CN114925888A - Power data mining analysis method based on big data technology - Google Patents
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Abstract
The invention discloses an electric power data mining analysis method based on big data technology, which relates to the field of electric power data processing and comprises the steps of displaying electric power data in a chart form by adopting a visualization technology; converting a non-stationary time sequence of visual electric power data into a stationary time sequence by using a CNN-BilSTM-Attention-based model, and establishing a stationary time sequence model; repeatedly optimizing the stationary power data of the stationary time series model by using the neural network model to obtain an optimization result meeting a preset target; the electric power data mining and analyzing method based on the big data technology, which is constructed by the method, has independence, easy measurement, instantaneity, flexibility and practicability, is beneficial to visualizing electric power data, visually knowing the operation condition of a power grid, monitoring and managing the power grid in real time, analyzing a large amount of data, deeply mining the interrelation and the internal relation in mass data, and improving the management level of the power grid.
Description
Technical Field
The invention relates to the field, in particular to an electric power data mining analysis method based on big data technology
Background
At present, the construction of a digital power grid is gradually promoted, and a large amount of power data are accumulated. How to utilize this data has become a significant challenge in current grid operation. The visual analysis technology is applied to the power system, the power data are displayed in a vivid and visual image or graphic mode, the behavior and performance of the power system can be understood and optimized, prediction and prevention can be carried out before a power accident occurs or a quick response can be rapidly made when the accident occurs, and the distribution situation and the development trend of power utilization customers, the power utilization behaviors of users and the like can be better understood. The electric power data mining analysis based on the electric power big data technology has great significance as an application of the electric power big data. Macroscopically, accurate power data analysis can provide important data for national power development planning or major policy decision makers; from a microscopic perspective, accurate power data analysis is an important guarantee for reasonably planning power generation or power transmission of each power plant or power grid company to avoid power failure. Due to the advantages of big data, load prediction based on the big data is faster and more accurate than that of the traditional method, and the method has strong practical significance on electric power data mining and prediction through matching of the big electric power data with reasonable, economic and efficient means. However, at present, the management of the power data is still disordered, the displayed data is numerous and complicated, and the monitoring and management level of the power grid is low
In order to solve the defects in the background art, the invention aims to provide an electric power data mining analysis method based on a big data technology, which is helpful for visualizing electric power data, visually knowing the operation condition of a power grid, monitoring and managing the power grid in real time, analyzing a large amount of data, deeply mining the interrelations and the internal relations in mass data and improving the management level of the power grid.
Disclosure of Invention
The purpose of the invention can be realized by the following technical scheme: a power data mining analysis method based on big data technology comprises the following steps:
s1, acquiring visualized electric power data by adopting a visualization technology;
s2, introducing the visual electric power data into relevant environment data influencing the stability of the visual electric power data by using a Pearson correlation coefficient formula, and performing data preprocessing to obtain relevant stable electric power data with relevant environmental factors;
s3, the stable power data in the stable time sequence model is optimized by the CNN-BilSTM-Attention model, and an optimization result meeting a preset target is obtained.
The further steps of obtaining the visual data are as follows:
s1.1, collecting power grid operation data, power customer data, power grid enterprise management data and user power utilization data, and combining a plurality of data to form a data warehouse; data compression is realized by clustering and deleting redundant data in the data warehouse to form electric power data; scientific and reasonable analysis and judgment are carried out by using a Pearson correlation coefficient formula, scientific selection of influencing factors is realized, corresponding analysis and data collection are completed, and the data are included in sample data;
s1.2, carrying out visual interaction on a chart assembly based on a visual technology and electric power data to obtain visual electric power data; the chart component based on the visualization technology comprises a pie chart, a scatter chart, a funnel chart, a bubble chart and a thermodynamic chart.
Further, the specific step of S2 is:
s2.1 first collect the relevant impact environment data and calculate the correlation coefficient using the Pearson correlation coefficient formula as follows:
wherein s is I And s I Is the standard deviation of X, Y;
s2.2, performing interval normalization on data comprising the environment sample to [0,1], and calculating the formula as follows:
x i ＝(x i -x min )/(x max -x min )
s2.3 in order to obtain more obvious data characteristics, data normalization is also needed to be carried out as follows:
wherein mu β Is taken as the mean value of the average value,
in order to be a variance thereof,
is a very small positive number.
