CN110210995B - Comprehensive energy load prediction method based on wavelet packet and neural network - Google Patents

Comprehensive energy load prediction method based on wavelet packet and neural network Download PDF

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CN110210995B
CN110210995B CN201910481870.8A CN201910481870A CN110210995B CN 110210995 B CN110210995 B CN 110210995B CN 201910481870 A CN201910481870 A CN 201910481870A CN 110210995 B CN110210995 B CN 110210995B
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王绪利
胡斌
赵锋
代磊
周帆
杨欣
王加庆
江桂芬
胡旭东
张德广
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Economic and Technological Research Institute of State Grid Anhui Electric Power Co Ltd
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Abstract

The invention discloses a comprehensive energy load prediction method based on a wavelet packet and a neural network, which belongs to the field of comprehensive energy system load prediction.

Description

Comprehensive energy load prediction method based on wavelet packet and neural network
Technical Field
The invention relates to the field of comprehensive energy system load prediction, in particular to a comprehensive energy load prediction method based on a wavelet packet and a neural network.
Background
The comprehensive energy system is a system integrating power, heat energy, cold energy and the like. The method is an important trend of energy development, and has important effects on promoting energy structure optimization, improving energy efficiency and promoting renewable energy consumption. The accurate load prediction of the comprehensive energy system is a basic premise of optimal design, operation, scheduling and energy management, and has important theoretical significance and practical value.
Currently, there are many studies on load prediction by scholars at home and abroad, and common load prediction methods include traditional algorithms based on statistics and machine learning algorithms. The traditional algorithms are further classified into a time series method, a grey prediction method, a regression analysis method and the like, and the change trend of the future load is generally predicted by fitting historical load data. The machine learning algorithm comprises deep learning, a support vector machine, a neural network and the like, has the characteristics of self-organization, self-learning and self-adaptation, can realize the nonlinear mapping between the load influence factor and the load, and has better prediction accuracy compared with the traditional load prediction method, so that the method is more widely applied.
However, the current load prediction model does not highlight the frequency band characteristic of the load, and the influence of high-frequency noise on the load prediction precision is ignored; and most of predictions only aim at the load of a certain energy source, the correlation among energy sources in the comprehensive energy source system is not considered, and the coupling relation among the energy source systems is split.
Disclosure of Invention
The invention aims to provide a comprehensive energy load prediction method based on a wavelet packet and a neural network, which improves the accuracy of load prediction by comprehensively considering the frequency band characteristics of loads and the correlation among various loads.
In order to achieve the above purpose, the invention adopts a comprehensive energy load prediction method based on a wavelet packet and a neural network, which comprises the following steps:
acquiring historical load data;
respectively carrying out wavelet packet decomposition on different types of load data in the historical load data to obtain load sequences of each type of load data under different frequency bands;
dividing the load sequence into a training sample and a prediction sample, and establishing different BP neural networks according to the type and frequency band of the load sequence;
inputting the training samples into a corresponding BP neural network to obtain a load prediction model;
and inputting the prediction sample into a corresponding load prediction model to obtain a load prediction result.
Further, after the obtaining of the historical load data, the method further includes:
filling missing data of the historical load data to obtain preprocessed historical load data;
correspondingly, the performing wavelet packet decomposition on different types of load data in the historical load data to obtain load sequences of each type of load data under different frequency bands includes:
and respectively carrying out wavelet packet decomposition on different types of load data in the preprocessed historical load data to obtain load sequences of each type of load data under different frequency bands.
Further, the performing wavelet packet decomposition on different types of load data in the historical load data to obtain load sequences of each type of load data in different frequency bands includes:
decomposing different types of load data in the historical load data by adopting wavelet basis functions respectively to obtain a wavelet packet decomposition coefficient of each type of load data;
and performing single-branch reconstruction on the wavelet packet decomposition coefficient to obtain a load sequence of each load data under different frequency bands.
Further, after the performing wavelet packet decomposition on different types of load data in the historical load data respectively to obtain load sequences of each type of load data in different frequency bands, the method further includes:
calculating the correlation coefficient between every two different types of load sequences under the same frequency band;
screening out load sequences with the absolute values of the relation numbers larger than a set threshold value under a certain frequency band;
correspondingly, the dividing the load sequence into training samples and prediction samples, and establishing different BP neural networks according to the type and frequency band of the load sequence includes:
dividing the screened load sequences into training samples and prediction samples, and establishing different BP neural networks according to the types and frequency bands of the screened load sequences.
