CN111768020A - Customer electricity demand identification method based on SVM algorithm - Google Patents
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Abstract
The invention relates to the technical field of electric power, and particularly discloses a customer electricity demand identification method based on an SVM algorithm, which comprises the following steps: acquiring power consumption demand information of a customer to be identified, and generating a power consumption demand sample for storage; extracting a power consumption demand sample, and performing data analysis to obtain a training sample; performing SVM training on at least one training sample based on an SVM algorithm to obtain an SVM model; receiving a customer power demand request and inputting the customer power demand request into an SVM model for recognition to obtain recognition data; and classifying the identification data and then sharing. According to the method, the training samples are obtained by performing data analysis on the extracted power consumption demand samples based on the SVM algorithm, the training samples are trained by the SVM to obtain the SVM model, so that the power consumption demand requests of customers can be input into the SVM model for recognition, and the problem that the analysis and prediction accuracy is reduced due to the fact that the power consumption demands of the customers cannot be recognized is solved.
Description
Technical Field
The invention relates to the technical field of electric power, in particular to a customer electricity demand identification method based on an SVM algorithm.
Background
Nowadays, with the continuous development of power technology and the continuous increase of power utilization equipment, the demand of people for power utilization is also continuously increased. However, because the power customers have a large difference, the power demands of different customers have a large difference, and even the same customer has different power demands in different time periods.
In order to ensure that a power grid enterprise plans more accurately for future power generation, data are generally obtained by analyzing according to the power consumption requirements of customers, and a power generation plan is made according to the data to meet the power consumption requirements of different customers. The Chinese patent with publication number CN106447108A discloses an electricity demand analysis and prediction method considering business expansion data, and the method accurately judges the electricity quantity through a quantitative capacity release rule and the relation between the installation capacity and the electricity quantity of a user. However, the technical scheme cannot identify the electricity demand of the customer, and the accuracy of analysis and prediction is further reduced. Therefore, designing a customer electricity demand identification method based on an SVM algorithm becomes a problem which needs to be solved urgently.
Disclosure of Invention
The invention aims to provide a customer electricity demand identification method based on an SVM algorithm so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme:
the customer electricity demand identification method based on the SVM algorithm comprises the following steps:
acquiring power consumption demand information of a customer to be identified, and generating a power consumption demand sample for storage; the storage is realized by adopting a data storage module, and the power consumption demand sample is stored through the data storage module, so that the power consumption demand sample can be prevented from being lost due to power failure, and the safety is effectively improved;
extracting a power consumption demand sample, and performing data analysis to obtain a training sample;
performing SVM training on at least one training sample based on an SVM algorithm (support vector machine algorithm) to obtain an SVM model; the SVM model needs to perform average relative error calculation and mean square relative error calculation to determine the identification accuracy of the SVM model, and the identification accuracy can be effectively improved by increasing the identification accuracy;
receiving a customer power demand request and inputting the customer power demand request into an SVM model for recognition to obtain recognition data;
and classifying the identification data and then sharing.
As a further scheme of the invention: the data analysis is to classify the data information (including electricity consumption, electricity frequency, electricity time, electricity type and the like) in the customer electricity demand sample to obtain a training sample.
As a still further scheme of the invention: the information classification adopts a K nearest neighbor classification algorithm, specifically, each electricity demand sample can be represented by K neighbors which are closest to the electricity demand sample, if most of K nearest neighbor samples of one sample in a feature space belong to a certain class, the sample also belongs to the class and has the characteristics of the samples in the class; the method determines the category of the sample to be classified only according to the category of the nearest limited sample or limited samples in the determination of the classification decision, thereby effectively improving the classification efficiency.
As a still further scheme of the invention: the sharing comprises the following steps: receiving a sharing request of a client; extracting identification data of a corresponding client and compressing the identification data to generate a data packet; acquiring a data packet, decompressing the data packet to generate picture data, and outputting the picture data to the mobile terminal for displaying; the power consumption requirements and specific power consumption conditions of customers can be visually reflected through the picture data, the picture data can comprise the total power consumption of the customers and the power consumption acceleration of specific power utilization equipment, and the predicted power demand, wherein the predicted power demand needs to consider seasonal factors to confirm the predicted value of the power consumption.
As a still further scheme of the invention: the mobile terminal is one of a smart phone, a tablet computer, an MP4 player, a notebook computer or a desktop computer.
As a still further scheme of the invention: the customer power consumption requirement identification method based on the SVM algorithm further comprises the steps of generating a newly-increased power consumption requirement sample according to power consumption requirement information of a newly-increased customer, inputting the newly-increased power consumption requirement sample to the SVM model to conduct SVM training to update the SVM model, and effectively keeping the real-time performance of the identification process through updating the SVM model.
