CN114742153A - Power utilization behavior analysis method based on one graph of power distribution network - Google Patents
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Abstract
The invention discloses a power utilization behavior analysis method and device based on one graph of a power distribution network, electronic equipment, a medium and a product. The method comprises the following steps: acquiring power utilization data of a plurality of users in a plurality of months through one map of a power distribution grid in a county; performing cluster analysis on the power utilization data through a preset clustering algorithm to obtain a plurality of power utilization curves of different types, wherein one power utilization curve is formed by one type of power utilization data; determining a corresponding target prediction model aiming at the power utilization curve of each category; and predicting the electricity utilization data of the category of the target user according to the target prediction model to obtain a monthly electricity utilization data prediction result of the target user so as to obtain the electricity utilization behavior of the target user according to the prediction result. By the method, the monthly power utilization behavior of the user can be analyzed and predicted.
Description
Technical Field
The embodiment of the invention relates to the technical field of smart power grids, in particular to a power utilization behavior analysis method based on one graph of a power distribution network.
Background
In recent years, in the face of a strongly increasing power demand in the field of intelligent power utilization, analyzing and predicting power utilization data of users is an important task.
The power consumption data adopted in the traditional user power consumption data analysis method is not targeted, so that the problems of large calculation amount, low analysis efficiency and the like are caused; most of traditional user electricity consumption data analysis methods analyze and predict the electricity consumption of users by adopting curves formed by daily power data, but the daily electricity consumption curves cannot well reflect the overall electricity consumption rules of the users, so that the analysis is inaccurate, the prediction accuracy is low, and the electricity consumption habits of the users cannot be well reflected.
Disclosure of Invention
The invention provides a power consumption behavior analysis method based on one map of a power distribution network, which aims at analyzing power consumption data obtained through one map of a county power grid so as to overcome the defect that the power consumption data adopted in the prior art is not targeted; by constructing corresponding target prediction models for different types of power consumption curves, the problem of inaccurate power consumption behavior analysis caused by low prediction accuracy when power consumption is predicted through one prediction model in the prior art is solved.
According to one aspect of the invention, a power consumption behavior analysis method based on one graph of a power distribution network is provided, and the method comprises the following steps:
acquiring power utilization data of a plurality of users in a plurality of months through one map of a power distribution grid in a county;
performing cluster analysis on the power utilization data through a preset clustering algorithm to obtain a plurality of power utilization curves of different types, wherein one power utilization curve is formed by one type of power utilization data;
determining a corresponding target prediction model aiming at the power utilization curve of each category;
and predicting the electricity utilization data of the category of the target user according to the target prediction model to obtain a monthly electricity utilization data prediction result of the target user so as to determine the electricity utilization behavior of the target user according to the prediction result.
According to another aspect of the present invention, there is provided a power consumption behavior analysis device based on a map of a power distribution network, including:
the acquisition module is used for acquiring the power utilization data of a plurality of users in a plurality of months through one map of the power distribution grid in a county;
the clustering module is used for clustering and analyzing the electricity utilization data through a preset clustering algorithm to obtain a plurality of electricity utilization curves of different types, and one electricity utilization curve is formed by one type of electricity utilization data;
the determining module is used for determining a corresponding target prediction model according to the power utilization curve of each category;
and the analysis module is used for predicting the electricity utilization data of the target user in the category to which the target user belongs according to the target prediction model to obtain a monthly electricity utilization data prediction result of the target user so as to determine the electricity utilization behavior of the target user according to the prediction result.
According to another aspect of the present invention, there is provided an electronic apparatus including: at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the electricity usage data analysis method according to any one of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer-readable storage medium, the computer
The readable storage medium stores computer instructions for causing the processor to implement the power consumption data analysis method according to any one of the embodiments of the present invention when the computer instructions are executed.