Further, the dividing the screened load sequence into a training sample and a prediction sample, and establishing different BP neural networks according to the type and frequency band of the screened load sequence includes:
normalizing the screened load sequence;
dividing the load sequence after normalization into a training sample and a prediction sample;
and establishing different BP neural networks according to the types and frequency bands of the screened load sequences.
Further, the inputting the training samples into the corresponding BP neural network to obtain a load prediction model includes:
inputting the normalized load sequence obtained before the moment k in the training sample into a corresponding BP neural network to obtain a prediction result of the load sequence at the moment k;
and (4) carrying out iterative training on the BP neural network until the prediction error reaches a set threshold value, and stopping training to obtain a load prediction model.
Further, the inputting the prediction sample into a corresponding load prediction model to obtain a load prediction result includes:
inputting the prediction samples into corresponding load prediction models to obtain a prediction result of each load sequence;
and adding the prediction results of the same load sequence under different frequency bands to obtain a load prediction result.
Further, after the load prediction results are obtained by adding the prediction results of the load sequences of the same kind in different frequency bands, the method further includes:
and performing inverse normalization processing on the load prediction result to obtain a normalized load prediction result.
Compared with the existing load prediction method, the method has the following beneficial effects:
in the invention, considering that the frequency band characteristics of the load can influence the load prediction, the load data is decomposed into load sequences under a plurality of frequency bands by performing multi-layer wavelet packet transformation on historical load data, then load prediction models are respectively established, and the prediction results of the load sequences under different frequency bands are added to obtain a load prediction result, thereby improving the accuracy of the load prediction; in addition, a proper load sequence is screened out by calculating the correlation coefficients of different loads on each frequency band, so that the coupling between each energy source in the comprehensive energy source system is improved, and the accuracy of load prediction is further improved.
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The following detailed description of embodiments of the invention refers to the accompanying drawings in which:
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a schematic diagram of a load data wavelet packet decomposition;
FIG. 3 is a graph of a predicted value versus an actual value of an electrical load;
FIG. 4 is a graph of a predicted value versus an actual value of heat load fitted to a load;
fig. 5 is a graph of a predicted value versus an actual value of the cooling load.
Detailed Description
To further illustrate the features of the present invention, refer to the following detailed description of the invention and the accompanying drawings. The drawings are for reference and illustration purposes only and are not intended to limit the scope of the present disclosure.
As shown in fig. 1, the invention adopts a comprehensive energy load prediction method based on a wavelet packet and a neural network, which comprises the following steps:
s1, acquiring historical load data;
s2, performing wavelet packet decomposition on different types of load data in the historical load data respectively to obtain load sequences of each type of load data under different frequency bands;
s3, dividing the load sequence into a training sample and a prediction sample, and establishing different BP neural networks according to the type and frequency band of the load sequence;
s4, inputting the training samples into the corresponding BP neural network to obtain a load prediction model;
and S5, inputting the prediction sample into a corresponding load prediction model to obtain a load prediction result.
Further, after S1, the method further includes:
filling missing data of the historical load data to obtain preprocessed historical load data;
accordingly, S2 includes:
and respectively carrying out wavelet packet decomposition on different types of load data in the preprocessed historical load data to obtain load sequences of each type of load data under different frequency bands.
Specifically, the present embodiment specifically describes using the electrical load data, the thermal load data, and the cooling load data in the historical load data:
first, electric load data l (T), heat load data h (T), and cold load data f (T) are acquired, where T is 1,2, …, and T is a history time. The embodiment collects the electricity, heat and cold load data of a college comprehensive energy system from 1 month and 1 day in 2018 to 12 months and 31 days in 2018, the sampling frequency is 1h, 24 times of data are collected every day, and 8760 points of data exist in each load.
In order to improve the accuracy of prediction, the initial historical load data needs to be preprocessed, missing data is filled, and the preprocessed historical load data is obtained, wherein the filling formula is as follows:
Figure BDA0002084112040000051
wherein x (t) represents the electrical, thermal and cold load data at time t after padding; beta is a repairing factor, beta belongs to (0.8,1.2),the value is generally 1; recording the preprocessed electric, heat and cold load data as
Figure BDA0002084112040000052
Further, S2 includes the following subdivision steps:
s21, decomposing different types of load data in the historical load data by adopting wavelet basis functions respectively to obtain wavelet packet decomposition coefficients of each type of load data;
and S22, performing single branch reconstruction on the wavelet packet decomposition coefficient to obtain a load sequence of each load data under different frequency bands.
