Method and device for predicting energy load of energy internet and electronic equipment

Abstract

The invention provides an energy internet energy load prediction method and device and electronic equipment, and relates to the technical field of energy internet. The energy internet energy load prediction method comprises the steps of obtaining a load data sequence carrying noise of a target area in an energy internet in a preset time period; and processing the load data sequence carrying the noise by using the noise-free load amplitude prediction model to obtain the amplitude of the noise-free energy load data of the target area at the next moment. According to the invention, accurate prediction of noiseless load data of the energy internet can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of energy internet, and in particular to a method, device and electronic equipment for predicting energy load of energy internet. Background Art

[0002] As an emerging regional energy system, the efficient and robust operation of Energy Internet relies heavily on advanced artificial intelligence technologies such as neural networks. In order to achieve efficient operation of Energy Internet, timely and accurate load forecasting is essential.

[0003] However, in the actual operation of the Energy Internet, due to the influence of internal factors of the system and external security attacks, the collected load data often has a certain degree of noise. These noisy data will interfere with the normal operation of the Energy Internet, affect the accuracy of system modeling and optimization, and in extreme cases may even cause system failure or paralysis. When estimating the noise level of the collected load data, it is necessary to overcome the challenges brought by the uncertainty and randomness of the noise part and the original load data. In the relevant technology, there is still a lack of effective solutions.

[0004] Therefore, how to achieve accurate prediction of noise-free load data of the energy Internet is a technical problem that needs to be solved urgently. Summary of the invention

[0005] In view of the above problems existing in the prior art, the present invention provides a method, device and electronic device for predicting energy load of energy Internet, so as to realize accurate prediction of noise-free load data of energy Internet.

[0006] The present invention provides a method for predicting energy load of energy internet, comprising the following steps.

[0007] Acquire a noisy load data sequence of a target area in an energy internet within a preset time period; use a noise-free load amplitude prediction model to process the noisy load data sequence to obtain the amplitude of the noise-free energy load data of the target area at the next moment; wherein the noise-free load amplitude prediction model includes a denoising autoencoder network and an amplitude estimation network, the denoising autoencoder network is used to process the noisy load data sequence to obtain a noise-free load data sequence, and the amplitude estimation network is used to process the noise-free load data sequence to obtain the amplitude of the noise-free energy load data at the next moment.

[0008] According to a method for predicting energy load of an energy internet provided by the present invention, the denoising autoencoder network includes an encoder implemented by a ReLU function and a decoder implemented by a sigmoid function; the noise-free load data sequence is obtained by using the denoising autoencoder network in the following manner: the noisy load data sequence is processed by a ReLU function to obtain a load data representation after dimensionality reduction; based on the load data representation after dimensionality reduction, data is reconstructed by using a sigmoid function to obtain the noise-free load data sequence.

[0009] According to a method for predicting energy load of an energy internet provided by the present invention, the amplitude estimation network is a multi-layer backward propagation network; the amplitude estimation network is used to obtain the amplitude of the noise-free energy load data of the target area at the next moment in the following manner: the noise-free load data sequence is input into the multi-layer backward propagation network to obtain the amplitude of the noise-free energy load data of the target area at the next moment output by the multi-layer backward propagation network.

[0010] According to a method for predicting energy load of an energy Internet provided by the present invention, the method also includes: repeatedly executing the steps of obtaining a noisy load data sequence of the energy Internet, and processing the noisy load data sequence using a noise-free load amplitude prediction model, until the number of noisy load data sequences obtained reaches a sampling threshold; for each of the noisy load data sequences, determining the noise level of the noisy load data sequence according to the difference between the amplitude of the noisy load data sequence and the amplitude of the noise-free load data sequence corresponding to the noisy load data sequence; in response to the noise levels of the obtained noisy load data sequences being greater than a preset noise level threshold, determining that the load data sequence in the target area is subject to noise interference or signal attack during transmission.