Compared with the prior art, the invention has the beneficial effects that:
according to the method, the training samples are obtained by performing data analysis on the extracted power consumption demand samples based on the SVM algorithm, the training samples are trained by the SVM to obtain the SVM model, so that the power consumption demand requests of customers can be input into the SVM model for recognition, and the problem that the analysis and prediction accuracy is reduced due to the fact that the power consumption demands of the customers cannot be recognized is solved.
Drawings
Fig. 1 is a flowchart illustrating an implementation of a customer electricity demand identification method based on an SVM algorithm according to an embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the figures and specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that variations and modifications can be made by persons skilled in the art without departing from the spirit of the invention. All falling within the scope of the present invention.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
Referring to fig. 1, in an embodiment of the present invention, a method for identifying a customer power demand based on an SVM algorithm includes:
in step 101, acquiring power demand information of a customer to be identified, generating a power demand sample and storing the power demand sample; the storage is realized by adopting a data storage module, and the power consumption requirement sample is stored through the data storage module, so that the power consumption requirement sample can be prevented from being lost due to power failure, and the safety is effectively improved;
wherein the data storage module may be implemented by any type of volatile or non-volatile memory device or combination thereof, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory ((ROM), magnetic memory, flash memory, magnetic or optical disk;
the data storage module can also be a cloud storage, and is specifically set according to actual requirements, which is not limited herein; the cloud storage is accessed through a Web service Application Program Interface (API) or a Web user interface, the cloud storage is a mode of storage on the Internet, namely, data are stored in a plurality of virtual servers which are generally managed by a third party, but not exclusive servers, and the data occupation space can be effectively reduced through the cloud storage, so that the work efficiency is improved; the data is stored in the memory or the cloud server for backup, so that the safety of data storage is effectively improved, data loss caused by memory damage is further prevented, and the data backup method has a wide market prospect;
in step 102, extracting a power consumption requirement sample, and performing data analysis to obtain a training sample;
in step 103, performing SVM training on at least one training sample based on an SVM algorithm to obtain an SVM model;
in step 104, receiving a customer power demand request and inputting the customer power demand request into an SVM model for recognition to obtain recognition data;
in step 105, the identification data is classified and then shared.
Referring to fig. 1, in another embodiment of the present invention, a customer electricity demand identification method based on SVM algorithm includes the following steps:
acquiring power consumption demand information of a customer to be identified, and generating a power consumption demand sample for storage; the storage is realized by adopting a data storage module, and the power consumption demand sample is stored through the data storage module, so that the power consumption demand sample can be prevented from being lost due to power failure, and the safety is effectively improved;
the data storage module may be implemented by any type of volatile or non-volatile storage device or combination thereof, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory ((ROM), magnetic memory, flash memory, magnetic disk or optical disk, which is set according to practical requirements and is not limited herein;
extracting a power consumption demand sample, and performing data analysis to obtain a training sample;
the method comprises the steps that SVM training is carried out on at least one training sample based on an SVM algorithm to obtain an SVM model, the SVM model needs to carry out average relative error calculation and mean square relative error calculation to determine the recognition accuracy of the SVM model, and the accuracy of recognition of customer electricity demand can be effectively improved by increasing the recognition accuracy;
receiving a customer power demand request and inputting the customer power demand request into an SVM model for recognition to obtain recognition data;
and classifying the identification data and then sharing.
Further, in the embodiment of the present invention, the data analysis is to classify the data information (including power consumption, power frequency, power utilization time, power utilization type, etc.) in the customer power demand sample to obtain the training sample.
Further, in the embodiment of the present invention, the power consumption may be a power consumption preset by a customer, a remaining power consumption of the customer, a total power consumption of a single power consumption device of the customer, and the like, and the power consumption device may be a computer, an air conditioner, a refrigerator, a washing machine, a microwave oven, a printer, a facsimile machine, or the like; the electricity frequency may be a time interval of each continuous use of electricity in a certain time, the electricity time may be a time of each continuous use of electricity, and the electricity type may be industrial electricity, residential electricity, or the like.
It should be noted that the information classification adopts a K nearest neighbor classification algorithm, specifically, each power consumption demand sample can be represented by its nearest K neighbors, and if most of K nearest neighbor samples of a sample in the feature space belong to a certain class, the sample also belongs to the class and has the characteristics of the sample in the class; the method determines the category of the sample to be classified only according to the category of the nearest limited sample or limited samples in the determination of the classification decision, thereby effectively improving the classification efficiency.
Further, in the embodiment of the present invention, the sharing includes the following steps:
receiving a sharing request of a client;
extracting identification data of a corresponding client and compressing the identification data to generate a data packet;
acquiring a data packet, decompressing the data packet to generate picture data, and outputting the picture data to the mobile terminal for displaying; the power consumption requirements and specific power consumption conditions of customers can be intuitively reflected through picture data, and the picture data can comprise the total power consumption of the customers, the power consumption acceleration of specific power consumption equipment and predicted power demand; the predicted power demand needs to consider seasonal factors to determine the predicted value of the power consumption.