According to the technical scheme of the embodiment of the invention, the corresponding target prediction models are constructed for the power consumption curves of different types, so that the problem of low prediction accuracy when the power consumption is analyzed and predicted through one prediction model in the prior art is solved, and the beneficial effect of improving the prediction accuracy is achieved.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a power consumption behavior analysis method based on a diagram of a power distribution network according to a first embodiment of the present invention;
fig. 2 is an exemplary flowchart of a power consumption behavior analysis method based on a diagram of a power distribution network according to a second embodiment of the present invention;
fig. 3 is a comparison diagram of clustering effects corresponding to different clustering numbers provided in the second embodiment of the present invention;
FIG. 4a is a schematic diagram of a first type of power consumption data curve according to a second embodiment of the present invention;
FIG. 4b is a diagram illustrating a second type of power consumption data curve according to the second embodiment of the present invention;
FIG. 4c is a schematic diagram of a third type electricity consumption data curve according to the second embodiment of the present invention;
FIG. 4d is a schematic diagram of a fourth type electricity consumption data curve according to the second embodiment of the present invention;
fig. 4e is a schematic diagram of a fifth type electricity consumption data curve provided by the second embodiment of the present invention;
fig. 4f is a schematic diagram of a sixth type electricity consumption data curve according to the second embodiment of the present invention;
fig. 5 is a schematic structural diagram of a power consumption behavior analysis apparatus based on one diagram of a power distribution network according to a third embodiment of the present invention;
fig. 6 is a schematic structural diagram of an electronic device of a power consumption behavior analysis method based on one diagram of a power distribution network according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention. It should be understood that the various steps recited in the method embodiments of the present invention may be performed in a different order and/or performed in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the invention is not limited in this respect.
The term "include" and variations thereof as used herein are open-ended, i.e., "including but not limited to". The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions for other terms will be given in the following description.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It is noted that references to "a", "an", and "the" modifications in the present invention are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that reference to "one or more" unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present invention are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
Example one
Fig. 1 is a schematic flow chart of a power consumption behavior analysis method based on a diagram of a power distribution network according to an embodiment of the present invention, where the method is applicable to a case of analyzing monthly power consumption behaviors of a user, and the method can be executed by a power consumption behavior analysis apparatus based on a diagram of a power distribution network, where the apparatus can be implemented by software and/or hardware and is generally integrated on an electronic device, where the electronic device in this embodiment includes, but is not limited to: a computer device.
As shown in fig. 1, a power consumption behavior analysis method based on one graph of a power distribution network according to an embodiment of the present invention includes the following steps:
and S110, acquiring power utilization data of a plurality of users in a plurality of months through one map of the power distribution grid in one county.
The county power distribution network one-map is constructed by using network structure data and operation data of each business department of the county power distribution system according to data characteristics of county power distribution companies through coupling of a topological model of a county power distribution network map-model integration technology and imported actual operation simulation data.
It should be noted that, in this embodiment, the electricity consumption data of the user is obtained after the user authorizes the electricity consumption data. The user may be an individual user, or a company or a factory may be used as a user.
The specific number of the multiple months is not specifically limited herein, and may be set arbitrarily, and exemplary users may obtain power consumption data of each user within 33 months, where the power consumption data of each month may be multiple categories of power consumption data.
In this embodiment, the original electricity consumption data can be obtained through one map of the power distribution grid in a county, and the electricity consumption data can be obtained after the original electricity consumption data is preprocessed.
Specifically, the acquiring of the power consumption data of a plurality of users in a plurality of months through one map of the power distribution grid in a county area includes: acquiring original power utilization data of a plurality of users in a plurality of months through one map of a power distribution grid in a county; and carrying out data cleaning and data transformation on the original power consumption data to obtain power consumption data of a plurality of users in a plurality of months.
In this embodiment, the data cleansing may include operations of removing null values, removing zero values, and removing outliers. The null value indicates that data is not acquired or not input due to machine failure or manual error of the field, and the user with the null value is processed in a record deleting mode; the zero value indicates that the user does not use electricity in the month corresponding to the field, and the electricity users related to the zero value are also processed in a record deleting mode; the abnormal value is a value less than 0 according to the actual service judgment, and the abnormal value is processed by adopting a mean filling method, namely the mean of 5 pieces of data before and after the abnormal value is used for replacing the abnormal value.
