New energy generation power prediction system based on deep learning

Abstract

The invention discloses a new energy power generation power prediction system based on deep learning, and the system comprises a wind power climbing frame construction module which is used for constructing an information hierarchical frame of new energy power generation power, a natural language processing module, a trend information real-time correction module, a regional meteorological analysis module, and a prediction module. According to the invention, not only is the influence of active and reactive power flow power data at different nodes on a wind power climbing event introduced, but also the power flow power information when the wind power climbs can be corrected in real time; after correction, the new energy power generation environment numerical value result after regional meteorological analysis is combined for fusion prediction, more comprehensive prediction model input is formed, the accuracy and reliability of data are guaranteed, and prediction and judgment work of new energy power generation in the face of a wind power climbing event is facilitated.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy data processing, and in particular to a new energy power generation prediction system based on deep learning. Background Art

[0002] The large changes in wind power generated by wind power in a short period of time are called wind power ramp events. Wind power ramp events can cause a short-term imbalance in power generation and consumption in the power system, threatening the safe operation of the power grid. The existing prediction methods for wind power ramp events can be roughly divided into two categories: indirect prediction method and direct prediction method. The indirect prediction method refers to first predicting the wind power time series with the help of wind power prediction technology, and then using the definition of ramp events to detect the occurrence of ramp events based on the prediction results; the direct prediction method uses historical sample data to mine the relationship between the ramp feature quantity and the regional meteorological information, and establishes a mapping from meteorological information to the ramp feature quantity, without the need to predict wind power in advance.

[0003] However, the existing new energy power generation prediction system uses a relatively simple direct prediction method to predict the regional meteorological information obtained for the prediction of wind power ramping events, and does not take into account the current flow information of the wind power grid power generation. Since the flow information of the wind power grid power generation has a greater impact on wind power ramping events, and there is no real-time correction of the current wind power grid power generation flow information and the fusion prediction process with the regional meteorological information, so in the scenario of multi-dimensional spatial range and time scale, facing multi-level and multi-objective influencing events, the prediction effect and accuracy of new energy power generation when facing wind power ramping events are often average. Summary of the invention

[0004] In view of the above problems existing in the existing data processing of wind power ramping, the present invention is proposed.

[0005] Therefore, one of the objects of the present invention is to provide a new energy power generation prediction system based on deep learning, which collects the real-time flow information of the wind power grid power generation and makes real-time corrections, and then integrates the prediction with the regional meteorological information, which has a multi-level and multi-objective influencing event evaluation function, and its prediction data is richer and more accurate.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a new energy power generation prediction system based on deep learning, comprising:

[0007] Wind power ramping framework construction module, used to construct the information hierarchical framework of renewable energy power generation, divide and configure the renewable energy power generation prediction hierarchy and the matching power generation related feature words, and generate information frameworks of renewable energy related data of different levels and targets according to the renewable energy power generation prediction hierarchy and the matching power generation related feature words;

[0008] A natural language processing module is used to receive a power generation prediction request sent by a new energy power station, perform semantic analysis on the power generation prediction request according to a pre-trained natural language processing model, and obtain a corresponding information hierarchy framework and matching power generation related feature words;

[0009] The real-time correction module of the power flow information is used to respond to the information framework of the wind power ramp framework construction module and the various power generation related feature words processed by the natural language processing module, determine the power flow information of the new energy power generation and the information framework information of the matching level, analyze the comprehensive influence of the power flow information under the current information framework according to the power flow information and the information framework information of the matching level, judge whether the power flow information should be corrected according to the comprehensive influence of the power flow information under the current level framework, build a power flow information correction model based on the Newton-Raphson method to complete the correction, and generate the power flow information correction result;

[0010] A regional meteorological analysis module is used to obtain regional meteorological information and receive power generation-related feature words from the natural language processing module under given initial conditions and boundary conditions, and to perform numerical environmental prediction of new energy power generation based on the power generation-related feature words and the obtained regional meteorological information to generate numerical results of the new energy power generation environment;

[0011] The fusion prediction module is used to receive the numerical results of the new energy power generation environment and the correction results of the power flow information, perform fusion prediction of the new energy power generation power, generate and output the fusion prediction results of the new energy power generation power.