Further, the specific step of S3 is:
firstly, performing one-dimensional convolution on preprocessed data and adding a dropout layer to learn local characteristics of the data, wherein the formula is as follows:
where h (k) is the weight of the convolution kernel, u (k) is the input into the convolution layer, and y (k) is the resulting output of the network, we have added a dropout layer to it in order to get better training of the model and to prevent over-fitting of the model.
And (3) sending the features obtained by the one-dimensional convolution into a bidirectional LSTM (BiLSTM), then sending the features into a dropout, and then sending the features into a self-attention module to further extract global high-level features.
The LSTM part mainly comprises a forgetting gate part, an input gate part and an output gate part.
The main calculation of the forgetting gate part is as follows:
wherein U and W are weights, x is an input, f is an obtained forgetting factor, c is a memory factor, k is a main line forgetting output: the input gate portion is calculated mainly as follows:
where U and W are weights, x is an input, j is the resulting gated supply size, c is a memory factor,
the output gate part is mainly calculated as follows:
wherein U and W are weights, x is input, o is the obtained output size, h is the main line output size, the features before and after self-attention are merged and stretched and then sent to the full-connection layer for data regression:
wherein Q, K and V are vectors, and d is the dimension of K.
The invention has the beneficial effects that: displaying the power data in a chart form by adopting a visualization technology; converting a non-stationary time sequence of visual electric power data into a stationary time sequence by using a CNN-BilSTM-Attention-based model, and establishing a stationary time sequence model; repeatedly optimizing the stationary power data of the stationary time series model by using the neural network model to obtain an optimization result meeting a preset target; the electric power data mining and analyzing method based on the big data technology, which is constructed by the method, has independence, easy measurement, instantaneity, flexibility and practicability, is beneficial to visualizing electric power data, visually knowing the operation condition of a power grid, monitoring and managing the power grid in real time, analyzing a large amount of data, deeply mining the interrelation and the internal relation in mass data, and improving the management level of the power grid.
Drawings
FIG. 1 is a schematic flow diagram of the present invention.
FIG. 2 is a flow chart of the CNN-BilSTM-Attention model of the present invention.
Detailed Description
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.
S1, acquiring visualized electric power data by adopting a visualization technology;
and S2, introducing the visualized power data into relevant environmental data influencing the stability of the visualized power data by using a Pearson correlation coefficient formula, and preprocessing the data to obtain relevant stable power data with environmental factors.
S3, the stable power data in the stable time sequence model is optimized by the CNN-BilSTM-Attention model, and an optimization result meeting a preset target is obtained.
The further steps of obtaining the visual data are as follows:
s1.1, collecting power grid operation data, power customer data, power grid enterprise management data and user power utilization data, and combining a plurality of data to form a data warehouse; data compression is realized by clustering and deleting redundant data in the data warehouse to form electric power data; the method comprises the steps of filtering original mass data, preprocessing the data, including data type conversion, data cleaning, default value processing and data integration, performing weighted average completion on obviously missed data, selecting typical representative data, storing the data such as power grid operation data, power customer data, power grid enterprise management data and user electricity consumption in a big data platform after being collected by collection equipment, identifying, analyzing and combining the data from different sources, inheriting and combining a plurality of data into a unified data for storage, and forming a data warehouse or a data side. Data compression is realized by clustering and deleting redundant feature data; and displaying the power data in a chart form by adopting a visualization technology. Scientific and reasonable analysis and judgment are carried out by using a Pearson correlation coefficient formula, scientific selection of influencing factors is realized, corresponding analysis and data collection are completed, and the data are incorporated into sample data;
s1.2, carrying out visual interaction on a chart assembly based on a visual technology and electric power data to obtain visual electric power data; the chart component based on the visualization technology comprises a pie chart, a scatter chart, a funnel chart, a bubble chart and a thermodynamic chart.
Further, the specific steps of S2 are:
s2.1 first collect the relevant impact environment data and calculate the correlation coefficient using the Pearson correlation coefficient formula as follows:
wherein s is I And s I Is the standard deviation of X, Y;
the Pearson correlation coefficient obtained here is correlated with other introduced interference data to obtain a corresponding noise correlation characteristic vector.