[0011] According to a method for predicting energy load of an energy internet provided by the present invention, the sampling threshold is determined in the following manner: based on the prediction accuracy of the noise-free load amplitude prediction model, a single-step estimation probability of prediction error is obtained; wherein the prediction accuracy of the noise-free load amplitude prediction model is determined based on the difference between the noise level of a noise-carrying load data sequence obtained using the noise-free load amplitude prediction model and the actual noise level of the noise-carrying load data sequence; the sampling threshold is obtained based on the single-step estimation probability and a preset probability threshold.

[0012] According to a method for predicting energy load of an energy internet provided by the present invention, the noise-free load amplitude prediction model is trained in the following manner: a first training data set, a second training data set and a third training data set are obtained; wherein the first training data set includes a noise-carrying historical load data sequence as a sample and a noise-free historical load data sequence as a label, the second training data set includes a noise-free historical load data sequence as a sample and the amplitude of the load data at the next moment of the noise-free historical load data sequence as a label, and the third training data set includes a noise-carrying historical load data sequence as a sample and the amplitude of the load data at the next moment of the noise-free historical load data sequence as a label; using the first training data set, an initial denoising autoencoder network is trained to obtain a trained denoising autoencoder network; using the second training data set, an initial amplitude estimation network is trained to obtain a trained amplitude estimation network; using the third training data set, the trained denoising autoencoder network and the trained amplitude estimation network are jointly trained to obtain the trained noise-free load amplitude prediction model.

[0013] The present invention also provides a prediction device for energy load of an energy internet, comprising the following modules: a first acquisition module, used to obtain a noise-carrying load data sequence of a target area in the energy internet within a preset time period; a second acquisition module, used to use a noise-free load amplitude prediction model to process the noise-carrying load data sequence to obtain the amplitude of the noise-free energy load data of the target area at the next moment; wherein the noise-free load amplitude prediction model comprises a denoising autoencoder network and an amplitude estimation network, the denoising autoencoder network is used to process the noise-carrying load data sequence to obtain a noise-free load data sequence, and the amplitude estimation network is used to process the noise-free load data sequence to obtain the amplitude of the noise-free energy load data at the next moment.

[0014] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method for predicting energy load of the energy Internet as described in any one of the above-mentioned methods is implemented.

[0015] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements any of the above-described methods for predicting energy loads of the energy Internet.

[0016] The present invention also provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above-mentioned methods for predicting energy load of the energy Internet.

[0017] The energy load prediction method, device and electronic device of the energy internet provided by the present invention utilize a noise-free load amplitude prediction model to process a noise-carrying load data sequence of a target area in the energy internet within a preset time period, and obtain the amplitude of the noise-free energy load data of the target area at the next moment. By utilizing the powerful data processing capability and data feature learning capability of the machine learning model, the influence of the noise part of the load data sequence and the uncertainty and randomness of the load data sequence itself on the amplitude prediction accuracy of the load data can be effectively overcome, thereby realizing accurate prediction of noise-free load data of the energy internet. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 This is one of the flow charts of the energy load prediction method of the energy internet provided by the present invention.

[0020] Figure 2 This is the second flow chart of the method for predicting energy load of the energy Internet provided by the present invention.

[0021] Figure 3 It is a flow chart of a method for determining whether a load data sequence of a target area is subject to noise interference or signal attack during transmission provided by the present invention.

[0022] Figure 4 It is a flow chart of the training method of the noise-free load amplitude prediction model provided by the present invention.

[0023] Figure 5 It is a structural schematic diagram of the noise reduction autoencoding network provided by the present invention.

[0024] Figure 6 It is a schematic diagram of the structure of the multi-layer back-propagation network provided by the present invention.

[0025] Figure 7 It is a schematic diagram of the structure of the residual network provided by the present invention.

[0026] Figure 8 It is a structural schematic diagram of the energy load prediction device of the energy Internet provided by the present invention.

[0027] Fig. 9 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] Combine the following Figure 1-Figure 7 The present invention describes the method for predicting energy load of energy internet.

[0030] Figure 1 It is one of the flow charts of the energy load prediction method of the energy internet provided by the present invention, and the method is executed by the control system of the energy internet, such as Figure 1 As shown, the method includes the following: Step 101: Obtain a load data sequence carrying noise in a target area in the energy Internet within a preset time period.