[0012] As a preferred solution of the present invention, the real-time correction module of the power flow information analyzes the comprehensive influence of the power flow information under the current information framework according to the power flow information and the information framework information of the matching level, specifically by constructing a comprehensive influence evaluation model of the power flow information;

[0013] The power flow information and the information framework information of the matching level are input into the comprehensive impact assessment model of the power flow information to conduct a comprehensive impact assessment, that is, according to the risk confidence of each layer of information in the hierarchical framework, the comprehensive impact of the power flow information under the current information framework is calculated as follows:

[0014]

[0015] in, is the score of the current information framework E on the power flow information xi, infR e,xi is the evaluation value of the data at the e-th level of the current information framework, and e is the e-th level of information framework E.

[0016] As a preferred solution of the present invention, the comprehensive impact assessment model of the flow power information adopts a fuzzy evaluation method to perform assessment based on the power generation correlation characteristics of the input flow power information, specifically establishes a factor set for the comprehensive impact assessment of the flow power information, determines the evaluation set for the comprehensive impact assessment of the flow power information, determines the weights of the power generation correlation characteristics of the flow power information, constructs a fuzzy comprehensive evaluation matrix and determines the membership function through expert scoring to form a fuzzy evaluation matrix, and converts the fuzzy vectors of each feature weight on the comprehensive impact assessment factor set of the flow power information into fuzzy vectors on the comprehensive impact assessment evaluation set of the flow power information through fuzzy transformation to obtain the evaluation value of the flow power information.

[0017] As a preferred solution of the present invention, a power flow information correction model is constructed based on the Newton-Raphson method to complete the correction, which is expressed as follows:

[0018]

[0019] Among them, S ij It is represented as the transmission power from node i to node j, P ij represents the active power from node i to node j, Q i represents the reactive power from node i to node j, * represents conjugate, represents the ground admittance of node i, represents the branch admittance between node i and node j, V i represents the voltage of the i-th node, represents the vector value of the voltage at the i-th node, Represents the vector value of the voltage at the jth node.

[0020] As a preferred solution of the present invention, the real-time correction module of the power flow information inputs the basic parameters of the nodes, branches and generators of the new energy power generation system to form an admittance matrix;

[0021] Assume that the system has a total of n nodes and m PQ nodes. Since there is only one balanced node, there are a total of nm-1 PV nodes. For all PQ nodes and PV nodes, establish the unbalanced equations of active power and reactive power. Then, based on the Jacobian matrix, after solving the correction equation, add the correction amount to the voltage of each node and return to continue the iterative processing.

[0022] As a preferred solution of the present invention, the numerical environment prediction of the new energy power generation of the regional meteorological analysis module adopts a regression algorithm to analyze and calculate the numerical results of the new energy power generation environment, as follows:

[0023] P=α0+α1β1+α2β2+...+α m βm +ε;

[0024] Where P is the predicted probability value, that is, the numerical result of the new energy power generation environment, α1 to α m are the coefficients of the linear regression model for regional meteorological analysis. The coefficients are obtained by training the historical data set. m is the eigenvector of the features associated with the generated power, and ε is the error term.

[0025] As a preferred solution of the present invention, the fusion prediction module performs fusion prediction of new energy power generation by using the covariance cross fusion method to combine the numerical results of the new energy power generation environment and the correction results of the power flow information, outputs the estimated value of the state after the fusion state, and determines it as the fusion prediction result of the new energy power generation; as follows:

[0026]

[0027] Among them, R ab is the estimated value of the state after the fusion state, n is the total number of the i-th node, and w n is the weight value of node n, and P ab is the estimated variance value after fusion, P n is the local estimated variance of node n, R n is the fusion value of node n.

[0028] As a preferred solution of the present invention, generating and outputting the fusion prediction results of the new energy power generation power also includes adopting the rolling prediction method and calling the deep belief network predict function to predict the future power data.

[0029] As a preferred solution of the present invention, the invention further comprises:

[0030] A new energy power generation knowledge graph construction module is used to receive the power generation related feature words of the natural language processing module under given initial conditions and boundary conditions, and associate the scheduling plan of new energy power generation with the power generation related feature words to establish a new energy power generation scheduling plan knowledge graph;

[0031] The new energy power generation dispatching module is used to receive the fusion prediction results of the new energy power generation, match the new energy power generation adjustment plan in the knowledge graph of the new energy power generation dispatching plan, and perform visual presentation and output.