Specifically, the present embodiment uses db3 wavelet basis function to process the preprocessed electrical, thermal and cold load data
Figure BDA0002084112040000053
Multiple wavelet packet decompositions, e.g. N layers, are performed separately to obtain 2 for each loadNGrouping wavelet packet decomposition coefficients; then, single reconstruction is carried out on the wavelet packet decomposition coefficient to obtain the electricity, heat and cold load data of 2NThe load sequences in each frequency band are respectively recorded as
Figure BDA0002084112040000054
Wherein i represents a frequency band, and i ═ 1,2N。
For illustration, in this embodiment, 3-layer wavelet packet decomposition is performed on the preprocessed electrical, thermal, and cold load data, and as shown in fig. 2, 8 frequency bands, that is, 8 load sequences, are obtained by decomposition and reconstruction.
Further, after S2, the method further includes:
calculating the correlation coefficient between every two different types of load sequences under the same frequency band;
screening out load sequences with the absolute values of the relation numbers larger than a set threshold value under a certain frequency band;
accordingly, S3 includes:
dividing the screened load sequences into training samples and prediction samples, and establishing different BP neural networks according to the types and frequency bands of the screened load sequences.
In particular, since there is a correlation between different loads, considering the correlation between different loads in predicting the load result may improve the accuracy of the prediction.
After 8 load sequences of each load data are obtained, calculating the correlation coefficient between every two load sequences, wherein the calculation formula is as follows:
Figure BDA0002084112040000061
Figure BDA0002084112040000062
Figure BDA0002084112040000063
wherein ρlh,i、ρlf,i、ρhf,iRespectively are correlation coefficients between an electric load sequence and a heat load sequence, an electric load sequence and a cold load sequence, and a heat load sequence and a cold load sequence;
Figure BDA0002084112040000064
Figure BDA0002084112040000065
respectively electric load sequence
Figure BDA0002084112040000066
Thermal load sequence
Figure BDA0002084112040000067
Cold load sequence
Figure BDA0002084112040000068
Average value of (d); the value of the correlation coefficient is between-1 and 1, and the larger the absolute value of the correlation coefficient is, the stronger the correlation is.
Comparing the correlation coefficients among the three load sequences with a set threshold delta respectively, and screening out min (| rho)lh,i|,|ρlf,i|,|ρhf,iI) is greater than the load sequence of delta; assuming deletion of m payload sequences, 2 remains after screeningN-m load sequences.
Table 1 shows the correlation coefficients between the electrical, thermal, and cold load sequences in 8 frequency bands, where the threshold δ is 0.05, and the load sequences with the absolute values of the correlation coefficients greater than the threshold are selected, that is, the load sequences in the frequency bands 1,2, 3, and 4 are used as the load sequences after screening.
Table 1: correlation coefficient corresponding to each load sequence under different frequency bands
Load sequence Electric-cold correlation coefficient Coefficient of electric-thermal correlation Coefficient of cold-heat correlation
1 0.9490 -0.7842 -0.8236
2 0.3315 -0.3613 -0.4292
3 0.6850 -0.1666 -0.1563
4 0.4857 0.0543 -0.1235
5 0.2259 -0.0568 0.0212
6 0.2951 -0.0489 0.0857
7 0.3029 0.0071 0.0110
8 0.3471 -0.1883 -0.0166
Further, dividing the screened load sequence into a training sample and a prediction sample, establishing different BP neural networks according to the type and frequency band of the screened load sequence, and subdividing the BP neural networks into the following steps:
normalizing the screened load sequence;
dividing the load sequence after normalization into a training sample and a prediction sample;
and establishing different BP neural networks according to the types and frequency bands of the screened load sequences.
Specifically, the screened load sequences are normalized, and the formula is as follows:
Figure BDA0002084112040000071
Figure BDA0002084112040000072
Figure BDA0002084112040000081
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0002084112040000082
respectively are electric, heat and cold load sequences at the time t after normalization processing in the frequency band i;
Figure BDA0002084112040000083
are respectively as
Figure BDA0002084112040000084
Minimum value of (d);
Figure BDA0002084112040000085
are respectively as
Figure BDA0002084112040000086
Is measured.