[0031] The load data series of the target area refers to a series of data points that record the changes in the power load of the target area in the energy internet over time. These data points are usually arranged in chronological order and can reflect the fluctuations in the power demand of the energy internet in different time periods.

[0032] The preset time period refers to a time period of a certain length, such as 15 minutes or 30 minutes before the current moment, etc. In the specific implementation process, the selection of the prediction time period can be determined according to the processing length of the input data by the noise-free load amplitude prediction model (for detailed description, refer to the relevant content in step 102, which will not be repeated here).

[0033] The load data of the Energy Internet is usually collected through measuring equipment (such as smart meters, sensors, etc.) installed at key nodes of the Energy Internet, and transmitted to the control system for analysis and processing through various methods such as power line carrier, wireless public network, and fiber optic communication.

[0034] Step 102: Use the noise-free load amplitude prediction model to process the load data sequence carrying noise to obtain the amplitude of the noise-free energy load data of the target area at the next moment.

[0035] The noise-free load amplitude prediction model includes a denoising autoencoder network and an amplitude estimation network. The denoising autoencoder network is used to process a noisy load data sequence to obtain a noise-free load data sequence. The amplitude estimation network is used to process a noise-free load data sequence to obtain the amplitude of the noise-free energy load data at the next moment.

[0036] In the specific implementation process, Figure 5 As shown, the acquired noisy load data sequence can be input into the noise-free load amplitude prediction model; the noise-free load amplitude prediction model obtained for different training data can output the predicted amplitude of the load data at the next moment, or can output the predicted amplitude sequence of the load data sequence for the next time period (a sequence composed of predicted amplitudes at multiple time points), which is not limited by the description of this specification.

[0037] In the specific implementation process, a denoising autoencoder network can be constructed in a variety of ways, which are not limited to the description in this specification.

[0038] In the specific implementation process, the amplitude estimation network can be constructed in a variety of ways, which are not limited to the description in this specification.

[0039] For an embodiment of the denoising autoencoder network and the amplitude estimation network, see Figure 2 The relevant content in will not be repeated here.

[0040] For the training method of the noise-free load amplitude prediction model, see Figure 4 The relevant content in will not be repeated here.

[0041] Figure 2 This is the second flow chart of the energy load prediction method of the energy internet provided by the present invention. In this embodiment, the noise reduction autoencoder network includes an encoder implemented by a ReLU function and a decoder implemented by a sigmoid function, and the amplitude estimation network is a multi-layer back propagation network. Figure 2 As shown, the method includes the following: Step 201: Obtain a load data sequence carrying noise of the energy Internet.

[0042] For a detailed description of this step, please refer to the relevant content in step 101, which will not be repeated here.

[0043] Step 202: Use the ReLU function to process the noisy load data sequence to obtain a load data representation after dimensionality reduction.

[0044] The ReLU function is as follows: (1) in, For input data.

[0045] In the specific implementation process, the load data sequence carrying noise can be used as the input data of the ReLU function in turn. , after dimensionality reduction processing as shown in formula (1), the load data representation is obtained.

[0046] Step 203: Based on the load data representation after dimensionality reduction, the sigmoid function is used to reconstruct the data to obtain a noise-free load data sequence.

[0047] The sigmoid function is as follows (2) in, For input data.

[0048] In the specific implementation process, the load data obtained in step 202 can be expressed as the input data of the sigmoid function. , after data reconstruction as shown in formula (2), a noise-free load data sequence is obtained.

[0049] Step 204: input the noise-free load data sequence into the multi-layer backward propagation network to obtain the amplitude of the noise-free energy load data of the target area at the next moment output by the multi-layer backward propagation network.

[0050] like Figure 6 As shown, the multi-layer back propagation network includes an input layer, a hidden layer and an output layer. In some embodiments, the back propagation network can be Figure 7 The residual network shown improves the estimation performance of multi-layer backpropagation networks.

