CN114168583A - Electric quantity data cleaning method and system based on regular automatic encoder - Google Patents
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Abstract
The invention relates to an electric quantity data cleaning method and system based on a regular automatic encoder, wherein the method comprises the following steps: s1, reading original data of the intelligent ammeter in the transformer area; s2, calculating daily electricity consumption data of the user; s3, removing abnormal daily electric quantity data of the user; s4, dividing the user electricity data from the beginning of Monday to the end of Monday with 28 days as a period; s5, establishing a data filling model based on a regular automatic encoder; s6, counting the number of missing days of daily electricity consumption of the user, if the missing percentage is smaller than a set threshold value, performing S7, otherwise, not performing data filling work on the user; s7, inputting the missing user data into the data filling model based on the regular automatic encoder, and correcting and filling the electric quantity data of the user; and S8, filling missing data. The method and the system have small error of electric quantity data repair and improve data quality.
Description
Technical Field
The invention belongs to the field of electric power big data application, and particularly relates to an electric quantity data cleaning method and system based on a regular automatic encoder.
Background
The electric power big data mainly come from power generation, transmission of electricity, transformer, distribution, power consumption and scheduling links of electric power production and electric energy use, can gather each item operational data of electric power system through using intelligent terminal equipment such as smart electric meter, carry out systematic processing and analysis to the electric power big data of gathering again, can monitor, diagnose, optimize and predict the electric wire netting operation, provide the guarantee for electric wire netting safety, reliable, economy, high-efficient operation. The electric quantity data of the electricity customers is also an important component of the electric power big data. However, due to the complexity of the electricity consumption situation, the electricity quantity data may be abnormal or missing, which makes data analysis difficult. In the existing electric quantity data cleaning method, data repairing errors are large, and the accuracy of big data analysis is influenced.
Disclosure of Invention
The invention aims to provide an electric quantity data cleaning method and system based on a regular automatic encoder, which have small error of electric quantity data repair and improve data quality.
In order to achieve the purpose, the invention adopts the technical scheme that: a regular automatic encoder-based electric quantity data cleaning method comprises the following steps:
s1, reading original data of the intelligent ammeter in the transformer area;
s2, calculating daily electricity consumption data of the user;
s3, removing abnormal daily electric quantity data of the user;
s4, dividing the user electricity data from the beginning of Monday to the end of Monday with 28 days as a period;
s5, establishing a data filling model based on a regular automatic encoder;
s6, counting the number of missing days of daily electricity consumption of the user, if the missing percentage is smaller than a set threshold value, performing S7, otherwise, not performing data filling work on the user;
s7, inputting the missing user data into the data filling model based on the regular automatic encoder, and correcting and filling the electric quantity data of the user;
and S8, filling missing data.
Further, the step S2 specifically includes the following steps:
s21, distinguishing the electric meter data of different users according to the user numbers;
s22, sorting the electric meter data of each user according to date;
s23, judging whether the asset numbers of the electric meters in the next day are consistent with those in the previous day, if not, indicating that the user replaces the electric meter, and if so, executing the step S24, wherein the display data of the intelligent electric meter in the current day of the user is the current day electricity consumption of the user;
s24, judging whether numerical values exist in two days before and after the user, if one or two numerical values are lost, setting the electricity consumption of the user in the same day as null, namely data are lost, and if the two numerical values exist, executing the step S25;
and S25, subtracting the positive active power of the previous day from the next day of the intelligent electric meter to obtain the daily electric quantity data of the user.
Further, the step S3 specifically includes the following steps:
s31, according to the daily electricity consumption data of the user obtained in the step S2, if the data is a negative value, the data is regarded as abnormal, and the daily electricity consumption data of the user is set to be null;
and S32, according to the daily electricity consumption data of the users obtained in the step S2, if the electricity consumption data of all the users in the current day exist, but the summation value in the set error range is not equal to the total electricity consumption, the electricity consumption data of all the users in the current day are determined to be wrong, and the electricity consumption data of all the users in the current day are empty.