Normalizing the load sequence obtained by the time S
Figure BDA0002084112040000087
Figure BDA0002084112040000088
t 1, 2.. and S are used as training samples, and the load sequence after normalization processing acquired after the time S is used as a training sample
Figure BDA0002084112040000089
T is S +1, S +2, T as a prediction sample.
In this example, the selection of post-screening 2N8592 points of data in the first 358 days of the sequence of the electricity, heat and cold loads in the m frequency bands are used as training samples, and 168 points of data in the last 7 days are used as prediction samples.
According to 2 after screeningN-establishing BP neural network with different structures by electric, heat and cold load sequences in m frequency bands, wherein the number of input layer neurons is al,l＝1,2,...,2NM, number of neurons in the hidden layer bl,l＝1,2,...,2NM, number of neurons in the output layer cl,l＝1,2,...,2NM, training algebra dl,l＝1,2,...,2NM, and the neural network structure parameters of the load sequences under each frequency band are shown in the table 2.
Table 2: neural network structure parameter corresponding to different load sequences
Load sequence Implicit layer number Input-implicit-output layer neuron number Training algebra
1 1 20-12-1 20000
2 1 16-12-1 15000
3 1 12-12-1 10000
4 1 8-12-1 8000
Further, S4 is subdivided into the following steps:
inputting the normalized load sequence obtained before the moment k in the training sample into a corresponding BP neural network to obtain a prediction result of the load sequence at the moment k;
and (4) carrying out iterative training on the BP neural network until the prediction error reaches a set threshold value, and stopping training to obtain a load prediction model.
Specifically, g normalized load sequences before the time k to be predicted
Figure BDA0002084112040000091
Figure BDA0002084112040000092
And (d), inputting the BP neural networks of respective frequency bands and types, outputting load data at a time k to be predicted, and stopping iterative training when the prediction error of the output load data reaches a set threshold value beta, so as to obtain different BP neural network load prediction models.
In this embodiment, the load sequence after normalization processing 8 hours before the time k to be predicted is used as a training sample input to the BP neural network, the training sample is 8584 × 8 dimensional data, the output data is load data of the time k to be predicted, which is 8584 × 1 dimensional data, and the threshold β is set to 0.0001. And (3) realizing nonlinear mapping of input and output through iterative training, and storing a network structure to further obtain a corresponding load prediction model.
Further, S5 is subdivided into the following steps:
inputting the prediction samples into corresponding load prediction models to obtain a prediction result of each load sequence;
and adding the prediction results of the same load sequence under different frequency bands to obtain a load prediction result.
Further, after S5, the method further includes:
and performing inverse normalization processing on the load prediction result to obtain a normalized load prediction result.
Specifically, the processed load sequence is normalized
Figure BDA0002084112040000093
T, inputting the load prediction models of the frequency bands and the types where the load prediction models are located, and obtaining the prediction result of each load; respectively setting the electric, heat and cold load data at 2NAdding the predicted results of the load sequences under the m frequency bands to obtain a load predicted result; then, the load prediction results are subjected to inverse normalization processing to obtain final load prediction results of the electricity, heat and cold load data, which are respectively recorded as Lp(t)，Hp(t)，Fp(t)，t＝S+1,S+2,...,T。
As shown in FIGS. 3-5, experiments show that the curve of the prediction result of the electricity, heat and cold load data obtained by the method provided by the invention has high fitting degree with the curve of the actual electricity, heat and cold load data, and the effectiveness and feasibility of the method are verified.
Further, after performing inverse normalization processing on the load prediction result to obtain a normalized load prediction result, the method further includes:
and calculating the relative error and the average error of the load prediction result.
In order to further verify that the result of the load data predicted by the method provided by the invention has higher accuracy, the relative error and the average error of the load prediction result are respectively calculated, and the calculation formula is as follows:
Figure BDA0002084112040000101
Figure BDA0002084112040000102
Figure BDA0002084112040000103
Figure BDA0002084112040000104
Figure BDA0002084112040000105
Figure BDA0002084112040000106
wherein R isl(t)、Rh(t)、Rf(t) relative error of prediction of electrical, thermal and cold load data, Ml、Mh、MfThe average accuracy of the electrical, thermal and cold load data predictions, respectively.
