CN115344566A - Multi-energy consumption data completion method based on wavelet decomposition and Fourier transform - Google Patents
Multi-energy consumption data completion method based on wavelet decomposition and Fourier transform Download PDFInfo
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Abstract
The invention discloses a multi-energy data completion method based on wavelet decomposition and Fourier transform, which comprises the following steps: 1. performing wavelet decomposition on the collected carbon-containing characteristic energy consumption sequence to obtain an energy consumption period characteristic sequence and an energy consumption trend characteristic sequence; 2. obtaining a characteristic sequence for predicting energy consumption trend based on curve fitting; 3. obtaining a characteristic sequence of the predicted energy consumption period based on Fourier series fitting; 4. and completing missing data through wavelet reconstruction based on the second step and the third step. The method constructs a data completion model based on wavelet decomposition and Fourier transformation, so that massive multi-energy data completion of the deficient key control and emission enterprises can be realized.
Description
Technical Field
The invention relates to a multi-energy consumption data completion method, and belongs to the field of data analysis.
Background
Human beings have entered the big data era since 2010, and the big data era comes, so that a plurality of opportunities and challenges are brought to various carbon-containing energy consumption data mining technologies. Nowadays, the research on various carbon-containing energy consumption data does not adopt a sampling investigation method, but comprehensively analyzes all the various carbon-containing energy consumption data. The remarkable characteristics of various carbon-containing energy consumption data are that the periodicity and the trend are obvious, the types are multiple, the flow speed is high, the data volume is large, when various carbon-containing energy consumption data are collected daily, the phenomenon that various carbon-containing energy consumption data are lost can often occur, the reason for causing the loss of various carbon-containing energy consumption data is many, if information is accidentally omitted and cannot be obtained, various carbon-containing energy consumption data are lost, the carbon correlation real-time monitoring model established by a heavy-point emission control enterprise can be influenced, and the currently common processing methods for various carbon-containing energy consumption data loss values are three types as follows:
the first method directly deletes the consumption data of each carbon-containing energy source in one period. The method is simple and easy to implement, and is very effective if the deleted usage data of one period accounts for a small amount in the whole data. However, this approach degrades the quality of the data mining algorithm when the fraction of missing values fluctuates widely. Meanwhile, the usage data of one deleted cycle may contain important information, so that the data is deviated and even an error conclusion is drawn.
The second method is used for speculating and supplementing various carbon-containing energy consumption data. The method is generally based on the statistical principle, different algorithms are used for filling missing values, and common data filling algorithms include: average value (or median) filling, special value filling, artificial filling, a k-nearest neighbor method and the like, and the above algorithm only adapts to the overall trend of various carbon-containing energy consumption data, and cannot evaluate the periodic fluctuation of various carbon-containing energy consumption data.
The third method does not perform any processing, and operates by using a deep excavation method, and common methods include a bayesian network, an artificial neural network and the like. The method is characterized in that data mining is directly carried out on various carbon-containing energy consumption data sequences containing missing data, but due to the fact that the various carbon-containing energy consumption data are massive, the efficiency of data completion can be reduced by means of deep mining, and processing time is too long.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a multi-energy-consumption data completion method based on wavelet decomposition and Fourier transform, so that the minimization of the processing time of the mass data of various carbon-containing energy consumption is realized on the basis of meeting the data completion, and the efficiency of the completion of the mass data of various carbon-containing energy consumption can be improved.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention relates to a multi-energy data completion method based on wavelet decomposition and Fourier transform, which is characterized by comprising the following steps of:
step one, collecting various carbon-containing energy consumption data of the heavy-point emission control enterprise according to a sampling period to obtain a characteristic carbon-containing energy consumption data set
Representing j category carbon-containing characteristic energy consumption data acquired at the mth sampling moment; m is more than or equal to 1 and less than or equal to i, and the carbon-containing characteristic energy consumption data at the mth sampling moment
The characteristic of (1) comprises a periodic characteristic and a trend characteristic; i denotes the total sampling instant;