In this embodiment, since the dimensions of different features in different power consumption user data are not consistent, and the difference between values is large, a data conversion operation needs to be performed on the data. The data transformation operation provided by this embodiment is mainly data normalization transformation, and maps data to [0,1], so as to ensure the same numerical dimension, and the specific calculation formula is as follows:
wherein x ismaxMaximum sample value, x, representing all time instantsminMinimum sample value representing all time instants, x representing original sample value, xnewThe normalized values are shown.
In this embodiment, the electricity consumption data of the plurality of users obtained after the above operation for a plurality of months is X ═ X (X ═ X)1,x2,…,xn) Where n denotes the number of users, x1The multi-dimensional vector represents the electricity utilization data of the first user in a plurality of months, and one dimension represents the electricity utilization data of one month.
And S120, carrying out cluster analysis on the power utilization data through a preset cluster algorithm to obtain a plurality of power utilization curves of different types, wherein one power utilization curve is formed by one type of power utilization data.
The preset clustering algorithm may be any preset algorithm capable of performing clustering analysis, and for example, the preset clustering algorithm may be a K-means algorithm.
It can be understood that when the electricity consumption data is subjected to cluster analysis by using the K-means algorithm, the cluster number needs to be determined firstly, so that the electricity consumption data can be clustered according to the cluster number.
In this embodiment, the number of clusters may be directly obtained after being input by the user. For example, after the number of clusters is determined, the electricity consumption data can be subjected to cluster analysis through a K-means algorithm to obtain a plurality of different types of clusters, each cluster corresponds to one type of electricity consumption data, and one type of electricity consumption data can construct one type of electricity consumption curve.
Specifically, the power consumption data is subjected to clustering analysis through a preset clustering algorithm to obtain a plurality of power consumption curves of different categories, including: acquiring the clustering number input by a user, wherein the clustering number is determined according to the clustering effect of different clustering numbers and the real electricity service scene, the clustering effect of different clustering numbers is represented by the corresponding calculation result, one calculation result is determined by the clustering result corresponding to one clustering number, and the determination mode of each calculation result is the same; and performing cluster analysis on the electricity utilization data based on the cluster number input by the user and a preset clustering algorithm to obtain electricity utilization curves of different types, wherein the number of the electricity utilization curves of different types is the same as the cluster number input by the user.
The plurality of calculation results can be obtained by performing Euclidean distance calculation based on the clustering results corresponding to different clustering numbers.
Specifically, the determining manner of the one calculation result includes: performing cluster analysis on the electricity utilization data according to a cluster number and the preset cluster algorithm to obtain a cluster number of cluster clusters as a cluster result; determining the Euclidean distance sum from each sample point in each cluster to a cluster central point in each cluster; and adding the Euclidean distance sums corresponding to each cluster to obtain a calculation result.
In this embodiment, the process of obtaining the electricity curves of different categories by performing cluster analysis on the electricity data based on the cluster number K input by the user and a preset clustering algorithm may be as follows:
and 4, continuously repeating the step 2 and the step 3 until the maximum iteration times are reached to obtain K different cluster clusters, wherein each cluster represents a category of power utilization curve.
And S130, determining a corresponding target prediction model according to the power utilization curve of each category.
Wherein a class of power usage profiles may be used to determine a target prediction model. The target prediction model may be a time series prediction model, and the target prediction model may be obtained by optimizing an initial prediction model through a verification set, where the initial prediction model may be constructed based on a training set. Further, the determining a corresponding target prediction model for each category of power usage curve includes: constructing a corresponding training set and a verification set aiming at the power utilization curve of each category, wherein one training set comprises a plurality of samples and a label corresponding to each sample, one sample comprises power utilization data of one user in a continuous preset number of months, and the label is the power utilization data of the user in the next month of the continuous preset number of months; and determining a corresponding target prediction model according to the training set and the verification set.