[0032] Beneficial effects of the present invention: The present invention collects real-time flow information of wind power grid power generation and makes real-time corrections, and in the process of integrating and predicting with regional meteorological information, it has a multi-level and multi-objective impact event evaluation function, and its prediction data is richer and more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0034] Figure 1 It is a schematic diagram of the modular structure of the system of the present invention;

[0035] Figure 2 It is an analysis flow chart of the real-time correction module of the current information in the system of the present invention;

[0036] Figure 3 It is a modular schematic diagram of the wind power ramp generation power correlation characteristics of the present invention. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0038] Reference Figure 1-Figure 3 , is an embodiment of the present invention, which provides a new energy power generation prediction system based on deep learning, including:

[0039] Wind power ramping framework construction module, used to construct the information hierarchical framework of renewable energy power generation, divide and configure the renewable energy power generation prediction hierarchy and the matching power generation related feature words, and generate information frameworks of renewable energy related data of different levels and targets according to the renewable energy power generation prediction hierarchy and the matching power generation related feature words;

[0040] A natural language processing module is used to receive a power generation prediction request sent by a new energy power station, perform semantic analysis on the power generation prediction request according to a pre-trained natural language processing model, and obtain a corresponding information hierarchy framework and matching power generation related feature words;

[0041] like Figure 3 As shown, it is further explained that the wind power ramp power generation related characteristics include wind speed measurement value, wind vector measurement value, temperature measurement value, humidity measurement value, etc.;

[0042] The real-time correction module of the power flow information is used to respond to the information framework of the wind power ramp framework construction module and the various power generation related feature words processed by the natural language processing module, determine the power flow information of the new energy power generation and the information framework information of the matching level, analyze the comprehensive influence of the power flow information under the current information framework according to the power flow information and the information framework information of the matching level, judge whether the power flow information should be corrected according to the comprehensive influence of the power flow information under the current level framework, build a power flow information correction model based on the Newton-Raphson method to complete the correction, and generate the power flow information correction result, such as Figure 2 As shown;

[0043] The regional meteorological analysis module is used to obtain the regional meteorological information and receive the power generation related feature words from the natural language processing module under given initial conditions and boundary conditions, and to perform numerical environment prediction of new energy power generation based on the power generation related feature words and the obtained regional meteorological information to generate the new energy power generation environment numerical results;

[0044] The fusion prediction module is used to receive the numerical results of the new energy power generation environment and the correction results of the power flow information, perform fusion prediction of the new energy power generation power, generate and output the fusion prediction results of the new energy power generation power.

[0045] Based on the above, it can be known that this embodiment first constructs an information framework for predicting wind power ramping events based on natural language processing, performs feature processing on the flow information of wind power generation power, and matches it in the corresponding hierarchical framework, and then analyzes the comprehensive influence of the flow power information under the current information framework based on the flow power information and the information framework information of the matched level; in this way, not only the influence of active and reactive flow power data at different nodes on wind power ramping events is introduced, but also the flow power information can be corrected in real time when encountering wind power ramping, and after correction, it is combined with the numerical results of the new energy power generation environment after regional meteorological analysis to perform fusion prediction, forming a more comprehensive prediction model input, ensuring the accuracy and reliability of the data, and facilitating the prediction and evaluation of new energy power generation when facing wind power ramping events.

[0046] Specifically, in this embodiment, the power flow information real-time correction module analyzes the comprehensive influence of the power flow information under the current information framework according to the power flow information and the information framework information of the matching level, specifically by constructing a comprehensive influence evaluation model of the power flow information;

[0047] The power flow information and the information framework information of the matching level are input into the comprehensive impact assessment model of the power flow information to conduct a comprehensive impact assessment, that is, according to the risk confidence of each layer of information in the hierarchical framework, the comprehensive impact of the power flow information under the current information framework is calculated as follows:

[0048]

[0049] in, is the score of the current information framework E on the power flow information xi, infR e,xi is the evaluation value of the data at the e-th level of the current information framework, and e is the e-th level of information framework E.

