CN115471122A - Energy consumption evaluation method and system based on metadata model - Google Patents
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Abstract
The invention discloses an energy consumption evaluation method and system based on a metadata model, and relates to the field of energy consumption evaluation, wherein the method comprises the following steps: acquiring a plurality of energy consumption units in a target area; obtaining a plurality of identification information and a plurality of energy consumption information; obtaining a plurality of energy consumption information ranges; obtaining a plurality of energy consumption identification information according to a plurality of energy consumption information ranges; constructing an energy consumption evaluation model by adopting a plurality of identification information and a plurality of energy consumption identification information; acquiring energy consumption information of a plurality of current target energy consumption units, and acquiring a plurality of target identification information and a plurality of real-time energy consumption identification information; and inputting the target identification information and the real-time energy consumption identification information into an energy consumption evaluation model to obtain an energy consumption evaluation result. The method solves the technical problem that the accuracy of energy consumption assessment is not high in the prior art, and therefore the energy consumption assessment effect is poor. The technical effects of improving the accuracy of energy consumption assessment, further improving the quality of energy consumption assessment and the like are achieved.
Description
Technical Field
The invention relates to the field of energy consumption evaluation, in particular to an energy consumption evaluation method and system based on a metadata model.
Background
With the development of science and technology and the improvement of the living standard of people, the energy consumption market is continuously expanded. Meanwhile, the energy consumption density is continuously increased, the energy spreading is more and more serious, a huge energy-saving space is provided, and energy conservation and consumption reduction are imperative. Energy consumption assessment has a very important influence on the setting of energy-saving measures, energy management, energy utilization and the like. How to evaluate energy consumption with high quality is a great concern.
In the prior art, the technical problem that the energy consumption evaluation effect is poor due to low accuracy of energy consumption evaluation exists.
Disclosure of Invention
The application provides an energy consumption evaluation method and system based on a metadata model. The technical problem that in the prior art, the accuracy of energy consumption assessment is not high, and therefore the energy consumption assessment effect is not good is solved.
In view of the foregoing problems, the present application provides a method and a system for energy consumption evaluation based on a metadata model.
In a first aspect, the present application provides a metadata model-based energy consumption assessment method, where the method is applied to a metadata model-based energy consumption assessment system, and the method includes: acquiring a plurality of energy consumption units in a target area; acquiring identification information and energy consumption information of the energy consumption units to obtain a plurality of identification information and a plurality of energy consumption information; compensating the energy consumption information to obtain energy consumption information ranges; according to the energy consumption information ranges, marking different energy consumption information to obtain a plurality of energy consumption marking information; constructing an energy consumption evaluation model by adopting the identification information and the energy consumption identification information; acquiring energy consumption information of a plurality of current target energy consumption units, and acquiring a plurality of target identification information and a plurality of real-time energy consumption identification information; and inputting the target identification information and the real-time energy consumption identification information into the energy consumption evaluation model to obtain an energy consumption evaluation result.
In a second aspect, the present application further provides a metadata model-based energy consumption assessment system, where the system includes: the energy consumption unit acquisition module is used for acquiring a plurality of energy consumption units in a target area; the information acquisition module is used for acquiring identification information and energy consumption information of the energy consumption units to obtain a plurality of identification information and a plurality of energy consumption information; the information compensation module is used for compensating the energy consumption information to obtain a plurality of energy consumption information ranges; the information identification module is used for identifying different energy consumption information according to the energy consumption information ranges to obtain a plurality of energy consumption identification information; the building module is used for building an energy consumption evaluation model by adopting the identification information and the energy consumption identification information; the target information acquisition module is used for acquiring and acquiring energy consumption information of a plurality of current target energy consumption units to acquire a plurality of target identification information and a plurality of real-time energy consumption identification information; and the energy consumption evaluation module is used for inputting the target identification information and the real-time energy consumption identification information into the energy consumption evaluation model to obtain an energy consumption evaluation result.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
analyzing the energy consumption units of the target area to obtain a plurality of energy consumption units in the target area; acquiring identification information and energy consumption information of a plurality of energy consumption units to obtain a plurality of identification information and a plurality of energy consumption information; compensating the energy consumption information to obtain energy consumption information ranges; marking different energy consumption information according to a plurality of energy consumption information ranges to obtain a plurality of energy consumption marking information; constructing an energy consumption evaluation model by adopting a plurality of identification information and a plurality of energy consumption identification information; acquiring energy consumption information of a plurality of current target energy consumption units, and acquiring a plurality of target identification information and a plurality of real-time energy consumption identification information; and inputting the target identification information and the real-time energy consumption identification information into an energy consumption evaluation model to obtain an energy consumption evaluation result. The accuracy of energy consumption evaluation is improved, and the quality of energy consumption evaluation is improved; the intelligent, efficient and reliable energy consumption assessment is achieved, and therefore the technical effect of providing data reference for improving the energy management level, improving the energy utilization efficiency and mining the energy-saving potential is achieved.