S2.2, performing interval normalization on data comprising the environment sample to [0,1], and calculating the formula as follows:
x i ＝(x i -x min )/(x max -x min )
s2.3 in order to obtain more obvious data characteristics, data normalization is carried out as follows:
wherein mu β Is taken as the mean value of the average value,
as a result of the variance thereof,
is a very small positive number.
Finally, processed comprehensive original data and data of data noise are obtained;
further, the specific step of S3 is:
firstly, performing one-dimensional convolution on preprocessed data and adding a dropout layer to learn local characteristics of the data, wherein the formula is as follows:
where h (k) is the weight of the convolution kernel, u (k) is the input into the convolution layer, and y (k) is the resulting output of the network, we have added a dropout layer to it in order to get better training of the model and to prevent over-fitting of the model.
And (3) sending the features obtained by the one-dimensional convolution into a bidirectional LSTM (BiLSTM), then sending the features into a dropout, and then sending the features into a self-attention module to further extract global high-level features.
The LSTM part mainly comprises a forgetting gate part, an input gate part and an output gate part.
The main calculation of the forgetting gate part is as follows:
wherein U and W are weights, x is an input, f is an obtained forgetting factor, c is a memory factor, k is a main line forgetting output:
the input gate portion is calculated mainly as follows:
where U and W are weights, x is an input, j is the resulting gated supply size, c is a memory factor,
the output gate part is mainly calculated as follows:
where U and W are weights, x is an input, o is the resulting output magnitude, and h is the dominant line output magnitude, we obtain a comprehensive data feature vector by using convolution and feature extraction of the LSTM on it.
And sending the preliminarily extracted features into self-attribute for global feature extraction to obtain data features with higher relevance:
wherein Q, K and V are vectors, and d is the dimension of K.
The features before and after self-orientation were combined and stretched before being sent to the fully-connected layer for data regression to obtain the final result.
Claims (4)
1. A power data mining analysis method based on big data technology is characterized by comprising the following steps:
s1, acquiring visualized electric power data by adopting a visualization technology;
s2, introducing the visual electric power data into relevant environmental data influencing the stability of the visual electric power data by using a Pearson correlation coefficient formula, and performing data preprocessing to obtain relevant stable electric power data with environmental factors;
s3, the stable power data in the stable time sequence model is optimized by the CNN-BilSTM-orientation model, and an optimization result meeting a preset target is obtained.
2. The electric power data mining analysis method based on big data technology according to claim 1, characterized in that the specific steps of obtaining visual electric power data are as follows:
s1.1, collecting power grid operation data, power customer data, power grid enterprise management data and user power utilization data, and combining a plurality of data to form a data warehouse; data compression is realized by clustering and deleting redundant data in the data warehouse to form electric power data; scientific and reasonable analysis and judgment are carried out by using a Pearson correlation coefficient formula, scientific selection of influencing factors is realized, corresponding analysis and data collection are completed, and the data are included in sample data;
s1.2, carrying out visual interaction on a chart assembly based on a visual technology and electric power data to obtain visual electric power data; the chart component based on the visualization technology comprises a pie chart, a scatter chart, a funnel chart, a bubble chart and a thermodynamic chart.
3. The electric power data mining analysis method based on big data technology according to claim 1, wherein the specific steps of S2 are as follows:
s2.1 first collect the relevant impact environment data and calculate the correlation coefficient using the Pearson correlation coefficient formula as follows:
s2.2 interval-normalizing the data including the environmental sample to [0,1], and calculating formula as follows:
s2.3 in order to obtain more obvious data characteristics, data normalization is carried out as follows:
4. the electric power data mining analysis method based on big data technology according to claim 1, characterized in that:
firstly, performing one-dimensional convolution on preprocessed data and adding a dropout layer to learn local features of the data, wherein the formula is as follows:
sending the features obtained by the one-dimensional convolution into a bidirectional LSTM (BiLSTM), and if dropout is found, sending the features into a self-attribute module to further extract global high-level features; the features before and after self-orientation were then combined and stretched before being fed into the fully-connected layer for a data regression.
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