[0051] Figure 3 is a flow chart of a method for determining whether a load data sequence of a target area is subject to noise interference or signal attack during transmission provided by the present invention, such as Figure 3 As shown, the method includes the following: Step 301: Obtain a load data sequence carrying noise whose number reaches a sampling threshold.

[0052] In a specific implementation process, step 101 to step 102 may be repeatedly executed to obtain a plurality of continuous load data sequences carrying noise, and obtain the amplitude of the corresponding noise-free energy load data, until the number of acquired sequences reaches a sampling threshold.

[0053] In some embodiments, the sampling threshold may be determined by: According to the prediction accuracy of the noise-free load amplitude prediction model, the single-step estimation probability of prediction error is obtained; according to the single-step estimation probability and the preset probability threshold, the sampling threshold is obtained.

[0054] The prediction accuracy of the noise-free load amplitude prediction model is determined by the difference between the noise level of the load data sequence with noise obtained by using the noise-free load amplitude prediction model and the actual noise level of the load data sequence with noise (which can be obtained based on actual instrument measurement or simulation calculation). The calculation formula is as follows: (3) is the actual noise level, is the noise level of the load data sequence with noise obtained by the noise-free load amplitude prediction model, N is the number of sampling calculations, and level is the number of noise levels; is the prediction accuracy of the noise-free load amplitude prediction model.

[0055] One-step probability estimation The calculation formula is as follows: (4) in, is the prediction accuracy of the noise-free load amplitude prediction model, that is, .

[0056] As an example only, setting the preset probability threshold to a, the following formula can be used to estimate the probability based on the single step , and the preset probability threshold, to obtain the sampling threshold M.

[0057] (5) It can be seen from formula (5) that if more than M samples are judged to have a noise level greater than 1, the preset probability threshold a (for example, 0.99) can be used to determine that there is noise interference or signal attack behavior in the load data collection and transmission process of the energy Internet, thereby determining the size of M.

[0058] Step 302: for each noisy load data sequence, determine the noise level of the noisy load data sequence according to the difference between the amplitude of the noisy load data sequence and the amplitude of the noise-free load data sequence corresponding to the noisy load data sequence.

[0059] In the specific implementation process, the noise level of the noisy load data sequence can be determined in a variety of ways, which are not limited to the description of this specification. For example, the noise level of the noisy load data sequence can be determined by an adaptive classification algorithm (for example, a k-means clustering algorithm) according to the difference between the amplitude of the noisy load data sequence and the amplitude of the noise-free load data sequence corresponding to the noisy load data sequence. For another example, the noise level of the noisy load data sequence can be determined by noise threshold level quantization according to the difference between the amplitude of the noisy load data sequence and the amplitude of the noise-free load data sequence corresponding to the noisy load data sequence.

[0060] Step 303: In response to the noise levels of the acquired load data sequences carrying noise being greater than a preset noise level threshold, it is determined that the load data sequences in the target area are subject to noise interference or signal attack during transmission.

[0061] As an example only, assume that the noise level is 5 and the noise is always positive, and the noise level 1 is a noise-free situation. If the noise levels of the acquired load data sequences carrying noise are all greater than the preset noise level threshold 1, it can be considered that the load data sequence in the target area has noise interference or signal attack during the transmission process, and corresponding countermeasures can be taken, such as fault warning, safety warning, fault troubleshooting and other measures.

[0062] In the embodiment provided by the present invention, for each noisy load data sequence obtained, the noise level of the noisy load data sequence is determined according to the difference between the amplitude of the noisy load data sequence and the amplitude of the noise-free load data sequence corresponding to the noisy load data sequence; when the noise levels of the obtained noisy load data sequences are all greater than the preset noise level threshold, the load data sequence of the target area can be determined in a timely and reliable manner, and noise interference or signal attack exists during the transmission process, and countermeasures can be taken in a timely manner to improve the modeling and optimization performance of the energy Internet, thereby ensuring the stability and robustness of the operation of the energy Internet.

[0063] Figure 4 is a flow chart of the training method of the noise-free load amplitude prediction model provided by the present invention, such as Figure 4 As shown, the method includes the following: Step 401: Obtain a first training data set, a second training data set, and a third training data set.

