CN111950868B - Comprehensive energy system load scene generation method based on generation countermeasure network - Google Patents
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Abstract
The invention discloses a comprehensive energy system load scene generation method based on a generation countermeasure network, which comprises the following steps: s1: acquiring load data of a comprehensive energy system, and establishing a cold, heat and power load sample data set; s2: constructing and generating a confrontation network model; s3: alternately training a generator network and a discriminator network D; s4: and generating a comprehensive energy system load scene. The method utilizes the implicit relation of the generated confrontation network learning sample data to capture the internal complex deep nonlinear structural features. And when the real data is lacked or the diversity of the real data is insufficient, generating a comprehensive energy system multi-load scene with the statistical characteristics similar to the real scene. The scene generated by the method of the invention can provide decision support for load prediction, abnormal detection and operation scheduling, and the flexibility and reliability of the system are improved.
Description
Technical Field
The invention relates to a comprehensive energy system load scene generation method based on a generation countermeasure network, and belongs to the technical field of operation and analysis of comprehensive energy systems.
Background
In recent years, the degree of informatization and digitization has been increasing, and mining technology based on large data has become mature, and the value of data has been recognized and utilized. The comprehensive energy data with multi-source heterogeneity, complex relevance and real-time interactivity has huge information value, value information is extracted from the energy big data, a large number of scenes are calculated and analyzed, the change characteristics of loads are reflected, and decision support can be provided for key problems such as fault detection, load prediction, safety assessment and energy management. However, such data is often not readily available due to limitations in data privacy, data security, acquisition costs, etc., which prevents large-scale availability of data, posing challenges to further deployment of data-driven technologies in the energy field.
Therefore, it is necessary to provide a method for generating a comprehensive energy system scene, which finds and learns the potential association and the complex coupling in the training data when the real data is absent or the diversity of the real data is insufficient, mines the characteristics of the data, directly learns the probability distribution of the load time series data, and automatically generates a comprehensive energy system multi-load scene with the statistical characteristics similar to that of the real scene without explicitly modeling the probability distribution.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a comprehensive energy system load scene generation method based on a generation countermeasure network, which is used for generating a comprehensive energy system multi-element load scene with similar statistical characteristics to a real scene when real data is lacked or the diversity of the real data is insufficient.
In order to achieve the above object, the present invention provides a method for generating a comprehensive energy system load scenario based on a generation countermeasure network, comprising the following steps:
s1: acquiring load data of a comprehensive energy system, and establishing a cold, heat and power load sample data set;
s2: constructing and generating a confrontation network model;
s3: alternately training a generator network and a discriminator network D;
s4: and generating a comprehensive energy system load scene.
Preferably, the step S1 comprises the steps of:
s11: acquiring cold, heat and electricity load data of each day, acquiring N load values in one day, and acquiring the load values at the same interval;
s12: preprocessing the acquired load value, and completing lost data or correcting abnormal data;
s13: the load values are normalized according to the following equation, scaling the value of each datum to a value between 0 and 1:
in the formula: x denotes a sample data set, x = [ x = [ x ] 1 ，x 2 ，…，x n ]N is the total number of samples; x is the number of min Representing a minimum value in the sample data set; x is the number of max Representing a maximum value in the sample data set; x is the number of i Is the ith data in the sample data set, and the value range of i is [1, n ]]。
Preferably, the step S2 comprises the steps of:
s21: defining a generator model G consisting of a full-connection layer and four deconvolution layers, performing batch normalization processing after each calculation layer, and finally outputting by using a Sigmoid activation function;
wherein, the Sigmoid activation function is:
s22: defining a discriminator model D consisting of three convolution layers and a full-connection layer, wherein except the convolution layer of the first layer and the convolution layer of the third layer, the convolution layer of the second layer and the full-connection layer are subjected to batch normalization processing, and finally LeakyRelu is used as an activation function to be output;
wherein, leakyRelu activation function is:
preferably, the input to the generator model G is a set of random arrays of size 96 × 1.
Preferably, the filter sizes of the first, second and third convolutional layers are all 5 × 5.
Preferably, the step S3 comprises the steps of:
s31: the noise generating unit N generates a random noise sequence z of a certain length i ；
S32: random noise sequence z i Inputting a generator model G, and mapping the generator model G into a scene G (z) through a network of the generator model G;
s33: scene G (z) generated by generator model G and sampled training sample scene x i Inputting the values into a discriminator network D, wherein the discriminator network D respectively provides values D (x) and D (G (z)) representing the scene as true and false;
s34: respectively calculating loss functions of the discriminator model D and the generator model G according to the following formula;
L G ＝-E Z [D(G(z))]，
L D ＝-E X [D(x)+E Z [D(G(z))]]，
wherein L is G Representing the loss function of the generator, L D Representing the loss function of the arbiter, E Z Expected value, E, representing the corresponding random noise distribution X An expected value representing a corresponding true sample distribution;
s35: and reversely transmitting the result of the S34 to the generator model G and the discriminator model, and updating and adjusting the network parameter theta according to the following formula g And a network parameter theta d ；
Wherein, theta g,t-1 And theta d,t-1 The parameters of the generator network and the parameters of the discriminator network D are generated before the t-th round of training begins;
s36: repeating steps S31-S35 until the network parameter theta g And a network parameter theta d Until convergence.