suppose the mth sampling instantIf the collected j-th carbon-containing energy consumption data is missing data, selecting the missing data
The carbon-containing energy consumption data of the previous n sampling moments form a carbon-containing energy consumption sequence
And n is an even number divisible by 8, n is more than or equal to 1<m; wherein the content of the first and second substances,
representing j category carbon-containing energy consumption data acquired at the m-n +1 th sampling moment;
carbon-containing energy consumption sequence of n sampling moments through formula (1-1) -formula (1-2)
Performing 3-layer wavelet decomposition to obtain the energy consumption period characteristic sequence of the energy consumption sequence p-layer wavelet decomposition
Energy consumption trend characteristic sequence of p-layer wavelet decomposition
In the formula (1-1) -formula (1-3), n p The sampling time of the wavelet decomposition of the p-th layer; s is the horizontal coordinate of wavelet space, s is less than or equal to 2n p ；
A sequence of wavelet decomposition of the p-th layer; z is the whole integer number set, s ∈Z；
The j-th energy consumption period characteristic is obtained by decomposing the p-th layer wavelet;
obtaining j-th energy consumption trend characteristics for the p-th layer wavelet decomposition; g (s-2 n) p ) Is a high pass filter function; h (s-2 n) p ) Is a low pass filter function;
step two, obtaining an energy consumption trend characteristic sequence by utilizing the formula (2-1)
Is fitted to the prediction function
And determining the energy consumption trend characteristic prediction function by using the formula (2-2)
Four curve fitting coefficients omega 0 、ω 1 、ω 2 、ω 3 ；
In the formula (2-1) -formula (2-2), ε is a constant;
step three, obtaining an energy consumption period characteristic sequence by utilizing the formula (3-1)
The k-th main frequency of
1≤k<N-1, thereby obtaining an energy consumption period characteristic sequence by using the formula (3-2)
Energy consumption period characteristic prediction function
In the formula (3-1) -formula (3-2), ω is k An angular frequency that is a fourier series of the kth principal frequency;
step four, implementing missing data F by using steps 4.1-4.5 m Completing;
step 4.1, predicting function based on energy consumption cycle characteristics
To obtain (n) 1 +1)、(n 1 +2)、(n 1 + 3) and (n) 1 + 4) predicted values at respective times are
And
thereby obtaining (n) 2 + 4) energy consumption period characteristic prediction sequence of sampling time
Step 4.2, obtaining a carbon-containing energy consumption prediction sequence after three-layer wavelet reconstruction through the formula (4-1)
In the formula (4-1), the compound,
the energy consumption trend characteristic prediction sequence is obtained after the two layers of wavelets are reconstructed;
the j-th category carbon-containing energy consumption data from the m-th sampling time to the m +7 th sampling time
The predicted value of (2);
step 4.3, retention only
Carbon-containing energy consumption data as the m-th sampling time
To realize the jth type carbon-containing energy consumption missing data collected at the mth sampling moment
So as to obtain a complete j-th type carbon-containing energy consumption characteristic data set acquired at i sampling moments
The electronic device of the invention comprises a memory and a processor, and is characterized in that the memory is used for storing programs for supporting the processor to execute the multi-energy data completion method, and the processor is configured to execute the programs stored in the memory.
The invention relates to a computer-readable storage medium, on which a computer program is stored, wherein the computer program is executed by a processor to perform the steps of the multi-energy data completion method.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention analyzes two unique characteristics, namely the trend characteristic and the period characteristic, shared by various carbon-containing energy consumption data, decomposes various carbon-containing energy consumption data sequences into a sequence with the trend characteristic and a sequence with the period characteristic, namely an energy consumption trend characteristic sequence and an energy consumption period characteristic sequence by utilizing wavelet decomposition, and lays a data foundation for complementing missing data in the following various carbon-containing energy consumption data sequences.
2. According to the invention, through analyzing the characteristics of the energy consumption trend characteristic sequence and the characteristics of the energy consumption period characteristic sequence, a fitting function model of the adaptive energy consumption trend characteristic sequence is established by curve fitting respectively, a predicted energy consumption trend characteristic sequence is obtained through the fitting function model, a fitting function model of the adaptive energy consumption period characteristic sequence is established by Fourier series, a predicted period trend characteristic sequence is obtained through the fitting function model, and finally missing data is obtained through the reconstruction of the predicted energy consumption trend characteristic sequence and the predicted period trend characteristic sequence, so that the completion of data is realized.
Drawings
FIG. 1 is a flow chart of the method steps of the present invention.