Drawings
FIG. 1 is a schematic flowchart illustrating a method for evaluating energy consumption based on a metadata model according to the present application;
FIG. 2 is a schematic diagram illustrating a process of obtaining a plurality of energy consumption identification information in an energy consumption assessment method based on a metadata model according to the present application;
FIG. 3 is a schematic flow chart illustrating a process of constructing an energy consumption estimation model in the metadata model-based energy consumption estimation method according to the present application;
fig. 4 is a schematic structural diagram of an energy consumption estimation system based on a metadata model according to the present application.
Description of the reference numerals: the energy consumption unit monitoring system comprises an energy consumption unit obtaining module 11, an information collecting module 12, an information compensating module 13, an information identification module 14, a building module 15, a target information obtaining module 16 and an energy consumption evaluation module 17.
Detailed Description
The application provides an energy consumption evaluation method and system based on a metadata model. The technical problem that in the prior art, the accuracy of energy consumption assessment is not high, and therefore the energy consumption assessment effect is not good is solved. The accuracy of energy consumption evaluation is improved, and the quality of energy consumption evaluation is improved; the intelligent, efficient and reliable energy consumption assessment is realized, and therefore the technical effects of improving the energy management level, improving the energy utilization efficiency and providing data reference for exploiting the energy-saving potential are achieved.
Example one
Referring to fig. 1, the present application provides a metadata model-based energy consumption assessment method, where the method is applied to a metadata model-based energy consumption assessment system, and the method specifically includes the following steps:
step S100: acquiring a plurality of energy consumption units in a target area;
step S200: acquiring identification information and energy consumption information of the energy consumption units to obtain a plurality of identification information and a plurality of energy consumption information;
specifically, energy consumption analysis is performed on a target area to obtain a plurality of energy consumption units, information acquisition is performed on the plurality of energy consumption units to obtain a plurality of identification information and a plurality of energy consumption information. The target area can be an industrial park, a factory and the like which perform intelligent energy consumption assessment by using the energy consumption assessment system based on the metadata model. The energy consumption units comprise a plurality of energy consumption devices such as air conditioners, lighting devices, water utilization devices, elevators and energy consumption devices for production lines in a target area. The plurality of identification information includes data information such as device types, specification parameters, positions and the like corresponding to the plurality of energy consumption units. The energy consumption information comprises real-time energy consumption parameter information such as real-time electricity consumption and real-time water consumption corresponding to the energy consumption units. The technical effects that the target area is subjected to energy consumption analysis, a plurality of energy consumption units, a plurality of identification information and a plurality of energy consumption information are obtained, and a foundation is laid for subsequent energy consumption evaluation of the target area are achieved.
Step S300: compensating the energy consumption information to obtain energy consumption information ranges;
further, step S300 of the present application further includes:
step S310: acquiring energy consumption information of a plurality of previous time nodes of the energy consumption units to obtain a plurality of historical energy consumption information sets;
step S320: according to the plurality of historical energy consumption information sets, obtaining the times of different energy consumption information appearing in the plurality of historical energy consumption information sets, and obtaining a plurality of times information sets;
step S330: obtaining a preset time threshold;
step S340: extracting the frequency information meeting the preset frequency threshold in the multiple frequency information sets to obtain multiple high-frequency information sets;
step S350: obtaining a plurality of high-frequency energy consumption information sets according to the plurality of high-frequency times information sets and the plurality of historical energy consumption information sets;
step S360: and compensating the energy consumption information according to the range of the historical energy consumption information in the high-frequency energy consumption information sets to obtain the energy consumption information ranges.
Specifically, historical energy consumption information is acquired for a plurality of energy consumption units based on a plurality of previous time nodes, a plurality of historical energy consumption information sets are obtained, the times of occurrence of different energy consumption information in the plurality of historical energy consumption information sets are counted, and a plurality of times information sets are obtained. Furthermore, each time information in the multiple time information sets is compared with a preset time threshold value, whether each time information in the multiple time information sets meets the preset time threshold value is judged, and the time information meeting the preset time threshold value in the multiple time information sets is added to the multiple high-frequency time information sets. And then, matching the plurality of historical energy consumption information sets based on the plurality of high-frequency times information sets to obtain a plurality of high-frequency energy consumption information sets, and matching the plurality of energy consumption information according to the range of the historical energy consumption information in the plurality of high-frequency energy consumption information sets to obtain a plurality of energy consumption information ranges. Wherein, the previous time nodes can be a plurality of historical times determined by adaptive setting before 1 day, 1 week, 1 month and the like. The plurality of historical energy consumption information sets comprise a plurality of historical energy consumption information corresponding to a plurality of energy consumption units under a plurality of previous time nodes. The plurality of times information sets comprise times information of occurrence of different energy consumption information in a plurality of historical energy consumption information sets. The preset time threshold value can be set and determined in a self-adaptive mode according to the accuracy requirements of a plurality of energy consumption information ranges. The plurality of high-frequency number information sets comprise number information meeting a preset number threshold in the plurality of number information sets. The plurality of high-frequency energy consumption information sets comprise a plurality of historical energy consumption information sets corresponding to a plurality of high-frequency times information sets. The plurality of energy consumption information ranges includes a range of historical energy consumption information within a plurality of high frequency energy consumption information sets corresponding to the plurality of energy consumption information. The technical effects that a plurality of reliable high-frequency energy consumption information sets are obtained by performing historical energy consumption information acquisition and high-frequency historical energy consumption information analysis on a plurality of energy consumption units, and a plurality of energy consumption information ranges are obtained by compensating the plurality of energy consumption information sets according to the high-frequency energy consumption information sets, so that the accuracy of energy consumption evaluation on the plurality of energy consumption units is improved are achieved.