Preferably, in step S4, the generator model G and the discriminator model D after training are fixed, and the generator model G is used for the random noise sequence z input from the input i And simulating to generate and output phase cooling heat and power load data.
The invention achieves the following beneficial effects:
the invention discloses a comprehensive energy system load scene generation method based on generation of a countermeasure network, which utilizes the implicit relation of the generated countermeasure network learning sample data to capture the internal complex deep nonlinear structure characteristics and generate new data with the statistical distribution characteristics similar to the real data. When the real data are lacked or the diversity of the real data is insufficient, the comprehensive energy system multi-load scene with the statistical characteristics similar to the real scene is automatically generated, explicit modeling on probability distribution is not needed, decision support is provided for abnormal detection and operation scheduling, the flexibility and the reliability of the system are improved, and the problem that the diversity of the real data is lacked or the diversity of the real data is insufficient is solved.
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FIG. 1 is a flow chart of the present invention;
FIG. 2 is a graph of loss function variation for the generator and arbiter training process of the present invention;
FIG. 3 is a comparison of generated scene and real scene sample data for the present invention;
fig. 4 is a comparison graph of probability distribution characteristics of generated scene and real scene data of the present invention.
Detailed Description
The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
A comprehensive energy system load scene generation method based on generation of a countermeasure network comprises the following steps:
s1: acquiring load data of a comprehensive energy system, and establishing a cold, heat and power load sample data set;
s2: constructing and generating a confrontation network model;
s3: alternately training a generator network and a discriminator network D;
s4: and generating a comprehensive energy system load scene.
Further, the step S1 includes the steps of:
s11: acquiring cold, heat and electricity load data of each day, acquiring N load values in one day, and acquiring the load values at the same interval;
s12: preprocessing the acquired load value, and completing lost data or correcting abnormal data;
s13: the load values are normalized, scaling the value of each datum to a value between 0 and 1, according to the following equation:
in the formula: x represents a sample data set, x = [ x ] 1 ，x 2 ，…，x n ]N is the total number of samples; x is the number of min Representing a minimum value in the sample data set; x is the number of max Representing a maximum value in the sample data set; x is a radical of a fluorine atom i Is the ith data in the sample data set, and the value range of i is [1, n ]]。
Further, the step S2 includes the steps of:
s21: defining a generator model G consisting of a full-connection layer and four deconvolution layers, performing batch normalization processing after each calculation layer, and finally outputting by using a Sigmoid activation function;
wherein, the Sigmoid activation function is:
s22: defining a discriminator model D consisting of three convolution layers and a full-link layer, wherein except the first convolution layer and the third convolution layer, the second convolution layer and the full-link layer are subjected to batch normalization processing, and finally LeakyRelu is used as an activation function to be output;
wherein, the LeakyRelu activation function is:
further, the input to the generator model G is a set of random arrays of size 96 × 1.
Furthermore, the filter sizes of the first layer of convolutional layer, the second layer of convolutional layer and the third layer of convolutional layer are all 5 × 5.
Further, the step S3 includes the steps of:
s31: the noise generation unit N generates a random noise sequence z of a certain length i ；
S32: random noise sequence z i Inputting a generator model G and mapping the generator model G into a scene G (z) through a network of the generator model G;
s33: scene G (z) generated by generator model G and sampled training sample scene x i Inputting the values into a discriminator network D, wherein the discriminator network D respectively provides values D (x) and D (G (z)) representing the scene as true and false;
s34: respectively calculating loss functions of the discriminator model G and the generator model G according to the following formula;
L G ＝-E Z [D(G(z))]，
L D ＝-E X [D(x)+E Z [D(G(z))]]，
wherein L is G Representing the loss function of the generator, L D Representing the loss function of the arbiter, E Z Expected value, E, representing the corresponding random noise distribution X An expected value representing a corresponding true sample distribution;
s35: the result of S34 is reversely propagated to the generator model G and the discriminator model, and the network parameter theta is updated and adjusted according to the following formula g And a network parameter theta d ；
Wherein, theta g,t-1 And theta d,t-1 The parameters of the generator network and the parameters of the discriminator network D are generated before the t round of training begins;
s36: repeating steps S31-S35 until the network parameter theta g And a network parameter theta d Until convergence.
Further, in step S4, the generator model G and the discriminator model D after training are fixed, and the generator model G is used for the random noise sequence z input from the input i And simulating to generate and output phase cooling heat and power load data.
Example (b):
the embodiment uses the load data of residents in a certain area from 2013 to 2017 to generate a typical scene.
The generator and arbiter training process loss function variation graph of this embodiment is shown in fig. 2. After 2000 times of iterative training, the loss functions of the generator and the discriminator are basically converged, which shows that the generated countermeasure network reaches the balance at the moment. The loss function of the generator steadily fluctuates around the value 0 after continuously decreasing, which indicates that the artificial sample distribution synthesized by the generator is very close to the real sample distribution.