In this embodiment, a corresponding sample set is first constructed for each type of power consumption curve, and then a corresponding training set and a verification set are determined from the corresponding sample set. The method for constructing a sample set may be as follows: acquiring power utilization data of a plurality of months of each user from a class of power utilization curves; the method comprises the steps of splitting power utilization data of multiple months of each user based on a preset power consumption prediction rule, and constructing a sample set containing samples and labels, wherein the preset power consumption prediction rule is used for predicting the power utilization data of the next month for the power utilization data of a continuous preset number of months before use. The way of determining the corresponding training set and the verification set from the corresponding sample set may be: and randomly extracting data according to a preset proportion of the training set to the verification set to construct the training set and the verification set.
Further, the determining a corresponding target prediction model according to the training set and the verification set includes: obtaining an initial prediction model based on the training set and a time recurrent neural network model; and optimizing the initial prediction model based on the verification set to obtain a target prediction model.
In this embodiment, a temporal recurrent neural network model LSTM is first constructed, and model parameters are initialized. Different model parameters are initialized respectively for a plurality of different power utilization categories.
In this embodiment, the process of determining the target model may include the following steps:
ft＝σ(Wf·[ht-1,xt]+hf)
it＝σ(Wi·[ht-1,xt]+bi)
ot＝σ(Wo·[ht-1,xt]+bo)
wherein f ist,it,otRespectively representing a forgetting gate, an input gate and an output gate; wf,Wi,Wc,WoRespectively represent different weights; b is a mixture off,bi,bc,boRespectively represent different bias terms;
indicating the unit state input at the current moment; c. CtRepresenting the state of the unit at the current moment; σ, tanh represents the activation function; h is a total oftThe hidden layer state representing the current time instant, i.e. the output state at the current time instant, contains all information about the input at the current time instant.
and 3, continuously iterating the step 1 and the step 2 until the maximum iteration times is reached, and taking a model obtained after the last iteration as a target prediction model.
The target prediction model corresponding to the power utilization curve of one category can be determined in the above mode, the target prediction models corresponding to the power utilization curves of different categories are determined in the same mode, and the target prediction model corresponding to the power utilization curve of each category can be determined in the above mode.
S140, according to the target prediction model, predicting the electricity utilization data of the class of the target user to obtain a monthly electricity utilization data prediction result of the target user, and determining the electricity utilization behavior of the target user according to the prediction result.
The target user can be understood as a user needing to predict monthly electricity consumption data.
In this embodiment, each target prediction model has a category to which it belongs, and it can be understood that a monthly electricity data prediction result of a target user can be obtained by inputting different categories of electricity data of the target user into the corresponding target prediction model.
After the monthly electricity data prediction result of the target user is obtained, the electricity utilization behavior of the target user can be further determined according to the monthly electricity data prediction result of the target user.
According to the power consumption behavior analysis method based on one map of the power distribution network, provided by the embodiment of the invention, firstly, power consumption data of a plurality of users in a plurality of months are obtained through one map of a power distribution network in a county area; then, carrying out cluster analysis on the electricity utilization data through a preset clustering algorithm to obtain a plurality of electricity utilization curves of different types, wherein one electricity utilization curve is formed by one type of electricity utilization data; then, determining a corresponding target prediction model for each type of power utilization curve; and finally, according to the target prediction model, predicting the electricity utilization data of the target user in the category to which the target user belongs to obtain a monthly electricity utilization data prediction result of the target user, and determining the electricity utilization behavior of the target user according to the prediction result. The method analyzes the power consumption data obtained by one map of the power grid in one county area so as to overcome the defect that the power consumption data adopted in the prior art is not targeted; by constructing corresponding target prediction models for different types of power consumption curves, the problem of inaccurate power consumption behavior analysis caused by low prediction accuracy when power consumption is predicted through one prediction model in the prior art is solved.
