energy use condition data to obtain energy conservation allocation initial data; performing allocation optimization adjustment on the energy-saving allocation initial data according to the energy-saving potential influence factors to obtain energy-saving allocation optimization data; step S2 comprises the steps of:
step S21: performing energy flow visualization on the intelligent energy operation data to obtain an intelligent energy operation flow graph;
Step S22: performing behavior pattern recognition analysis on the intelligent energy operation flow graph to obtain an intelligent energy behavior pattern;
step S23: performing energy use detection analysis on the intelligent energy operation data according to the intelligent energy behavior mode to obtain energy use condition data;
step S24: carrying out flow network modeling processing on the energy use condition data to obtain an energy use flow network;
step S25: carrying out energy use load analysis on each energy node in the energy use flow network to obtain energy use load condition data;
step S26: carrying out energy-saving load allocation analysis on the energy use condition data according to the energy use load condition data to obtain energy-saving allocation initial data;
step S27: performing allocation optimization adjustment on the energy-saving allocation initial data according to the energy-saving potential influence factors to obtain energy-saving allocation optimization data; step S27 includes the steps of:
step S271: performing energy-saving simulation pre-modeling processing on the intelligent energy system through the energy-saving allocation initial data to obtain energy-saving simulation result data;
step S272: performing effect calculation on the energy-saving simulation result data by using an energy-saving effect degree value calculation formula to obtain an energy-saving simulation effect degree value;
The energy-saving effect degree value calculation formula is as follows:
；
wherein C is the energy-saving simulation effect degree value, t 1 Start time, t, calculated for effect 2 For the termination time of the effect calculation, t is the integral time variable of the effect calculation, ρ (t) is the energy-saving simulation efficiency coefficient of the energy-saving simulation result data at the time t, θ is the initial energy consumption of the energy-saving simulation result data before the implementation of the energy-saving simulation preview,for energy consumption of the energy-saving simulation result data after implementation of energy-saving simulation preview, delta (t) is an energy-saving balance factor of the energy-saving simulation result data at time t, N is the number of energy-saving measures in the energy-saving simulation preview process, and R j (t) is the energy consumption control parameter, σ, of the jth energy saving measure at time t j (t) is the energy-saving influence factor of the jth energy-saving measure at time t, m j (t) is the energy saving magnitude factor of the jth energy saving measure at time t, +.>Energy saving efficiency factor for the jth energy saving measure at time t, ε being the sectionCorrection values capable of simulating the effect degree values;
step S273: performing energy-saving difference evaluation analysis on the energy-saving simulation result data according to the energy-saving simulation effect degree value to obtain energy-saving simulation low-efficiency data and energy-saving simulation high-efficiency data;
Step S274: hierarchical adjustment processing is carried out on the energy-saving simulation low-efficiency data and the energy-saving simulation high-efficiency data according to the energy-saving potential influence factors, so that energy-saving simulation adjustment data are obtained; step S274 includes the steps of:
step S2741: performing energy-saving mining analysis on the energy-saving potential influence factors to obtain an energy-saving potential influence adjustment space;
step S2742: performing first-level adjustment processing on the energy-saving simulation low-efficiency data and the energy-saving simulation high-efficiency data according to the energy-saving potential influence adjustment space to obtain the energy-saving low-efficiency adjustment data and the energy-saving high-efficiency adjustment data;
step S2743: the energy-saving deep learning is carried out on the energy-saving potential influence adjustment space by utilizing the energy-saving high-efficiency adjustment data, so that the energy-saving deep influence adjustment space is obtained;
step S2744: performing second-level adjustment processing on the energy-saving low-efficiency adjustment data according to the energy-saving deep-layer influence adjustment space to obtain low-efficiency deep-layer adjustment data;
step S2745: performing elastic fusion adjustment on the low-efficiency deep adjustment data and the energy-saving high-efficiency adjustment data to obtain energy-saving simulation adjustment data;
step S275: performing collaborative optimization processing on the energy-saving allocation initial data by using the energy-saving simulation adjustment data to obtain energy-saving allocation optimization data;
Step S3: performing carbon emission evaluation analysis on the intelligent energy operation data to obtain carbon emission level evaluation data; performing energy carbon reduction treatment on the carbon emission level evaluation data to obtain energy carbon reduction initial data; performing carbon reduction optimization adjustment on the energy carbon reduction initial data according to the potential influence factors of carbon reduction to obtain energy carbon reduction optimization data; step S3 comprises the steps of:
step S31: performing carbon emission monitoring analysis on the intelligent energy operation data to obtain energy carbon emission monitoring data;
step S32: performing hot spot visual analysis on the energy carbon emission monitoring data to obtain an energy carbon emission hot spot distribution map;
step S33: performing level evaluation analysis on the energy carbon emission hotspot distribution map to obtain carbon emission level evaluation data;
step S34: performing energy carbon reduction treatment on the carbon emission level evaluation data to obtain energy carbon reduction initial data;
step S35: performing carbon reduction optimization recognition analysis on the energy carbon reduction initial data according to the potential influence factors of carbon reduction to obtain a potential factor carbon reduction optimization strategy;
step S36: dynamically adjusting the initial energy carbon reduction data through a potential factor carbon reduction optimization strategy to obtain energy carbon reduction optimization data;
Step S4: performing feature association analysis on the energy conservation allocation optimization data and the energy carbon reduction optimization data to obtain energy conservation carbon reduction optimization features; establishing an energy comprehensive optimization operation model according to the energy-saving carbon reduction optimization characteristics; step S4 comprises the steps of:
step S41: carrying out feature engineering extraction analysis on the energy conservation allocation optimization data and the energy carbon reduction optimization data to obtain energy conservation allocation optimization features and energy carbon reduction optimization features;
step S42: carrying out dynamic feature mining analysis on the energy-saving allocation optimization features and the energy carbon reduction optimization features to obtain energy-saving optimization dynamic features and carbon reduction optimization dynamic features;
step S43: performing association mining analysis on the energy-saving optimization dynamic characteristics and the carbon reduction optimization dynamic characteristics to obtain an energy-saving carbon reduction association relation;
step S44: performing feature fusion analysis on the energy-saving optimization dynamic features and the carbon reduction optimization dynamic features according to the energy-saving carbon reduction association relation to obtain energy-saving carbon reduction optimization features;
step S45: establishing an energy comprehensive optimization operation model according to the energy-saving carbon reduction optimization characteristics;
step S5: performing energy optimization prediction processing on the intelligent energy operation data through an energy comprehensive optimization operation model to obtain an energy optimization operation prediction result; and executing a corresponding comprehensive optimization operation strategy according to the energy optimization operation prediction result.
2. An integrated optimization operation system based on energy-saving and carbon-reduction intelligent energy, which is characterized by being used for executing the integrated optimization operation method based on energy-saving and carbon-reduction intelligent energy as claimed in claim 1, wherein the integrated optimization operation system based on energy-saving and carbon-reduction intelligent energy comprises:
the influence factor detection and analysis module is used for carrying out data monitoring, acquisition and processing on the intelligent energy system to obtain intelligent energy operation data; performing potential influence detection analysis on the intelligent energy operation data so as to obtain energy-saving potential influence factors and carbon reduction potential influence factors;
the energy-saving optimization adjustment module is used for carrying out energy use monitoring analysis on the intelligent energy operation data to obtain energy use condition data; performing energy conservation allocation analysis on the energy use condition data to obtain energy conservation allocation initial data; performing allocation optimization adjustment on the energy-saving allocation initial data according to the energy-saving potential influence factors to obtain energy-saving allocation optimization data;
the carbon reduction optimization adjustment module is used for carrying out carbon emission evaluation analysis on the intelligent energy operation data to obtain carbon emission level evaluation data; performing energy carbon reduction treatment on the carbon emission level evaluation data to obtain energy carbon reduction initial data; performing carbon reduction optimization adjustment on the energy carbon reduction initial data according to the potential influence factors of carbon reduction to obtain energy carbon reduction optimization data;
The comprehensive optimization model construction module is used for carrying out feature association analysis on the energy conservation allocation optimization data and the energy carbon reduction optimization data to obtain energy conservation carbon reduction optimization features; establishing an energy comprehensive optimization operation model according to the energy-saving carbon reduction optimization characteristics;
the model prediction processing module is used for carrying out energy optimization prediction processing on the intelligent energy operation data through the energy comprehensive optimization operation model so as to obtain an energy optimization operation prediction result; and executing a corresponding comprehensive optimization operation strategy according to the energy optimization operation prediction result.
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