Step S400: according to the energy consumption information ranges, marking different energy consumption information to obtain a plurality of energy consumption marking information;
further, as shown in fig. 2, step S400 of the present application further includes:
step S410: extracting energy consumption information according to the energy consumption information ranges to obtain a plurality of basic energy consumption information;
step S420: obtaining a plurality of energy consumption information description metadata;
step S430: and identifying the plurality of basic energy consumption information by adopting the plurality of energy consumption information description metadata to obtain the plurality of energy consumption identification information.
Specifically, energy consumption information is extracted based on a plurality of energy consumption information ranges, and a plurality of pieces of basic energy consumption information are obtained. Furthermore, based on the plurality of pieces of basic energy consumption information, a plurality of pieces of energy consumption information description metadata are collected, and the plurality of pieces of basic energy consumption information are identified according to the plurality of pieces of energy consumption information description metadata, so that a plurality of pieces of energy consumption identification information are obtained. Wherein the plurality of base energy consumption information includes a plurality of energy consumption information satisfying a plurality of energy consumption information ranges. The plurality of energy consumption information description metadata comprise a plurality of binary information corresponding to the plurality of basic energy consumption information, and the plurality of binary information comprise use environments, data attributes and storage positions of the plurality of basic energy consumption information, and data parameters such as themes, contents and characteristics for describing and explaining the plurality of basic energy consumption information. The plurality of energy consumption information description metadata are mainly used for basically knowing and knowing the plurality of basic energy consumption information, so that the plurality of basic energy consumption information can be retrieved, positioned, managed, analyzed and evaluated, and the convenience degree of utilizing the plurality of basic energy consumption information is improved. The plurality of energy consumption identification information includes a plurality of pieces of basic energy consumption information identified by a plurality of pieces of energy consumption information description metadata. The technical effects that the multiple pieces of basic energy consumption information are adaptively identified according to the multiple pieces of energy consumption information description metadata, the multiple pieces of accurate energy consumption identification information are obtained, and therefore the efficiency of subsequently constructing the energy consumption evaluation model is improved are achieved.
Step S500: constructing an energy consumption evaluation model by adopting the identification information and the energy consumption identification information;
further, step S500 of the present application further includes:
step S510: according to the energy consumption units, randomly selecting different basic energy consumption information to be combined to obtain a plurality of energy consumption information combination results;
step S520: performing energy consumption evaluation on the multiple energy consumption information combination results to obtain multiple sample energy consumption evaluation results;
step S530: acquiring a plurality of energy consumption evaluation result description metadata, and identifying the plurality of sample energy consumption evaluation results to acquire a plurality of evaluation result identification information;
specifically, based on a plurality of energy consumption units, random selection and combination of different basic energy consumption information are performed on a plurality of obtained basic energy consumption information, a plurality of energy consumption information combination results are obtained, energy consumption evaluation is performed on the energy consumption information combination results, and a plurality of sample energy consumption evaluation results are obtained. Furthermore, based on the energy consumption evaluation results of the multiple samples, the energy consumption evaluation result description metadata are collected, the energy consumption evaluation results of the multiple samples are identified according to the obtained energy consumption evaluation result description metadata, and identification information of the multiple evaluation results is obtained. The combined result of the plurality of energy consumption information includes a plurality of different pieces of basic energy consumption information corresponding to the plurality of energy consumption units in the plurality of pieces of basic energy consumption information. The energy consumption evaluation results of the plurality of samples comprise energy consumption evaluation parameter information such as energy consumption evaluation total amount, energy consumption evaluation cost, energy consumption evaluation trend, energy consumption evaluation proportion, energy consumption evaluation utilization rate and the like corresponding to the energy consumption information combination results. The energy consumption evaluation result description metadata comprises a plurality of binary information corresponding to the energy consumption evaluation results of the samples, and the binary information comprises the use environments, data attributes and storage positions of the energy consumption evaluation results of the samples, and data parameters such as topics, contents and characteristics for describing and explaining the energy consumption evaluation results of the samples. The plurality of evaluation result identification information includes a plurality of sample energy consumption evaluation results identified using a plurality of energy consumption evaluation result description metadata. The technical effects that the energy consumption evaluation results of the samples are identified through the energy consumption evaluation result description metadata to obtain identification information of the evaluation results, and the energy consumption evaluation model compaction foundation is subsequently constructed are achieved.