Example two
The second embodiment of the present invention provides a specific implementation manner based on the technical solutions of the above embodiments.
Exemplarily, fig. 2 is an exemplary flowchart of a power consumption behavior analysis method based on one diagram of a power distribution network according to a second embodiment of the present invention, and as shown in fig. 2, the method includes the following steps: acquiring original power consumption data; cleaning, denoising and normalizing original electricity utilization data; clustering analysis is carried out by using a K-means algorithm, and the processed electricity utilization data are clustered into 6 types of data; constructing corresponding LSTM prediction models aiming at different types of electricity utilization data; and predicting the electricity utilization data of the category according to the LSTM prediction model to obtain a prediction result, and determining the electricity utilization behavior of the target user according to the prediction result.
The original data mainly comes from a 'one-picture power distribution network' in a county area, and the data access mainly leads various power consumption data from the 'one-picture power distribution network' into a data analysis domain storage system for the calculation and analysis process. Illustratively, the accessed primary data includes: organization ID, user number, user name, monthly electricity consumption data from the user in 2019, month 1 to 2021, month 9. The data come from real electricity consumption data in a certain place and city, and the total number is 6626.
Table 1 is an original data sample table provided in the second embodiment of the present invention, where table 1 includes power consumption data of 4 users in different months.
Table 1 original data sample table
In this embodiment, since the number of clusters needs to be known when the K-means algorithm is used for cluster analysis, the number of clusters is determined by the user according to the real service scene and the clustering effect corresponding to different numbers of clusters, and then is input into the electronic device.
The clustering effect corresponding to different clustering numbers is represented by a calculation result, and the calculation mode of the calculation result is as follows: and selecting the clustering numbers from 1 to 13 to perform clustering analysis respectively through a K-means algorithm, calculating the sum of Euclidean distances from each point in each clustering cluster to a clustering central point after the clustering analysis is finished, and comparing the Euclidean distance sums corresponding to different clustering numbers. Fig. 3 is a comparison graph of clustering effects corresponding to different numbers of clusters provided in the second embodiment of the present invention, and as shown in fig. 3, the abscissa is the number of clusters, i.e., the number of clusters, from 1 to 13, and the ordinate is the sum of points in the clusters to the central point, i.e., the calculation result, which is used to characterize the clustering effect, and the smaller the numerical value of the calculation result, the better the clustering effect.
In the present exemplary embodiment, the cluster number is finally determined to be 6, and the electricity consumption data of different users are divided into 6 categories, as shown in tables 2 to 7.
TABLE 2 class 1 consumer electricity usage data
TABLE 3 class 2 consumer electricity consumption data
Table 4 type 3 consumer electricity consumption data
TABLE 5 class 4 consumer electricity data
TABLE 6 TYPE 5 USER ELECTRICITY-USE DATA
TABLE 7 type 6 consumer electricity data
Table 8 is a statistical table of the power consumption data amounts of each category provided in the second embodiment of the present invention, as shown in table 8. 579 electricity consumption data belong to category 1; 667 electricity consumption data belong to Category 2; 975 electricity usage data belong to category 3; 764 electricity usage data belonging to category 4; 696 electricity consumption data belong to category 5; 837 electricity usage data belong to category 6.
Further, selecting a clustering center of each category and drawing an electricity utilization curve, wherein fig. 4a is a schematic diagram of a first-category electricity utilization data curve provided by a second embodiment of the invention; FIG. 4b is a diagram illustrating a second type of power consumption data curve according to the second embodiment of the present invention; FIG. 4c is a schematic diagram of a third type electricity consumption data curve according to the second embodiment of the present invention; FIG. 4d is a schematic diagram of a fourth type electricity consumption data curve according to the second embodiment of the present invention; fig. 4e is a schematic diagram of a fifth type electricity consumption data curve provided by the second embodiment of the present invention; fig. 4f is a schematic diagram of a sixth type electricity consumption data curve provided by the second embodiment of the present invention. As shown in fig. 4a to 4f, the power consumption data curves of the 6 types have respective distribution trends. Therefore, time series prediction models are respectively established for 6 different types of power utilization data curves, so that power consumption prediction of users is better performed.