Detailed Description
In this embodiment, a multi-energy data completion method based on wavelet decomposition and fourier transform, as shown in fig. 1, includes the following steps:
step one, collecting various carbon-containing energy consumption data of the heavy-point emission control enterprise according to a sampling period to obtain a carbon-containing characteristic energy consumption data set
Representing j category carbon-containing energy consumption data acquired at the mth sampling moment; m is more than or equal to 1 and less than or equal to i, and carbon-containing characteristic energy consumption data at the mth sampling moment
The characteristics of (1) comprise periodic characteristics and trend characteristics; i represents the total sampling time and the data set of the consumption of various carbon-containing energy sources
Two unique features in common, namely trend and periodic features;
if the jth type carbon-containing energy consumption data acquired at the mth sampling moment is missing data, selecting the missing data
The carbon-containing energy consumption data of the previous n sampling moments form a carbon-containing energy consumption sequence
And n is an even number divisible by 8, and n is not less than 1<m; wherein, the first and the second end of the pipe are connected with each other,
representing j category carbon-containing energy consumption data acquired at the m-n +1 th sampling moment;
carbon-containing energy consumption sequence of n sampling moments through formula (1-1) -formula (1-2)
3, decomposing the carbon-containing energy consumption sequence into a sequence with trend characteristics and a sequence with periodic characteristics by utilizing wavelet decomposition to obtain an energy consumption periodic characteristic sequence of the energy consumption sequence p-layer wavelet decomposition
Energy consumption trend characteristic sequence of p-layer wavelet decomposition
In the formula (1-1) -formula (1-3), n p The sampling time of the wavelet decomposition of the p layer; s is the horizontal coordinate of wavelet space, s is less than or equal to 2n p ，
A sequence of wavelet decomposition of the p-th layer; z is the whole integer number set, and s belongs to Z;
the energy consumption period characteristic sequence is obtained by decomposing the p-th layer wavelet;
the energy consumption trend characteristic sequence is obtained by decomposing the p-th layer wavelet; g (s-2 n) p ) Is a high pass filter function; h (s-2 n) p ) Is a low pass filter function;
step two, obtaining an energy consumption trend characteristic sequence by a curve fitting method by utilizing the formula (2-1)
Fitted predictive function of
And using the formula (2-2) to estimate the value by minimizing the prediction
And energy consumption data
To determine an energy consumption trend characteristic prediction function
Four curve fitting coefficients omega 0 、ω 1 、ω 2 、ω 3 ；
In the formula (2-1) -formula (2-2), ε is a constant;
step three, obtaining the energy consumption period characteristic sequence by a Fourier series fitting method by utilizing the formula (3-1)
The k-th main frequency of
1≤k<N-1, thereby obtaining an energy consumption period characteristic sequence by using the formula (3-2)
Energy consumption period characteristic prediction function
In the formula (3-1) -formula (3-2), ω is k An angular frequency that is a Fourier series of the kth principal frequency;
step four, implementing missing data F by using steps 4.1-4.5 m Completing;
step 4.1, predicting function based on energy consumption trend characteristics
To obtain the (n) th 3 + 1) predicted value of time
Thereby obtaining (n) 3 + 1) energy consumption trend characteristic prediction sequence at sampling time
Energy consumption period characteristic based prediction function
To obtain (n) 3 + 1) predicted value of time
Thereby obtaining (n) 3 + 1) energy consumption period characteristic prediction sequence of sampling time
Energy consumption period characteristic based prediction function
To obtain (n) 2 + 1) and (n) 2 + 2) predicted values at respective times are
And
thereby obtaining (n) 2 + 2) energy consumption period characteristic prediction sequence of sampling time
Energy consumption period characteristic based prediction function
To obtain (n) 1 +1)、(n 1 +2)、(n 1 + 3) and (n) 1 + 4) predicted values at respective times are
And
thereby obtaining (n) 2 + 4) energy consumption period characteristic prediction sequence of sampling time
Obtaining an energy consumption trend characteristic prediction sequence through two-layer wavelet reconstruction by using a formula (4-1) -a formula (4-2)
Step 4.2, obtaining a carbon-containing energy consumption prediction sequence after three-layer wavelet reconstruction through the formula (4-3)
Wherein the content of the first and second substances,
carbon-containing energy consumption data from the m-th sampling time to the m +7 th sampling time
The predicted value of (c):
step 4.3, mixing
Predicted value of (2)
Is left off and only remains
Carbon-containing energy consumption data as the m-th sampling time
The predicted value of the data is used for realizing the jth type carbon-containing energy consumption missing data acquired at the mth sampling moment
The complete j-type carbon-containing characteristic energy consumption data set acquired at i sampling moments is finally obtained
In this embodiment, an electronic device includes a memory and a processor, where the memory is used to store a program that supports the processor to execute the above-mentioned multi-energy data completion method, and the processor is configured to execute the program stored in the memory.