And selecting partial curves from the test set to verify the generated scene. A comparison graph of sample data of a generated scene and a real scene is shown in fig. 3, a curve generated by the model has an output trend consistent with that of a real sample, and is in accordance with an actual load characteristic but not completely consistent with an original sample, and a consistent fluctuation rule is maintained between the two. This means that the model does not over-fit the data in the training set, but learns the inherent relevance of the sample data, and then generates completely new data, which indicates that the model has strong generalization capability.
And analyzing the probability distribution characteristics of the generated scene by adopting a Cumulative Distribution Function (CDF). The comparison graph of the probability distribution characteristics of the generated scene data and the real scene data of the invention is shown in FIG. 4. The CDF curve of the generated load scene set is highly fitted, the probability distribution characteristics of the generated scene are very similar to those of the historical scene, and the GAN model is proved to have the learning capability of learning the potential probability distribution of given data.
According to the comprehensive energy system load scene generation method, the comprehensive energy system load scene is generated by adopting the generation countermeasure network, the fluctuation and probability distribution characteristics of the load under different conditions can be effectively represented, and the load scene can be directly generated, so that the model has strong generalization capability. The samples are made to exhibit diversity of mode changes while ensuring that the generated data has a high degree of similarity to the actual scene.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (4)
1. A comprehensive energy system load scene generation method based on generation countermeasure network is characterized by comprising the following steps:
s1: acquiring load data of a comprehensive energy system, and establishing a cold, heat and power load sample data set;
s2: constructing and generating a confrontation network model;
s3: alternately training a generator model and a discriminator model D;
s4: generating a comprehensive energy system load scene;
the step S1 includes the steps of:
s11: acquiring cold, heat and electricity load data of each day, acquiring N load values in one day, and acquiring the load values at the same interval;
s12: preprocessing the acquired load value, and completing lost data or correcting abnormal data;
s13: the load values are normalized according to the following equation, scaling the value of each datum to a value between 0 and 1:
in the formula: x represents a sample data set, x = [ x ] 1 ，x 2 ，…，x i ，…，x n ]N is the total number of samples; x is the number of min Representing a minimum value in the sample data set; x is the number of max Representing a maximum value in the sample data set; x is a radical of a fluorine atom i Is the ith data in the sample data set, and the value range of i is [1, n ]]；
The step S2 includes the steps of:
s21: defining a generator model G consisting of a full-connection layer and four deconvolution layers, performing batch normalization processing after each calculation layer, and finally outputting by using a Sigmoid activation function;
wherein, the Sigmoid activation function is:
s22: defining a discriminator model D consisting of three convolution layers and a full-link layer, wherein except the first convolution layer and the third convolution layer, the second convolution layer and the full-link layer are subjected to batch normalization processing, and finally LeakyRelu is used as an activation function to be output;
wherein, the LeakyRelu activation function is:
the step S3 includes the steps of:
s31: the noise generation unit N generates a random noise sequence z of a certain length i ；
S32: random noise sequence z i Inputting a generator model G and mapping the generator model G into a scene G (z) through a network of the generator model G;
s33: scene G (z) generated by generator model G and sampled training sample scene x i Inputting the values into a discriminator model D, wherein the discriminator model D respectively gives values D (x) and D (G (z)) representing the scene as true and false;
s34: respectively calculating loss functions of the discriminator model D and the generator model G according to the following formula;
L G ＝-E Z [D(G(z))]，
L D ＝-E X [D(x)+E Z [D(G(z))]]，
wherein L is G Representing the loss function of the generator, L D Loss function representing the discriminator, E Z Expected value, E, representing the corresponding random noise distribution X An expected value representing a corresponding true sample distribution;
s35: the result of S34 is reversely propagated to the generator model G and the discriminator model, and the network parameter theta is updated and adjusted according to the following formula g，t And a network parameter theta d，t ；
θ g,t ＝θ g,t-1 -▽ln(1-D(G(z)))，
θ d,t ＝θ d,t-1 -▽ln(D(x))+ln(1-D(G(z)))，
Wherein, theta g,t-1 And theta d,t-1 The parameters of the generator network and the parameters of the discriminator network D are generated before the t-th round of training begins;
s36: repeating steps S31-S35 untilTo network parameter θ g，t And a network parameter θ d，t Until convergence.
2. The method as claimed in claim 1, wherein the generator model G is a set of random arrays with a size of 96 x 1.
3. The method for generating the integrated energy system load scene based on the generation countermeasure network of claim 1, wherein the filter sizes of the first layer of convolutional layer, the second layer of convolutional layer and the third layer of convolutional layer are all 5 x 5.
4. The method as claimed in claim 1, wherein in step S4, the generator model G and the discriminator model D are fixed, and the generator model G is used for the random noise sequence z input from the input i And (4) generating and outputting phase cooling, heating and power load data in a simulation mode.
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