Further, a data set is constructed based on the electricity usage data in the different classes of electricity usage curves. Table 9 is a sample set data table corresponding to category 1 provided in example two of the present invention, where X is power consumption data of the first 12 months and Y is power consumption data of the last 1 month, as shown in table 9.
TABLE 9 sample set data sheet for Category 1
Based on the constructed sample set, adopting a training set: and (4) verification set: and randomly extracting data according to the ratio of 7:1.5:1.5 in the test set, and finally constructing a training set, a verification set and a test set required by the model.
In this embodiment, a time series model is constructed using LSTM, and table 10 is a table of relevant parameters of the model provided in the second embodiment of the present invention.
TABLE 10 model-related parameters Table
In the embodiment, a deep learning framework pytorech is adopted to build and train a neural network model, the gradient descent algorithm is batch gradient descent, and the optimization mechanism is an Adam optimization algorithm. And verifying and adjusting the optimization model by using the verification set in the model training process. And after the model training is finished, performing model testing by adopting the test set.
For the model test effect, the mean square error MSE, the mean absolute error MAE and the absolute coefficient R2 are used as evaluation indexes to evaluate the model effect. And (3) comparing the prediction effects obtained in two modes of 'directly predicting the power consumption by using the LSTM without clustering' and 'predicting the power consumption by using the LSTM after clustering and aggregating into 6 types of power consumption categories'. Table 11 is a comparison table of the model prediction effects corresponding to the two methods provided in the second embodiment of the present invention, and it can be known from table 11 that the prediction effect is better when the original data is clustered and then the power consumption is predicted.
TABLE 11 comparison table of model prediction effects corresponding to two modes
Table 12 is a table of comparison of the relevant evaluation indexes of categories 2 to 6, and the prediction effect of the prediction model corresponding to the electricity consumption data of each category can be compared based on the data in table 12.
TABLE 12 COMPARATIVE TABLE OF RELATED EVANT INDICATIONS OF CLASS 2 TO CLASS 6
According to the electricity consumption data analysis method provided by the embodiment of the invention, the important application of the big data of the power distribution and utilization system in the aspects of improving the safe and economic operation level of the power distribution network, improving the customer service level, optimizing and consuming clean energy and the like is fully exerted based on the intelligent analysis method for the electricity consumption behavior of the user of 'one diagram of the power distribution and utilization network', and meanwhile, a foundation is laid for the marketized operation of the power distribution and utilization and the construction of a comprehensive energy system; the user power utilization rule is analyzed by adopting a clustering algorithm, the user types can be divided according to different point rules, and the power utilization characteristic analysis can be matched with actual users. In addition, the problem of non-ideal model effect caused by the existence of noise data in the original data can be avoided; the user electricity utilization prediction analysis model is established by utilizing technologies such as deep learning and data mining, electricity consumption in a future period is predicted, and an electric power department can conveniently and effectively manage electricity supply and demand quantity, so that electricity waste is reduced.
EXAMPLE III
Fig. 5 is a schematic structural diagram of a power consumption behavior analysis method based on one diagram of a power distribution network according to a third embodiment of the present invention, where the apparatus is applicable to a case of analyzing monthly power consumption behaviors of a user, and the apparatus may be implemented by software and/or hardware and is generally integrated on an electronic device.
As shown in fig. 5, the apparatus includes: an acquisition module 110, a clustering module 120, a determination module 130, and an analysis module 140.