In this embodiment, a computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the computer program executes the steps of the above-mentioned multi-energy data completion method.
Claims (3)
1. A multi-energy data completion method based on wavelet decomposition and Fourier transform is characterized by comprising the following steps:
step one, collecting various carbon-containing energy consumption data of the heavy-point emission control enterprises according to a sampling period to obtain a characteristic carbon-containing energy consumption data set
Representing j category carbon-containing characteristic energy consumption data acquired at the mth sampling moment; m is more than or equal to 1 and less than or equal to i, and the carbon-containing characteristic energy consumption data at the mth sampling moment
The characteristics of (1) comprise periodic characteristics and trend characteristics; i denotes the total sampling instant;
if the jth type carbon-containing energy consumption data acquired at the mth sampling moment is missing data, selecting the missing data
The carbon-containing energy consumption data of the previous n sampling moments form a carbon-containing energy consumption sequence
And n is an even number divisible by 8, n is more than or equal to 1<m; wherein the content of the first and second substances,
representing j category carbon-containing energy consumption data acquired at the m-n +1 th sampling moment;
carbon-containing energy consumption sequence of n sampling moments through formula (1-1) -formula (1-2)
Performing 3-layer wavelet decomposition to obtain the energy consumption period characteristic sequence of the energy consumption sequence p-layer wavelet decomposition
Energy consumption trend characteristic sequence of sum p-layer wavelet decomposition
In the formula (1-1) -formula (1-3), n p The sampling time of the wavelet decomposition of the p-th layer; s is the horizontal coordinate of wavelet space, s is less than or equal to 2n p ；
A sequence of wavelet decompositions for a p-th layer; z is the whole integer number set, and s belongs to Z;
the j-th energy consumption period characteristic is obtained by decomposing the p-th layer wavelet;
the j-th energy consumption trend characteristic is obtained by decomposing the p-th layer wavelet; g (s-2 n) p ) Is a high pass filter function; h (s-2 n) p ) Is a low pass filter function;
step two, obtaining an energy consumption trend characteristic sequence by utilizing the formula (2-1)
Is fitted to the prediction function
And determining the energy consumption trend using equation (2-2)Feature prediction function
Four curve fitting coefficients omega 0 、ω 1 、ω 2 、ω 3 ；
In the formula (2-1) -formula (2-2), ε is a constant;
step three, obtaining an energy consumption period characteristic sequence by utilizing the formula (3-1)
The k-th main frequency of
Thereby obtaining the energy consumption period characteristic sequence by using the formula (3-2)
Energy consumption period characteristic prediction function
In the formula (3-1) -formula (3-2), ω is k Of the Fourier series of the k-th main frequencyAn angular frequency;
step four, implementing missing data F by using steps 4.1-4.5 m Completing;
step 4.1, predicting function based on energy consumption cycle characteristics
To obtain (n) 1 +1)、(n 1 +2)、(n 1 + 3) and (n) 1 + 4) predicted values at respective times are
And
thereby obtaining (n) 2 + 4) energy consumption period characteristic prediction sequence of sampling time
Step 4.2, obtaining a carbon-containing energy consumption prediction sequence after three-layer wavelet reconstruction through the formula (4-1)
In the formula (4-1),
predicting a sequence for the energy consumption trend characteristics obtained by reconstructing the two layers of wavelets;
respectively at the m-th sampling instantCategory j carbonaceous energy consumption data up to the m +7 th sampling time
The predicted value of (2);
step 4.3, retention only
Carbon-containing energy consumption data as mth sampling time
To realize the jth type carbon-containing energy consumption missing data collected at the mth sampling moment
So as to obtain a complete j-th type carbon-containing energy consumption characteristic data set acquired at i sampling moments
2. An electronic device comprising a memory and a processor, wherein the memory is configured to store a program that enables the processor to perform the method of claim 1, and wherein the processor is configured to execute the program stored in the memory.
3. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method as claimed in claim 1.
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| CN116776228B (en) * | 2023-08-17 | 2023-10-20 | 合肥工业大学 | Power grid time sequence data decoupling self-supervision pre-training method and system |
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