Further, the step S5 specifically includes the following steps:
s51, applying rule constraint to the loss function of the automatic encoder, embedding orthogonal constraint and L21 norm to realize the regularization of the automatic encoder, and then the target function expression of the regular automatic encoder to be optimized is as follows:
in the formula (1), N is the total number of samples, x is the input data,
for reconstructing data, v is the number of layers of the canonical autoencoder, σ is the coefficient of the L21 norm, β is the coefficient of the orthogonal constraint, I is the identity matrix, and W is the weight of the model;
s52, establishing a power utilization data training set containing various users;
s53, training the parameters of the regular automatic encoder by using a training set; calculating the electricity consumption data x and the reconstruction number according to the formula (1)According to
Judging whether the error meets the expectation, if so, saving the parameters of the regular automatic encoder, and if not, updating the parameters to perform encoding and decoding again;
s54: and saving the parameters of the optimal regular automatic encoder to obtain a data filling model based on the regular automatic encoder.
Further, in step S6, the threshold value is set to 40% of the total number of days.
The invention also provides an electric quantity data cleaning system based on the regular automatic encoder, which comprises a memory, a processor and a computer program instruction which is stored on the memory and can be run by the processor, wherein when the processor runs the computer program instruction, the steps of the method can be realized.
Compared with the prior art, the invention has the following beneficial effects: the method realizes regularization by adding L21 norm and orthogonal constraint to a loss function through a regular self-encoder, prevents a model from generating an overfitting phenomenon, and further improves the generalization capability of the model. The method can accurately extract the characteristics of the power consumption of the user, and fill up the missing data according to the characteristics, the error of the data repairing result is relatively small, and the method has a positive effect on the research and development of the correlation of the power data and the improvement of the data quality of a big data technology in the application of a power distribution network.
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FIG. 1 is a flow chart of a method implementation of an embodiment of the present invention.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
As shown in fig. 1, the present embodiment provides a method for cleaning power data based on a regular automatic encoder, which includes the following steps:
and S1, reading the original data of the district smart electric meter.
And S2, calculating the daily electricity consumption data of the user.
The step S2 specifically includes the following steps:
s21, distinguishing the electric meter data of different users according to the user numbers;
s22, sorting the electric meter data of each user according to date;
s23, judging whether the asset numbers of the electric meters in the next day are consistent with those in the previous day, if not, indicating that the user replaces the electric meter, and if so, executing the step S24, wherein the display data of the intelligent electric meter in the current day of the user is the current day electricity consumption of the user;
s24, judging whether numerical values exist in two days before and after the user, if one or two numerical values are lost, setting the electricity consumption of the user in the same day as null, namely data are lost, and if the two numerical values exist, executing the step S25;
and S25, subtracting the positive active power of the previous day from the next day of the intelligent electric meter to obtain the daily electric quantity data of the user.
And S3, removing abnormal daily electricity consumption data of the user.
The step S3 specifically includes the following steps:
s31, according to the daily electricity consumption data of the user obtained in the step S2, if the data is a negative value, the data is regarded as abnormal, and the daily electricity consumption data of the user is set to be null;
and S32, according to the daily electricity consumption data of the users obtained in the step S2, if the electricity consumption data of all the users in the current day exist, but the summation value in the set error range is not equal to the total electricity consumption, the electricity consumption data of all the users in the current day are determined to be wrong, and the electricity consumption data of all the users in the current day are empty.
S4, the user electricity data is divided from the beginning of monday to the end of sunday with 28 days as a cycle.
And S5, establishing a data filling model based on the regular automatic encoder.
The step S5 specifically includes the following steps:
s51, applying rule constraint to the loss function of the automatic encoder, embedding orthogonal constraint and L21 norm, and realizing the regularization of the common automatic encoder, wherein the target function expression of the regular automatic encoder to be optimized is as follows:
in the formula (1), N is the total number of samples, x is the input data,
for reconstructing data, v is the number of layers of the canonical autoencoder, σ is the coefficient of the L21 norm, β is the coefficient of the orthogonal constraint, I is the identity matrix, and W is the weight of the model;
s52, establishing a power utilization data training set containing various users;
and S6, counting the number of missing days of the daily electricity consumption of the user, if the missing percentage is smaller than a set threshold value, performing the step S7, otherwise, not performing data filling work on the user.
In the present embodiment, the threshold is set to 40% of the total number of days.
And S7, inputting the missing user data into the data padding model based on the regular automatic encoder, and correcting and padding the electric quantity data of the user.
And S8, filling missing data.
S53, use ofTraining parameters of a regular automatic encoder by a training set; calculating power utilization data x and reconstruction data according to formula (1)
Judging whether the error meets the expectation, if so, saving the parameters of the regular automatic encoder, and if not, updating the parameters to perform encoding and decoding again;
s54: and saving the parameters of the optimal regular automatic encoder to obtain a data filling model based on the regular automatic encoder.