The acquiring module 110 is configured to acquire power consumption data of a plurality of users in a plurality of months through a single map of a power distribution grid in a county;
the clustering module 120 is configured to perform clustering analysis on the power consumption data through a preset clustering algorithm to obtain a plurality of power consumption curves of different categories, where one power consumption curve is formed by one category of power consumption data;
a determining module 130, configured to determine, for each category of power usage curve, a corresponding target prediction model;
and the analysis module 140 is configured to predict the electricity consumption data of the category to which the target user belongs according to the target prediction model to obtain a monthly electricity consumption data prediction result of the target user, so as to obtain the electricity consumption behavior of the target user according to the prediction result.
In the embodiment, the device firstly obtains the electricity consumption data of a plurality of users in a plurality of months through a map of a power distribution grid in a county by the obtaining module 110; then, the clustering module 120 performs clustering analysis on the power consumption data through a preset clustering algorithm to obtain a plurality of power consumption curves of different types, wherein one power consumption curve is formed by one type of power consumption data; then, determining a corresponding target prediction model for each type of power utilization curve through the determination module 130; finally, the analysis module 140 predicts the electricity utilization data of the category to which the target user belongs according to the target prediction model to obtain a monthly electricity utilization data prediction result of the target user, so as to obtain the electricity utilization behavior of the target user according to the prediction result.
The present embodiment provides a power consumption data analysis device capable of analyzing and predicting monthly power consumption behavior of a user.
Further, the obtaining module 110 is specifically configured to: acquiring original power utilization data of a plurality of users in a plurality of months through one map of a power distribution grid in a county; and carrying out data cleaning and data transformation on the original power consumption data to obtain power consumption data of a plurality of users in a plurality of months.
Further, the analysis module 140 is specifically configured to: acquiring the clustering number input by a user, wherein the clustering number is determined according to the clustering effect of different clustering numbers and the real electricity service scene, the clustering effect of different clustering numbers is represented by corresponding calculation results, one calculation result is determined based on the clustering result corresponding to one clustering number, and the determination mode of each calculation result is the same; and performing cluster analysis on the electricity utilization data based on the cluster number input by the user and a preset cluster algorithm to obtain electricity utilization curves of different types, wherein the number of the electricity utilization curves of the different types is the same as the cluster number input by the user.
Further, the determining of the one calculation result includes: performing cluster analysis on the electricity utilization data according to a cluster number and the preset cluster algorithm to obtain a cluster number of cluster clusters as a cluster result; determining the Euclidean distance sum from each sample point in each cluster to a cluster central point in each cluster; and adding the Euclidean distance sums corresponding to each cluster to obtain a calculation result.
Further, the determining module 130 is specifically configured to: constructing a corresponding training set and a verification set aiming at each type of power utilization curve, wherein one training set comprises a plurality of samples and a label corresponding to each sample, one sample comprises power utilization data of one user in one type of power utilization curve in a continuous preset number of months, and the label is the power utilization data of one user in one type of power utilization curve in the next month of the continuous preset number of months; and determining a corresponding target prediction model according to the training set and the verification set. Further, the determining a corresponding target prediction model according to the training set and the verification set includes: obtaining an initial prediction model based on the training set and a time recurrent neural network model; and optimizing the initial prediction model based on the verification set to obtain a target prediction model.
The electricity consumption data analysis device can execute the electricity consumption data analysis method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example four
FIG. 6 illustrates a schematic structural diagram of an electronic device 10 that may be used to implement an embodiment of the present invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 6, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM)12, a Random Access Memory (RAM)13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 can perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM)12 or the computer program loaded from a storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the electronic apparatus 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
A number of components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, or the like; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
In some embodiments, the power usage behavior analysis method based on a map of the power distribution grid may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When loaded into RAM 13 and executed by processor 11, the computer program may perform one or more of the steps of the power usage behavior analysis method based on a map of the power distribution grid described above. Alternatively, in other embodiments, the processor 11 may be configured to perform the power usage behavior analysis method based on a map of the distribution grid in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on a machine, as a stand-alone software package partly on a machine and partly on a remote machine or entirely on a remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solution of the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A power consumption behavior analysis method based on a graph of a power distribution network is characterized by comprising the following steps:
acquiring power utilization data of a plurality of users in a plurality of months through one map of a power distribution grid in a county;
performing cluster analysis on the power utilization data through a preset clustering algorithm to obtain a plurality of power utilization curves of different types, wherein one power utilization curve is formed by one type of power utilization data;
determining a corresponding target prediction model aiming at the power utilization curve of each category;
and predicting the electricity utilization data of the category of the target user according to the target prediction model to obtain a monthly electricity utilization data prediction result of the target user so as to determine the electricity utilization behavior of the target user according to the prediction result.