Through calculation, the maximum relative errors of the electricity load data, the heat load data and the cold load data predicted by the method are respectively 4.82%, 7.10% and 6.99%, and the average accuracies of the electricity load data, the heat load data and the cold load data are respectively 97.57%, 97.42% and 96.87%. The prediction model provided by the present invention is compared with commonly used machine learning algorithm models (such as neural network models, support vector regression models), and the same prediction sample data is used, with the results as shown in table 3:
table 3: prediction accuracy comparison with machine learning
Figure BDA0002084112040000107
Figure BDA0002084112040000111
Then, the method provided by the invention is compared with a wavelet packet neural network method without considering the correlation in terms of time and precision, and the result is shown in the table 4:
table 4: comparison of various indexes of different prediction methods
Figure BDA0002084112040000112
The experimental result shows that the prediction method provided by the invention can effectively improve the accuracy of load prediction while shortening the training time.
The invention decomposes the load data into load sequences of different frequency bands by carrying out multi-layer wavelet packet transformation on the historical load data, screens out proper load sequences by calculating the correlation coefficient between different types of load sequences on each frequency band, and trains a neural network load prediction model by using the screened load sequences, thereby improving the coupling between energy sources in the comprehensive energy system and improving the accuracy of load prediction while considering the frequency characteristic of the load.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (7)

1. A comprehensive energy load prediction method based on a wavelet packet and a neural network is characterized by comprising the following steps:
acquiring historical load data;
respectively carrying out wavelet packet decomposition on different types of load data in the historical load data to obtain load sequences of each type of load data under different frequency bands;
calculating the correlation coefficient between every two different types of load sequences under the same frequency band;
screening out load sequences with relation numbers absolute values larger than a set threshold value under a certain frequency band;
dividing the screened load sequences into training samples and prediction samples, and establishing different BP neural networks according to the types and frequency bands of the screened load sequences;
inputting the training samples into a corresponding BP neural network to obtain a load prediction model;
and inputting the prediction sample into a corresponding load prediction model to obtain a load prediction result.
2. The method of claim 1, after said obtaining historical load data, further comprising:
filling missing data of the historical load data to obtain preprocessed historical load data;
correspondingly, the performing wavelet packet decomposition on different types of load data in the historical load data respectively to obtain load sequences of each type of load data under different frequency bands includes:
and respectively carrying out wavelet packet decomposition on different types of load data in the preprocessed historical load data to obtain load sequences of each type of load data under different frequency bands.
3. The method of claim 1, wherein the performing wavelet packet decomposition on different types of load data in the historical load data to obtain a load sequence of each type of load data in different frequency bands comprises:
decomposing different types of load data in the historical load data by adopting wavelet basis functions respectively to obtain a wavelet packet decomposition coefficient of each type of load data;
and performing single-branch reconstruction on the wavelet packet decomposition coefficient to obtain a load sequence of each load data under different frequency bands.
4. The method of claim 1, wherein the dividing the screened load sequences into training samples and prediction samples and establishing different BP neural networks according to the types and frequency bands of the screened load sequences comprises:
carrying out normalization processing on the screened load sequences;
dividing the load sequence after normalization into a training sample and a prediction sample;
and establishing different BP neural networks according to the types and frequency bands of the screened load sequences.
5. The method of claim 4, wherein inputting the training samples into a corresponding BP neural network to obtain a load prediction model comprises:
inputting the normalized load sequence obtained before the moment k in the training sample into a corresponding BP neural network to obtain a prediction result of the load sequence at the moment k;
and (4) carrying out iterative training on the BP neural network until the prediction error reaches a set threshold value, and stopping training to obtain a load prediction model.
6. The method of claim 1, wherein inputting the prediction samples into corresponding load prediction models to obtain load prediction results comprises:
inputting the prediction samples into corresponding load prediction models to obtain a prediction result of each load sequence;
and adding the prediction results of the same load sequence under different frequency bands to obtain a load prediction result.
7. The method of claim 6, wherein after adding the prediction results of the load sequences of the same kind in different frequency bands to obtain the load prediction results, the method further comprises:
and performing inverse normalization processing on the load prediction result to obtain a normalized load prediction result.
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