The embodiment also provides an electric quantity data cleaning system based on the regular automatic encoder, which comprises a memory, a processor and computer program instructions stored on the memory and capable of being executed by the processor, wherein when the computer program instructions are executed by the processor, the steps of the method can be realized.
In this embodiment, the data padding is performed by using the method and a method based on a normal self-encoder and a noise reduction self-encoder, and the effect is shown in table 1.
TABLE 1 comparison of the data filling effect of the present method with the prior art
In table 1, the E function is represented by:
in the formula (2), xijIs the true value, xi′jTo fill in values, M is the set of all missing values.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is directed to preferred embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow. However, any simple modification, equivalent change and modification of the above embodiments according to the technical essence of the present invention are within the protection scope of the technical solution of the present invention.
Claims (6)
1. A regular automatic encoder-based electric quantity data cleaning method is characterized by comprising the following steps:
s1, reading original data of the intelligent ammeter in the transformer area;
s2, calculating daily electricity consumption data of the user;
s3, removing abnormal daily electric quantity data of the user;
s4, dividing the user electricity data from the beginning of Monday to the end of Monday with 28 days as a period;
s5, establishing a data filling model based on a regular automatic encoder;
s6, counting the number of missing days of daily electricity consumption of the user, if the missing percentage is smaller than a set threshold value, performing S7, otherwise, not performing data filling work on the user;
s7, inputting the missing user data into the data filling model based on the regular automatic encoder, and correcting and filling the electric quantity data of the user;
and S8, filling missing data.
2. The regular automatic encoder-based electric quantity data cleaning method according to claim 1, wherein the step S2 specifically comprises the following steps:
s21, distinguishing the electric meter data of different users according to the user numbers;
s22, sorting the electric meter data of each user according to date;
s23, judging whether the asset numbers of the electric meters in the next day are consistent with those in the previous day, if not, indicating that the user replaces the electric meter, and if so, executing the step S24, wherein the display data of the intelligent electric meter in the current day of the user is the current day electricity consumption of the user;
s24, judging whether numerical values exist in two days before and after the user, if one or two numerical values are lost, setting the electricity consumption of the user in the same day as null, namely data are lost, and if the two numerical values exist, executing the step S25;
and S25, subtracting the positive active power of the previous day from the next day of the intelligent electric meter to obtain the daily electric quantity data of the user.
3. The regular automatic encoder-based electric quantity data cleaning method according to claim 1, wherein the step S3 specifically comprises the following steps:
s31, according to the daily electricity consumption data of the user obtained in the step S2, if the data is a negative value, the data is regarded as abnormal, and the daily electricity consumption data of the user is set to be null;
and S32, according to the daily electricity consumption data of the users obtained in the step S2, if the electricity consumption data of all the users in the current day exist, but the summation value in the set error range is not equal to the total electricity consumption, the electricity consumption data of all the users in the current day are determined to be wrong, and the electricity consumption data of all the users in the current day are empty.
4. The regular automatic encoder-based electric quantity data cleaning method according to claim 1, wherein the step S5 specifically comprises the following steps:
s51, applying rule constraint to the loss function of the automatic encoder, embedding orthogonal constraint and L21 norm to realize the regularization of the automatic encoder, and then the target function expression of the regular automatic encoder to be optimized is as follows:
in the formula (1), N is the total number of samples, x is the input data,
for reconstructing data, v is the number of layers of the canonical autoencoder, σ is the coefficient of the L21 norm, β is the coefficient of the orthogonal constraint, I is the identity matrix, and W is the weight of the model;
s52, establishing a power utilization data training set containing various users;
s53, training the parameters of the regular automatic encoder by using a training set; calculating power utilization data x and reconstruction data according to formula (1)
Judging whether the error meets the expectation, if so, saving the parameters of the regular automatic encoder, and if not, updating the parameters to perform encoding and decoding again;
s54: and saving the parameters of the optimal regular automatic encoder to obtain a data filling model based on the regular automatic encoder.
5. The regular automatic encoder based electric quantity data cleaning method as claimed in claim 1, wherein in the step S6, the threshold value is set to 40% of the total days.
6. A regular autoencoder based electrical quantity data cleaning system comprising a memory, a processor and computer program instructions stored on the memory and executable by the processor, the computer program instructions when executed by the processor being operable to perform the method steps of claims 1-5.
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