[0064] The first training data set includes a historical load data sequence with noise as a sample and a historical load data sequence without noise as a label.

[0065] In the specific implementation process, the first training data set can be obtained in a variety of ways, which are not limited to the description of this specification. For example, the original load data sequence can be obtained, random noise can be added to the original load data sequence, and a historical load data sequence carrying noise can be obtained as sample data, and the original load data sequence can be used as a label for the sample data.

[0066] The second training data set includes a noise-free historical load data sequence as a sample and an amplitude of load data at the next moment of the noise-free historical load data sequence as a label.

[0067] In the specific implementation process, the second training data set can be obtained in a variety of ways, which are not limited to the description of this specification. For example, the original load data sequence can be divided into two time periods: the first time period and the second time period, and the load data of the first time period is used as the sample data, and the amplitude of the load data of the second time period is used as the label of the sample data. If the second time period includes multiple load data, the noise-free load amplitude prediction model obtained by training can output the amplitudes of multiple load data in the next time period; if the second time period includes 1 load data, the noise-free load amplitude prediction model obtained by training can output the amplitude of the load data at the next moment.

[0068] The third training data set includes a historical load data sequence with noise as a sample, and an amplitude of load data at the next moment of the historical load data sequence without noise as a label.

[0069] In the specific implementation process, the third training data set can be obtained in a variety of ways, which are not limited to the description of this specification. For example, the historical load data sequence carrying noise can be divided into two time periods: the first time period and the second time period, and the load data carrying noise in the first time period is used as sample data, and the amplitude of the noise-free load data in the second time period is used as the label of the sample data. If the second time period includes multiple load data, the noise-free load amplitude prediction model obtained by training can output the amplitudes of multiple load data in the next time period; if the second time period includes 1 load data, the noise-free load amplitude prediction model obtained by training can output the amplitude of the load data at the next moment.

[0070] In the specific implementation process, by collecting historical load data carrying noise and considering its collection density and frequency, the load forecast of the energy Internet in different time periods and different area sizes can be achieved.

[0071] Step 402: Use the first training data set to train the initial denoising autoencoder network to obtain a trained denoising autoencoder network.

[0072] In the specific implementation process, the first training data set can be used to train the denoising autoencoder network by greedy training layer by layer. When training a certain layer, the weight parameters of the layer are first randomly initialized, and then a recovery layer is added after the layer. The weight parameters of the layer are updated by minimizing the mean square error between the restored signal and the original signal. After that, the recovery layer is removed and the training of the next layer is continued. In this way, training is performed layer by layer until the training end condition is reached, such as model convergence, to obtain the trained denoising autoencoder network.

[0073] Step 403: Use the second training data set to train the initial amplitude estimation network to obtain a trained amplitude estimation network.

[0074] When training a multi-layer back propagation network, the second training data set can be used, the tanh function or the ReLU function can be used as the activation function, and the appropriate loss function (e.g., mean square error function) and optimization algorithm (e.g., gradient descent algorithm) can be combined for training. During the training process, the network will update the weight parameters through the back propagation algorithm according to the gradient of the loss function to minimize the value of the loss function until the training end condition is reached, such as model convergence, to obtain the trained amplitude estimation network.

[0075] The tanh function is shown below: (6) in, x For input data.

[0076] The Relu function is as follows: (7) in, x For input data.

[0077] Step 404: Use the third training data set to jointly train the trained denoising autoencoder network and the trained amplitude estimation network to obtain a trained noise-free load amplitude prediction model.

[0078] In the specific implementation process, in order to obtain higher prediction accuracy, the third training data set can be used to connect the trained denoising autoencoder network and the trained amplitude estimation network in series and then conduct joint training.

[0079] In the embodiment provided by the present invention, based on the (explicit or implicit) periodic characteristics of the load data of the energy Internet and the high correlation between the current noise distribution and the historical noise distribution, a noise-carrying historical load data sequence and a noise-free historical load data sequence are used to train a noise-free load amplitude prediction model that can accurately predict the load data amplitude of the load data sequence at the next moment. The noise-free load amplitude prediction model can then be used to timely and accurately predict the operating load of the energy Internet.