[0050] It should be emphasized in the present embodiment that the comprehensive impact assessment model of the flow power information adopts a fuzzy evaluation method to perform assessment based on the power generation correlation characteristics of the input flow power information. Specifically, a factor set for the comprehensive impact assessment of the flow power information is established, an evaluation set for the comprehensive impact assessment of the flow power information is determined, and the weights of the power generation correlation characteristics of the flow power information are determined. A fuzzy comprehensive evaluation matrix is ​​constructed and the membership function is determined by expert scoring to form a fuzzy evaluation matrix. The fuzzy transformation is used to convert the fuzzy vectors of the feature weights on the comprehensive impact assessment factor set of the flow power information into fuzzy vectors on the evaluation set of the comprehensive impact assessment of the flow power information to obtain the assessment value of the flow power information.

[0051] Specifically, in this embodiment, a power flow information correction model is constructed based on the Newton-Raphson method to complete the correction, which is expressed as follows:

[0052]

[0053] Among them, S ij It is represented as the transmission power from node i to node j, P ij represents the active power from node i to node j, Q i represents the reactive power from node i to node j, * represents conjugate, represents the ground admittance of node i, represents the branch admittance between node i and node j, V i represents the voltage of the i-th node, represents the vector value of the voltage at the i-th node, Represents the vector value of the voltage at the jth node.

[0054] Specifically, in this embodiment, the real-time correction module of the power flow information inputs the basic parameters of the nodes, branches and generators of the new energy power generation system to form an admittance matrix;

[0055] Assume that the system has a total of n nodes and m PQ nodes. Since there is only one balanced node, there are a total of nm-1 PV nodes. For all PQ nodes and PV nodes, establish the unbalanced equations of active power and reactive power. Then, based on the Jacobian matrix, after solving the correction equation, add the correction amount to the voltage of each node and return to continue the iterative processing.

[0056] In this embodiment, preferably, the numerical environment prediction of the new energy power generation of the regional meteorological analysis module adopts a regression algorithm to analyze and calculate the numerical results of the new energy power generation environment, as follows:

[0057] P=α0+α1β1+α2β2+...+α m β m +ε;

[0058] Where P is the predicted probability value, that is, the numerical result of the new energy power generation environment, α1 to α m are the coefficients of the linear regression model for regional meteorological analysis. The coefficients are obtained by training the historical data set. m is the eigenvector of the features associated with the generated power, and ε is the error term.

[0059] Specifically, in this embodiment, the fusion prediction module performs fusion prediction of new energy power generation by using the covariance cross fusion method to combine the numerical results of the new energy power generation environment and the correction results of the power flow information, outputs the state estimation value after the fusion state, and determines it as the fusion prediction result of the new energy power generation; as follows:

[0060]

[0061] Among them, R ab is the estimated value of the state after the fusion state, n is the total number of the i-th node, and w n is the weight value of node n, and P ab is the estimated variance value after fusion, P n is the local estimated variance of node n, R n is the fusion value of node n.

[0062] Specifically, in this embodiment, when generating and outputting the fusion prediction result of the new energy power generation, it also includes using the rolling prediction method and calling the deep belief network predict function to predict the future power data. Further explanation: In the process of processing wind power data, the wavelet transform can extract the time domain and frequency domain information of the time series data, which is helpful to capture the sudden change of wind power, which is the key feature of wind power ramping events. Generally, a deep belief network (DBN) is used. Through feature adaptive selection and data wavelet decomposition, DBN can build an accurate wind power ramping event prediction model.

[0063] Since it is often necessary to perform subsequent control operations such as scheduling after obtaining the corresponding prediction of renewable energy power generation after deep learning-based analysis and processing, and to provide auxiliary reference for power personnel, the renewable energy power generation prediction system based on deep learning of this embodiment further includes:

[0064] A new energy power generation knowledge graph construction module is used to receive the power generation related feature words from the natural language processing module under given initial conditions and boundary conditions, and associate the scheduling plan of new energy power generation with the power generation related feature words to establish a new energy power generation scheduling plan knowledge graph;

[0065] The new energy power generation dispatching module is used to receive the fusion prediction results of the new energy power generation, match the new energy power generation adjustment plan in the knowledge graph of the new energy power generation dispatching plan, and perform visual presentation and output.