Further, in the embodiment of the present invention, the mobile terminal is one of a smart phone, a tablet computer, an MP4 player, a notebook computer, or a desktop computer; specifically, the picture data is transmitted to the mobile terminal through the wireless network by the computer to be displayed, when a sharing request of a client is received, the identity of the client needs to be verified according to the received sharing request of the client, whether identification data of the corresponding client is extracted and compressed to generate a data packet is determined according to whether the client accords with the safety certification information, if the client does not accord with the safety certification information, secondary confirmation is carried out, if the client does not accord with the safety certification information, the identification data of the corresponding client is refused to be extracted and compressed to generate the data packet, and the identification data of the corresponding client is encrypted and stored.
Furthermore, in the embodiment of the invention, the customer electricity demand identification method based on the SVM algorithm further comprises the steps of generating a newly-added electricity demand sample according to the electricity demand information of the newly-added customer, inputting the newly-added electricity demand sample to the SVM model to perform SVM training to update the SVM model, and effectively keeping the real-time performance of the identification process by updating the SVM model.
It should be noted that, as one of ordinary skill in the art would understand, all or part of the steps in the method for implementing the above embodiments may be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the program is executed by a processor, the program may include the procedures of the embodiments of the methods as described above. The storage medium may be a random access memory, a flash memory, a read only memory, a programmable read only memory, an electrically erasable programmable memory, a register, etc.
In another embodiment provided by the present invention, the processor, when executing the computer program, further performs the following steps:
acquiring power consumption demand information of a customer to be identified, and generating a power consumption demand sample for storage;
extracting a power consumption demand sample, and performing data analysis to obtain a training sample;
performing SVM training on at least one training sample based on an SVM algorithm to obtain an SVM model;
receiving a customer power demand request and inputting the customer power demand request into an SVM model for recognition to obtain recognition data;
and classifying the identification data and then sharing.
In another embodiment provided by the present invention, the processor, when executing the computer program, further performs the following steps:
receiving a sharing request of a client;
extracting identification data of a corresponding client and compressing the identification data to generate a data packet;
and acquiring the data packet, decompressing the data packet to generate picture data, and outputting the picture data to the mobile terminal for displaying.
The steps in the above-described embodiment methods are not limited to be performed in the exact order provided they are explicitly described herein, and may be performed in other orders. Moreover, at least some of the steps are not necessarily performed at the same time, but may be performed at different times, and these steps are not necessarily performed in sequence, but may be performed alternately or alternately with other steps.
The invention has the beneficial effects that: according to the method, the training samples are obtained by performing data analysis on the extracted power consumption demand samples based on the SVM algorithm, the training samples are trained by the SVM to obtain the SVM model, so that the power consumption demand requests of customers can be input into the SVM model for recognition, and the problem that the analysis and prediction accuracy is reduced due to the fact that the power consumption demands of the customers cannot be recognized is solved.
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
While the preferred embodiments of the present invention have been illustrated and described in detail, it is to be understood that the invention is not limited thereto, and that various changes and modifications may be effected therein by one skilled in the art without departing from the spirit and scope of the invention. And are neither required nor exhaustive of all embodiments. And obvious variations or modifications are intended to be covered by the present application.
Claims (7)
1. The customer electricity demand identification method based on the SVM algorithm is characterized by comprising the following steps of:
acquiring power consumption demand information of a customer to be identified, and generating a power consumption demand sample for storage;
extracting a power consumption demand sample, and performing data analysis to obtain a training sample;
performing SVM training on at least one training sample based on an SVM algorithm to obtain an SVM model;
receiving a customer power demand request and inputting the customer power demand request into an SVM model for recognition to obtain recognition data;
and classifying the identification data and then sharing.
2. The SVM algorithm-based customer power demand recognition method according to claim 1, wherein the data analysis is to classify data information in a customer power demand sample to obtain a training sample.
3. The SVM algorithm-based customer electricity demand recognition method according to claim 2, wherein the information classification employs a K-nearest neighbor classification algorithm.
4. A customer electricity demand recognition method based on SVM algorithm according to any of claims 1-3, characterized in that said sharing comprises the following steps:
receiving a sharing request of a client;
extracting identification data of a corresponding client and compressing the identification data to generate a data packet;
and acquiring the data packet, decompressing the data packet to generate picture data, and outputting the picture data to the mobile terminal for displaying.
5. The SVM algorithm-based customer power demand identification method according to claim 4, wherein the mobile terminal is one of a smartphone, a tablet computer, an MP4 player, a laptop computer or a desktop computer.
6. The SVM algorithm-based customer power demand recognition method according to claim 5, wherein the mobile terminal is a smart phone.
7. The SVM algorithm-based customer power demand identification method according to claim 6, further comprising generating a newly added power demand sample according to power demand information of the newly added customer, and inputting the newly added power demand sample to an SVM model for SVM training to update the SVM model.
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