Step S540: and constructing the energy consumption evaluation model by adopting the identification information, the energy consumption identification information and the evaluation result identification information.
Further, as shown in fig. 3, step S540 of the present application further includes:
step S541: constructing a network structure of the energy consumption evaluation model based on a BP neural network, wherein input data of the energy consumption evaluation model are identification information and energy consumption identification information, and output data are evaluation result identification information;
step S542: performing data identification and division on the identification information, the energy consumption identification information and the evaluation result identification information to obtain a constructed data set;
step S543: and carrying out supervision training, verification and testing on the energy consumption evaluation model by adopting the constructed data set until the accuracy of the energy consumption evaluation model meets the preset requirement, and obtaining the constructed energy consumption evaluation model.
Specifically, a constructed data set is obtained by performing data identification and division on a plurality of identification information, a plurality of energy consumption identification information and a plurality of evaluation result identification information. The construction data set comprises an identification information training set, an energy consumption identification information training set, an evaluation result identification information training set, an identification information test set, an energy consumption identification information test set and an evaluation result identification information test set. And the identification information training set, the energy consumption identification information training set and the evaluation result identification information training set have a corresponding relationship, and the identification information test set, the energy consumption identification information test set and the evaluation result identification information test set have a corresponding relationship. Further, a network structure of the energy consumption evaluation model is constructed based on the BP neural network, and the energy consumption evaluation model is obtained by performing supervision training on the identification information training set and the energy consumption identification information training set in the constructed data set. And when the accuracy of the energy consumption evaluation model meets the preset requirement, namely the similarity between the identification information training set, the output information corresponding to the energy consumption identification information training set and the evaluation result identification information training set meets the preset requirement, finishing the cross supervision training. And then, taking the identification information test set and the energy consumption identification information test set in the constructed data set as input information, inputting an energy consumption evaluation model, and verifying the energy consumption evaluation model. And when the accuracy of the energy consumption evaluation model meets the preset requirement, namely the similarity between the identification information test set, the output information corresponding to the energy consumption identification information test set and the evaluation result identification information test set meets the preset requirement, obtaining the energy consumption evaluation model with the accuracy meeting the preset requirement. The BP neural network is a multi-layer feedforward neural network trained according to an error back propagation algorithm. The BP neural network comprises an input layer, a plurality of layers of neurons and an output layer. The BP neural network can perform forward calculation and backward calculation. During forward calculation, input information is processed layer by layer from an input layer through a plurality of layers of neurons, the input information is turned to an output layer, and the state of each layer of neurons only affects the state of the next layer of neurons. If the expected output can not be obtained in the output layer, the reverse calculation is carried out, the error signal is returned along the original connecting path, and the weight of each neuron is modified to ensure that the error signal is minimum. The network structure of the energy consumption evaluation model comprises an input layer, a plurality of layers of neurons and an output layer, and meets the requirement of a BP neural network. The input data of the energy consumption evaluation model comprises identification information and energy consumption identification information, and the output data comprises evaluation result identification information. The accuracy of the energy consumption evaluation model comprises the similarity between the identification information training set, the output information corresponding to the energy consumption identification information training set and the evaluation result identification information training set, and the similarity between the identification information testing set, the output information corresponding to the energy consumption identification information testing set and the evaluation result identification information testing set. The preset requirements comprise preset accuracy, and the preset accuracy can be set and determined in a user-defined mode according to the accuracy requirement of the energy consumption evaluation model. The technical effect that the energy consumption assessment model is supervised, trained, verified and tested by constructing a data set, the energy consumption assessment model with accuracy meeting preset requirements, high adaptability and good generalization performance is obtained, and therefore the accuracy of energy consumption assessment is improved is achieved.
Step S600: acquiring energy consumption information of a plurality of current target energy consumption units, and acquiring a plurality of target identification information and a plurality of real-time energy consumption identification information;
further, step S600 of the present application further includes:
step S610: acquiring energy consumption information of a plurality of current target energy consumption units to acquire a plurality of pieces of real-time energy consumption information;
step S620: according to the plurality of real-time energy consumption information and the plurality of high-frequency energy consumption information sets, carrying out abnormity detection on the plurality of real-time energy consumption information, and judging whether the plurality of real-time energy consumption information is abnormal data;
further, step S620 in the present application further includes:
step S621: dividing the plurality of real-time energy consumption information and the plurality of high-frequency energy consumption information sets according to the plurality of target energy consumption units to obtain a plurality of energy consumption information sets;
step S622: respectively constructing a plurality of energy consumption information anomaly detection trees according to the energy consumption information sets, wherein each energy consumption information anomaly detection tree comprises a multi-stage division node and an abnormal data output node;
step S623: respectively inputting the plurality of real-time energy consumption information sets and the plurality of high-frequency energy consumption information sets into the plurality of energy consumption information anomaly detection trees to obtain a plurality of anomaly detection results;
step S624: and judging whether the real-time energy consumption information is abnormal data or not according to the abnormal detection results.