2. The method of claim 1, wherein obtaining electricity consumption data for a plurality of users over a plurality of months from a county power distribution grid map comprises:
acquiring original power utilization data of a plurality of users in a plurality of months through one map of a power distribution grid in a county;
and carrying out data cleaning and data transformation on the original power consumption data to obtain power consumption data of a plurality of users in a plurality of months.
3. The method of claim 1, wherein the clustering the electricity consumption data by a predetermined clustering algorithm to obtain a plurality of electricity consumption curves of different categories comprises:
acquiring the clustering number input by a user, wherein the clustering number is determined according to the clustering effect of different clustering numbers and the real electricity service scene, the clustering effect of different clustering numbers is represented by corresponding calculation results, one calculation result is determined based on the clustering result corresponding to one clustering number, and the determination mode of each calculation result is the same;
and performing cluster analysis on the electricity utilization data based on the cluster number input by the user and a preset cluster algorithm to obtain electricity utilization curves of different types, wherein the number of the electricity utilization curves of the different types is the same as the cluster number input by the user.
4. The method of claim 3, wherein the one computation result is determined by:
performing cluster analysis on the electricity utilization data according to a cluster number and the preset cluster algorithm to obtain a cluster number of cluster clusters as a cluster result;
determining the Euclidean distance sum from each sample point in each cluster to a cluster central point in each cluster;
and adding the Euclidean distance sums corresponding to each cluster to obtain a calculation result.
5. The method of claim 1, wherein determining a corresponding target prediction model for each class of power usage profile comprises:
constructing a corresponding training set and a verification set aiming at each type of power utilization curve, wherein one training set comprises a plurality of samples and a label corresponding to each sample, one sample comprises power utilization data of one user in one type of power utilization curve in a continuous preset number of months, and the label is the power utilization data of one user in one type of power utilization curve in the next month of the continuous preset number of months;
and determining a corresponding target prediction model according to the training set and the verification set.
6. The method of claim 5, wherein determining the corresponding target prediction model from the training set and the validation set comprises:
obtaining an initial prediction model based on the training set and a time recurrent neural network model;
and optimizing the initial prediction model based on the verification set to obtain a target prediction model.
7. An electricity consumption behavior analysis device based on a graph of a distribution network, the device comprising:
the acquisition module is used for acquiring the power utilization data of a plurality of users in a plurality of months through one map of the power distribution grid in a county;
the clustering module is used for clustering and analyzing the power utilization data through a preset clustering algorithm to obtain a plurality of power utilization curves of different types, and one power utilization curve consists of one type of power utilization data;
the determining module is used for determining a corresponding target prediction model according to the power utilization curve of each category;
and the analysis module is used for predicting the electricity utilization data of the category of the target user according to the target prediction model to obtain a monthly electricity utilization data prediction result of the target user so as to determine the electricity utilization behavior of the target user according to the prediction result.
8. An electronic device, characterized in that the electronic device comprises:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the electricity data analysis method of any one of claims 1-6.
9. A computer-readable storage medium having stored thereon computer instructions for causing a processor to perform the power usage data analysis method of any one of claims 1-6 when executed.
10. A computer program product, characterized in that the computer program product comprises a computer program which, when being executed by a processor, implements a method for analyzing electricity usage data according to any one of claims 1-6.
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