[0080] The following is a description of the energy load prediction device for the energy internet provided by the present invention. The energy load prediction device for the energy internet described below and the energy load prediction method for the energy internet described above can be referenced to each other.

[0081] Figure 8 Schematic diagram of the structure of the energy load prediction device of the energy internet provided by the present invention. Figure 8 As shown, the device 800 includes the following modules.

[0082] The first acquisition module 810 is used to acquire a load data sequence carrying noise in a target area in the energy Internet within a preset time period.

[0083] The second acquisition module 820 is used to process the noisy load data sequence using the noise-free load amplitude prediction model to obtain the amplitude of the noise-free energy load data of the target area at the next moment.

[0084] Among them, the noise-free load amplitude prediction model includes a denoising autoencoder network and an amplitude estimation network. The denoising autoencoder network is used to process the noisy load data sequence to obtain a noise-free load data sequence, and the amplitude estimation network is used to process the noise-free load data sequence to obtain the amplitude of the noise-free energy load data at the next moment.

[0085] Fig. 9 An example of a physical structure diagram of an electronic device is shown in FIG. Fig. 9 As shown, the electronic device may include: a processor 910, a communication interface 920, a memory 930 and a communication bus 940, wherein the processor 910, the communication interface 920 and the memory 930 complete mutual communication through the communication bus 940. The processor 910 may call the logic instructions in the memory 930 to execute the prediction method of the energy load of the energy Internet, the method comprising: obtaining a load data sequence carrying noise in a target area in the energy Internet within a preset time period; using a noise-free load amplitude prediction model, processing the load data sequence carrying noise, and obtaining the amplitude of the noise-free energy load data of the target area at the next moment; wherein the noise-free load amplitude prediction model comprises a noise reduction autoencoder network and an amplitude estimation network, the noise reduction autoencoder network is used to process the load data sequence carrying noise, and obtain the noise-free load data sequence, and the amplitude estimation network is used to process the noise-free load data sequence, and obtain the amplitude of the noise-free energy load data at the next moment.

[0086] In addition, the logic instructions in the above-mentioned memory 930 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0087] On the other hand, the present invention also provides a computer program product, which includes a computer program, which can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the energy Internet energy load prediction method provided by the above-mentioned methods, which includes: obtaining a noise-carrying load data sequence in a target area in the energy Internet within a preset time period; using a noise-free load amplitude prediction model to process the noise-carrying load data sequence to obtain the amplitude of the noise-free energy load data of the target area at the next moment; wherein the noise-free load amplitude prediction model includes a denoising autoencoder network and an amplitude estimation network, the denoising autoencoder network is used to process the noise-carrying load data sequence to obtain a noise-free load data sequence, and the amplitude estimation network is used to process the noise-free load data sequence to obtain the amplitude of the noise-free energy load data at the next moment.

[0088] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to execute the energy Internet energy load prediction method provided by the above-mentioned methods, the method comprising: obtaining a noise-carrying load data sequence of a target area in the energy Internet within a preset time period; using a noise-free load amplitude prediction model to process the noise-carrying load data sequence to obtain the amplitude of the noise-free energy load data of the target area at the next moment; wherein the noise-free load amplitude prediction model comprises a denoising autoencoder network and an amplitude estimation network, the denoising autoencoder network is used to process the noise-carrying load data sequence to obtain a noise-free load data sequence, and the amplitude estimation network is used to process the noise-free load data sequence to obtain the amplitude of the noise-free energy load data at the next moment.

[0089] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0090] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting energy load of energy internet, characterized in that: include: Obtaining a load data sequence carrying noise in a target area in the energy internet within a preset time period; Using a noise-free load amplitude prediction model, the load data sequence carrying noise is processed to obtain the amplitude of the noise-free energy load data of the target area at the next moment; Among them, the noise-free load amplitude prediction model includes a denoising autoencoder network and an amplitude estimation network. The denoising autoencoder network is used to process the noisy load data sequence to obtain a noise-free load data sequence, and the amplitude estimation network is used to process the noise-free load data sequence to obtain the amplitude of the noise-free energy load data at the next moment.