Step S630: and if the energy consumption identification information is not acquired, acquiring the real-time energy consumption identification information according to the real-time energy consumption information and the energy consumption identification information.
Specifically, random selection and manual setting are performed on the basis of the plurality of energy consumption units, the plurality of target energy consumption units are obtained, current energy consumption information is acquired for the plurality of target energy consumption units, and the plurality of real-time energy consumption information is obtained. And then, dividing the real-time energy consumption information sets and the high-frequency energy consumption information sets based on the target energy consumption units to obtain energy consumption information sets. Further, based on the isolated forest algorithm idea, a plurality of energy consumption information anomaly detection trees are constructed, specifically, a plurality of energy consumption information in a plurality of energy consumption information sets are randomly selected respectively to obtain a first division threshold value, a second division threshold value of \823030A, a N division threshold value, the first division threshold value of 8230A, the second division threshold value of \8230A, the N division threshold value is set to be a first-level division node, a second-level division node of \8230A, a Nth-level division node and other multi-level division nodes of each energy consumption information anomaly detection tree in the plurality of energy consumption information anomaly detection trees, and abnormal data output nodes are set according to the multi-level division nodes, half of the multi-level division nodes can be set to be abnormal data output nodes, and single real-time energy consumption information output by the abnormal data output nodes and the division nodes below the abnormal data output nodes is an anomaly detection result. And acquiring a plurality of energy consumption information anomaly detection trees based on the multi-level division nodes and the anomaly data output nodes. And then, a plurality of sets of real-time energy consumption information and high-frequency energy consumption information are used as input information, a plurality of energy consumption information abnormity detection trees are input, and a plurality of abnormity detection results are obtained. Further, based on the plurality of abnormal detection results, whether the plurality of real-time energy consumption information is abnormal data or not is judged, that is, whether the plurality of abnormal detection results correspond to the plurality of real-time energy consumption information or not is judged, and if the plurality of real-time energy consumption information is abnormal data, the energy consumption information of the plurality of target energy consumption units is collected again. And if the plurality of pieces of real-time energy consumption information are not abnormal data, matching and identifying the plurality of pieces of real-time energy consumption information based on the plurality of pieces of energy consumption identification information to obtain the plurality of pieces of real-time energy consumption identification information, and matching the plurality of target energy consumption units according to the obtained plurality of pieces of identification information to obtain the plurality of pieces of target identification information.
The target energy consumption units comprise a plurality of energy consumption units which are randomly selected or artificially set from the energy consumption units. The plurality of real-time energy consumption information comprises current energy consumption information corresponding to a plurality of target energy consumption units. Each energy consumption information set in the plurality of energy consumption information sets comprises real-time energy consumption information and a high-frequency energy consumption information set corresponding to a plurality of target energy consumption units. Each energy consumption information abnormity detection tree in the plurality of energy consumption information abnormity detection trees comprises a plurality of stages of division nodes and abnormal data output nodes. The plurality of abnormal detection results comprise a plurality of real-time energy consumption information, a plurality of high-frequency energy consumption information sets and energy consumption information corresponding to abnormal data output nodes and dividing nodes below the abnormal data output nodes. The plurality of real-time energy consumption identification information comprise a plurality of real-time energy consumption information which is obtained by matching and identifying the plurality of real-time energy consumption information according to the plurality of energy consumption identification information. The plurality of target identification information includes a plurality of identification information corresponding to a plurality of target energy consuming units. The technical effects that the real-time energy consumption information is subjected to abnormal detection through the energy consumption information abnormal detection trees, so that reliable real-time energy consumption identification information is obtained, and the reliability of energy consumption evaluation is improved are achieved.
Step S700: and inputting the target identification information and the real-time energy consumption identification information into the energy consumption evaluation model to obtain an energy consumption evaluation result.
Specifically, a plurality of target identification information and a plurality of real-time energy consumption identification information are used as input information, an energy consumption evaluation model is input, and the energy consumption evaluation model is used for matching the plurality of target identification information and the plurality of real-time energy consumption identification information with a plurality of evaluation result identification information to obtain an energy consumption evaluation result. The energy consumption evaluation result comprises a plurality of target identification information and evaluation result identification information corresponding to a plurality of real-time energy consumption identification information. The technical effects of accurately and efficiently evaluating the target identification information and the real-time energy consumption identification information through the energy consumption evaluation model, obtaining a reliable energy consumption evaluation result and improving the quality of energy consumption evaluation are achieved.
In summary, the energy consumption evaluation method based on the metadata model provided by the present application has the following technical effects:
1. analyzing the energy consumption units of the target area to obtain a plurality of energy consumption units in the target area; acquiring identification information and energy consumption information of a plurality of energy consumption units to obtain a plurality of identification information and a plurality of energy consumption information; compensating the energy consumption information to obtain energy consumption information ranges; marking different energy consumption information according to a plurality of energy consumption information ranges to obtain a plurality of energy consumption marking information; constructing an energy consumption evaluation model by adopting a plurality of identification information and a plurality of energy consumption identification information; acquiring energy consumption information of a plurality of current target energy consumption units, and acquiring a plurality of target identification information and a plurality of real-time energy consumption identification information; and inputting the plurality of target identification information and the plurality of real-time energy consumption identification information into the energy consumption evaluation model to obtain an energy consumption evaluation result. The accuracy of energy consumption evaluation is improved, and the quality of energy consumption evaluation is improved; the intelligent, efficient and reliable energy consumption assessment is realized, and therefore the technical effects of improving the energy management level, improving the energy utilization efficiency and providing data reference for exploiting the energy-saving potential are achieved.
2. The energy consumption units are subjected to historical energy consumption information acquisition and high-frequency historical energy consumption information analysis to obtain a plurality of reliable high-frequency energy consumption information sets, and a plurality of energy consumption information are compensated according to the high-frequency energy consumption information sets to obtain a plurality of accurate energy consumption information ranges, so that the accuracy of energy consumption evaluation on the energy consumption units is improved.
3. The energy consumption assessment model is supervised, trained, verified and tested by constructing a data set, so that the energy consumption assessment model with accuracy meeting preset requirements, strong adaptability and good generalization performance is obtained, and the accuracy of energy consumption assessment is improved.
4. And carrying out anomaly detection on the real-time energy consumption information through the energy consumption information anomaly detection trees, thereby obtaining a plurality of reliable real-time energy consumption identification information and improving the reliability of energy consumption evaluation.
Example two
Based on the energy consumption evaluation method based on the metadata model in the foregoing embodiment, the same inventive concept, the present invention further provides an energy consumption evaluation system based on the metadata model, please refer to fig. 4, where the system includes:
the energy consumption unit acquiring module 11, where the energy consumption unit acquiring module 11 is configured to acquire a plurality of energy consumption units in a target area;
the information acquisition module 12 is configured to acquire identification information and energy consumption information of the plurality of energy consumption units, and obtain a plurality of identification information and a plurality of energy consumption information;
the information compensation module 13, the information compensation module 13 is configured to compensate the multiple energy consumption information, and obtain multiple energy consumption information ranges;
the information identification module 14, the information identification module 14 is configured to identify different energy consumption information according to the plurality of energy consumption information ranges, and obtain a plurality of energy consumption identification information;
a building module 15, wherein the building module 15 is configured to build an energy consumption evaluation model by using the plurality of identification information and the plurality of energy consumption identification information;
the target information acquisition module 16, where the target information acquisition module 16 is configured to acquire and acquire energy consumption information of a plurality of current target energy consumption units, and acquire a plurality of target identification information and a plurality of real-time energy consumption identification information;
and the energy consumption evaluation module 17 is configured to input the plurality of target identification information and the plurality of real-time energy consumption identification information into the energy consumption evaluation model, so as to obtain an energy consumption evaluation result.
Further, the system further comprises:
the historical energy consumption information set determining module is used for acquiring energy consumption information of a plurality of previous time nodes of the energy consumption units to obtain a plurality of historical energy consumption information sets;
the number information set determining module is used for obtaining the number of times that different energy consumption information appears in the historical energy consumption information sets according to the historical energy consumption information sets to obtain a plurality of number information sets;
the frequency threshold setting module is used for obtaining a preset frequency threshold;
the high-frequency time information set determining module is used for extracting the time information meeting the preset time threshold value in the multiple time information sets to obtain multiple high-frequency time information sets;
the high-frequency energy consumption information set determining module is used for obtaining a plurality of high-frequency energy consumption information sets according to the plurality of high-frequency times information sets and the plurality of historical energy consumption information sets;
and the energy consumption information range determining module is used for compensating the energy consumption information according to the range of the historical energy consumption information in the high-frequency energy consumption information sets to obtain the energy consumption information ranges.
Further, the system further comprises:
the basic energy consumption information determining module is used for extracting energy consumption information according to the plurality of energy consumption information ranges to obtain a plurality of basic energy consumption information;
an energy consumption information description metadata determination module, configured to obtain a plurality of energy consumption information description metadata;
and the energy consumption identification information determining module is used for identifying the plurality of basic energy consumption information by adopting the plurality of energy consumption information description metadata to obtain the plurality of energy consumption identification information.
Further, the system further comprises:
the energy consumption information combination result determining module is used for randomly selecting different basic energy consumption information to be combined according to the energy consumption units to obtain a plurality of energy consumption information combination results;
the sample energy consumption evaluation result determining module is used for carrying out energy consumption evaluation on the multiple energy consumption information combination results to obtain multiple sample energy consumption evaluation results;
the evaluation result identification information determining module is used for acquiring a plurality of energy consumption evaluation result description metadata, identifying the plurality of sample energy consumption evaluation results and acquiring a plurality of evaluation result identification information;
and the energy consumption evaluation model determining module is used for constructing the energy consumption evaluation model by adopting the identification information, the energy consumption identification information and the evaluation result identification information.