2. The method for predicting energy load of energy internet according to claim 1, characterized in that: The denoising autoencoder network includes an encoder implemented by a ReLU function and a decoder implemented by a sigmoid function; The noise-free load data sequence is obtained by using the noise reduction autoencoder network in the following manner: Processing the noisy load data sequence using a ReLU function to obtain a load data representation after dimensionality reduction; Based on the load data representation after dimensionality reduction, data reconstruction is performed using a sigmoid function to obtain the noise-free load data sequence.

3. The method for predicting energy load of energy internet according to claim 2, characterized in that: The amplitude estimation network is a multi-layer back-propagation network; The amplitude estimation network is used to obtain the amplitude of the noise-free energy load data of the target area at the next moment in the following manner: The noise-free load data sequence is input into the multi-layer backward propagation network to obtain the amplitude of the noise-free energy load data of the target area at the next moment output by the multi-layer backward propagation network.

4. The method for predicting energy load of energy internet according to claim 1, characterized in that: The method further comprises: Repeat the steps of obtaining a noisy load data sequence of the energy internet and processing the noisy load data sequence using a noise-free load amplitude prediction model until the number of the obtained noisy load data sequences reaches a sampling threshold; For each of the noisy load data sequences, determining a noise level of the noisy load data sequence according to a difference between an amplitude of the noisy load data sequence and an amplitude of the noise-free load data sequence corresponding to the noisy load data sequence; In response to the noise levels of the acquired load data sequences carrying noise being greater than a preset noise level threshold, it is determined that the load data sequences in the target area are subject to noise interference or signal attack during transmission.

5. The method for predicting energy load of energy internet according to claim 4 is characterized in that: The sampling threshold is determined in the following manner: According to the prediction accuracy of the noise-free load amplitude prediction model, a single-step estimation probability of prediction error is obtained; wherein the prediction accuracy of the noise-free load amplitude prediction model is determined according to the difference between the noise level of the load data sequence carrying noise obtained by using the noise-free load amplitude prediction model and the actual noise level of the load data sequence carrying noise; The sampling threshold is obtained according to the single-step estimation probability and a preset probability threshold.

6. The method for predicting energy load of energy internet according to any one of claims 1 to 3, characterized in that: The noise-free load amplitude prediction model is trained in the following manner: Acquire a first training data set, a second training data set, and a third training data set; wherein the first training data set includes a historical load data sequence with noise as a sample, and a historical load data sequence without noise as a label, the second training data set includes a historical load data sequence without noise as a sample, and the amplitude of the load data at the next moment of the historical load data sequence without noise as a label, and the third training data set includes a historical load data sequence with noise as a sample, and the amplitude of the load data at the next moment of the historical load data sequence without noise as a label; Using the first training data set, training the initial denoising autoencoder network to obtain a trained denoising autoencoder network; Using the second training data set, training the initial amplitude estimation network to obtain a trained amplitude estimation network; The trained denoising autoencoder network and the trained amplitude estimation network are jointly trained using the third training data set to obtain the trained noise-free load amplitude prediction model.

7. A device for predicting energy load of energy internet, characterized in that: include: The first acquisition module is used to acquire a load data sequence carrying noise in a target area in the energy Internet within a preset time period; A second acquisition module is used to process the load data sequence carrying noise by using a noise-free load amplitude prediction model to obtain the amplitude of the noise-free energy load data of the target area at the next moment; Among them, the noise-free load amplitude prediction model includes a denoising autoencoder network and an amplitude estimation network. The denoising autoencoder network is used to process the noisy load data sequence to obtain a noise-free load data sequence, and the amplitude estimation network is used to process the noise-free load data sequence to obtain the amplitude of the noise-free energy load data at the next moment.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, it implements the method for predicting energy load of the energy Internet as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for predicting energy load of the energy Internet as claimed in any one of claims 1 to 6 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for predicting energy load of the energy Internet as claimed in any one of claims 1 to 6 is implemented.

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