Further, the system further comprises:
the network structure determining module is used for constructing a network structure of the energy consumption evaluation model based on a BP neural network, wherein input data of the energy consumption evaluation model are identification information and energy consumption identification information, and output data of the energy consumption evaluation model are evaluation result identification information;
a constructed data set determining module, configured to perform data identification and division on the multiple identification information, the multiple energy consumption identification information, and the multiple evaluation result identification information to obtain a constructed data set;
the first execution module is used for performing supervision training, verification and testing on the energy consumption evaluation model by adopting the constructed data set until the accuracy of the energy consumption evaluation model meets a preset requirement, and obtaining the constructed energy consumption evaluation model.
Further, the system further comprises:
the real-time energy consumption information determining module is used for acquiring energy consumption information of a plurality of current target energy consumption units to obtain a plurality of pieces of real-time energy consumption information;
the abnormality judgment module is used for carrying out abnormality detection on the plurality of pieces of real-time energy consumption information according to the plurality of pieces of real-time energy consumption information and the plurality of high-frequency energy consumption information sets, and judging whether the plurality of pieces of real-time energy consumption information are abnormal data or not;
and the second execution module is used for acquiring the energy consumption information of the target energy consumption units again if the energy consumption information of the target energy consumption units is positive, and acquiring the real-time energy consumption identification information according to the real-time energy consumption information and the energy consumption identification information if the energy consumption information of the target energy consumption units is negative.
Further, the system further comprises:
the energy consumption information set determining module is used for dividing the plurality of real-time energy consumption information sets and the plurality of high-frequency energy consumption information sets according to the plurality of target energy consumption units to obtain a plurality of energy consumption information sets;
the energy consumption information anomaly detection tree determining module is used for respectively constructing a plurality of energy consumption information anomaly detection trees according to the energy consumption information sets, wherein each energy consumption information anomaly detection tree comprises a multi-stage division node and an abnormal data output node;
an anomaly detection result determining module, configured to input the multiple sets of real-time energy consumption information and the multiple sets of high-frequency energy consumption information into the multiple energy consumption information anomaly detection trees, respectively, to obtain multiple anomaly detection results;
and the third execution module is used for judging whether the real-time energy consumption information is abnormal data or not according to the abnormal detection results.
The application provides an energy consumption assessment method based on a metadata model, wherein the method is applied to an energy consumption assessment system based on the metadata model, and the method comprises the following steps: performing energy consumption unit analysis on the target area to obtain a plurality of energy consumption units in the target area; acquiring identification information and energy consumption information of a plurality of energy consumption units to obtain a plurality of identification information and a plurality of energy consumption information; compensating the energy consumption information to obtain energy consumption information ranges; marking different energy consumption information according to a plurality of energy consumption information ranges to obtain a plurality of energy consumption marking information; constructing an energy consumption evaluation model by adopting a plurality of identification information and a plurality of energy consumption identification information; acquiring energy consumption information of a plurality of current target energy consumption units, and acquiring a plurality of target identification information and a plurality of real-time energy consumption identification information; and inputting the plurality of target identification information and the plurality of real-time energy consumption identification information into the energy consumption evaluation model to obtain an energy consumption evaluation result. The technical problem that in the prior art, the accuracy of energy consumption assessment is not high, and therefore the energy consumption assessment effect is not good is solved. The accuracy of energy consumption evaluation is improved, and the quality of energy consumption evaluation is improved; the intelligent, efficient and reliable energy consumption assessment is realized, and therefore the technical effects of improving the energy management level, improving the energy utilization efficiency and providing data reference for exploiting the energy-saving potential are achieved.
All possible combinations of the technical features in the above embodiments may not be described for the sake of brevity, but should be considered as being within the scope of the present disclosure as long as there is no contradiction between the combinations of the technical features.
The specification and drawings are merely illustrative of the present application, and it is intended that the present invention cover modifications and variations of this invention provided they come within the scope of the invention and their equivalents.
Claims (8)
1. A method for energy consumption assessment based on a metadata model, the method comprising:
acquiring a plurality of energy consumption units in a target area;
acquiring identification information and energy consumption information of the energy consumption units to obtain a plurality of identification information and a plurality of energy consumption information;
compensating the energy consumption information to obtain energy consumption information ranges;
according to the energy consumption information ranges, marking different energy consumption information to obtain a plurality of energy consumption marking information;
constructing an energy consumption evaluation model by adopting the identification information and the energy consumption identification information;
acquiring energy consumption information of a plurality of current target energy consumption units, and acquiring a plurality of target identification information and a plurality of real-time energy consumption identification information;
and inputting the target identification information and the real-time energy consumption identification information into the energy consumption evaluation model to obtain an energy consumption evaluation result.
2. The method of claim 1, wherein compensating the plurality of energy consumption information comprises:
acquiring energy consumption information of a plurality of previous time nodes of the energy consumption units to obtain a plurality of historical energy consumption information sets;
according to the plurality of historical energy consumption information sets, obtaining the times of different energy consumption information appearing in the plurality of historical energy consumption information sets, and obtaining a plurality of times information sets;
obtaining a preset time threshold;
extracting the frequency information meeting the preset frequency threshold in the multiple frequency information sets to obtain multiple high-frequency information sets;
obtaining a plurality of high-frequency energy consumption information sets according to the plurality of high-frequency times information sets and the plurality of historical energy consumption information sets;
and compensating the energy consumption information according to the range of historical energy consumption information in the high-frequency energy consumption information sets to obtain the energy consumption information ranges.
3. The method of claim 2, wherein identifying different energy consumption information according to the plurality of energy consumption information ranges comprises:
extracting energy consumption information according to the energy consumption information ranges to obtain a plurality of basic energy consumption information;
obtaining a plurality of energy consumption information description metadata;
and identifying the plurality of basic energy consumption information by adopting the plurality of energy consumption information description metadata to obtain the plurality of energy consumption identification information.
4. The method of claim 3, wherein using the plurality of identification information and the plurality of energy consumption identification information to construct an energy consumption assessment model comprises:
according to the energy consumption units, randomly selecting different basic energy consumption information to be combined to obtain a plurality of energy consumption information combination results;
performing energy consumption evaluation on the multiple energy consumption information combination results to obtain multiple sample energy consumption evaluation results;
acquiring a plurality of energy consumption evaluation result description metadata, and identifying the plurality of sample energy consumption evaluation results to acquire a plurality of evaluation result identification information;
and constructing the energy consumption evaluation model by adopting the identification information, the energy consumption identification information and the evaluation result identification information.
5. The method of claim 4, wherein constructing the energy consumption assessment model using the plurality of identification information, the plurality of energy consumption identification information, and the plurality of assessment result identification information comprises:
constructing a network structure of the energy consumption evaluation model based on a BP neural network, wherein input data of the energy consumption evaluation model are identification information and energy consumption identification information, and output data are evaluation result identification information;
performing data identification and division on the identification information, the energy consumption identification information and the evaluation result identification information to obtain a constructed data set;
and carrying out supervision training, verification and testing on the energy consumption evaluation model by adopting the constructed data set until the accuracy of the energy consumption evaluation model meets the preset requirement, and obtaining the constructed energy consumption evaluation model.
6. The method of claim 5, wherein the collecting and obtaining energy consumption information of the plurality of current target energy consumption units further comprises:
acquiring energy consumption information of a plurality of current target energy consumption units to acquire a plurality of pieces of real-time energy consumption information;
according to the plurality of pieces of real-time energy consumption information and the plurality of sets of high-frequency energy consumption information, carrying out anomaly detection on the plurality of pieces of real-time energy consumption information, and judging whether the plurality of pieces of real-time energy consumption information are anomalous data or not;
if so, acquiring the energy consumption information of the target energy consumption units again, and if not, acquiring the real-time energy consumption identification information according to the real-time energy consumption information and the energy consumption identification information.
7. The method of claim 6, wherein performing anomaly detection on the plurality of real-time energy consumption information comprises:
dividing the plurality of real-time energy consumption information and the plurality of high-frequency energy consumption information sets according to the plurality of target energy consumption units to obtain a plurality of energy consumption information sets;
respectively constructing a plurality of energy consumption information abnormality detection trees according to the energy consumption information sets, wherein each energy consumption information abnormality detection tree comprises a multistage division node and an abnormal data output node;
respectively inputting the plurality of real-time energy consumption information sets and the plurality of high-frequency energy consumption information sets into the plurality of energy consumption information anomaly detection trees to obtain a plurality of anomaly detection results;
and judging whether the real-time energy consumption information is abnormal data or not according to the abnormal detection results.
8. A metadata model-based energy consumption assessment system, the system comprising:
the energy consumption unit acquisition module is used for acquiring a plurality of energy consumption units in a target area;
the information acquisition module is used for acquiring the identification information and the energy consumption information of the energy consumption units to obtain a plurality of identification information and a plurality of energy consumption information;
the information compensation module is used for compensating the energy consumption information to obtain a plurality of energy consumption information ranges;
the information identification module is used for identifying different energy consumption information according to the energy consumption information ranges to obtain a plurality of energy consumption identification information;
the building module is used for building an energy consumption evaluation model by adopting the identification information and the energy consumption identification information;
the target information acquisition module is used for acquiring and acquiring energy consumption information of a plurality of current target energy consumption units, and acquiring a plurality of target identification information and a plurality of real-time energy consumption identification information;
and the energy consumption evaluation module is used for inputting the target identification information and the real-time energy consumption identification information into the energy consumption evaluation model to obtain an energy consumption evaluation result.
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| CN115600014B (en) * | 2022-12-14 | 2024-02-20 | 浙江万胜智能科技股份有限公司 | Personalized power distribution configuration method and system based on electricity utilization characteristics |
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