[0066] Based on the above, the present invention collects the real-time flow information of the wind power grid power generation and makes real-time corrections, and integrates the prediction with the regional meteorological information, so as to have the function of multi-level and multi-objective impact event evaluation. In this process, the richness and accuracy of the prediction data are improved by correcting the wind power data in real time. Specifically, the invention involves the following key functions and effects, as follows:

[0067] Real-time power flow information collection: First, it is necessary to collect the power generation data of the wind power grid in real time. These data are the basis for accurate prediction.

[0068] Real-time data correction: The collected data will be corrected in real time to ensure the accuracy and reliability of the data.

[0069] Regional meteorological information fusion: The corrected wind power data is integrated with regional meteorological information to form a more comprehensive forecast model input.

[0070] Multi-level and multi-objective impact event evaluation: In this process, the invention has the function of evaluating impact events at different levels and with different objectives, which helps to analyze and predict changes in wind power from multiple perspectives.

[0071] Richness and accuracy of forecast data: Through the above steps, the forecast data finally obtained is not only rich in content but also highly accurate, providing strong support for the dispatching and operation of the power grid.

[0072] This prediction method based on data-driven and meteorological information fusion can better cope with the volatility and uncertainty of wind power, and provide an important decision-making basis for the stable operation and optimized dispatch of the power grid. Through real-time correction and multi-level evaluation, the present invention can effectively improve the accuracy of wind power prediction and reduce the risk of power grid operation.

[0073] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function according to the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium.

[0074] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples, unless they are contradictory.

[0075] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0076] Any process or method description in the flow chart or otherwise described herein can be understood to represent a module, fragment or portion of a code including one or more executable instructions for implementing the steps of a specific logical function or process. And the scope of the preferred embodiment of the present application includes other implementations, in which the functions may not be performed in the order shown or discussed, including in a substantially simultaneous manner or in a reverse order according to the functions involved.

[0077] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, which can be embodied in any computer-readable medium for use by an instruction execution system, apparatus or device (such as a computer-based system, a system including a processor or other system that can fetch instructions from an instruction execution system, apparatus or device and execute instructions), or used in combination with these instruction execution systems, apparatuses or devices.

[0078] It should be understood that the various parts of the present application can be implemented with hardware, software, firmware or a combination thereof. In the above embodiments, multiple steps or methods can be implemented with software or firmware stored in a memory and executed by a suitable instruction execution system. All or part of the steps of the above embodiment method can be completed by instructing the relevant hardware through a program, which can be stored in a computer-readable storage medium, and when the program is executed, it includes one of the steps of the method embodiment or a combination thereof.

[0079] In addition, each functional unit in each embodiment of the present application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into one module. The above-mentioned integrated module can be implemented in the form of hardware or in the form of a software functional module. If the above-mentioned integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. The storage medium can be a read-only memory, a disk or an optical disk, etc.

[0080] The above are only specific implementations of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A new energy power generation prediction system based on deep learning, characterized in that: include: Wind power ramping framework construction module, used to construct the information hierarchical framework of renewable energy power generation, divide and configure the renewable energy power generation prediction hierarchy and the matching power generation related feature words, and generate information frameworks of renewable energy related data of different levels and targets according to the renewable energy power generation prediction hierarchy and the matching power generation related feature words; A natural language processing module is used to receive a power generation prediction request sent by a new energy power station, perform semantic analysis on the power generation prediction request according to a pre-trained natural language processing model, and obtain a corresponding information hierarchy framework and matching power generation related feature words; The real-time correction module of the power flow information is used to respond to the information framework of the wind power ramp framework construction module and the various power generation related feature words processed by the natural language processing module, determine the power flow information of the new energy power generation and the information framework information of the matching level, analyze the comprehensive influence of the power flow information under the current information framework according to the power flow information and the information framework information of the matching level, judge whether the power flow information should be corrected according to the comprehensive influence of the power flow information under the current level framework, build a power flow information correction model based on the Newton-Raphson method to complete the correction, and generate the power flow information correction result; A regional meteorological analysis module is used to obtain regional meteorological information and receive power generation-related feature words from the natural language processing module under given initial conditions and boundary conditions, and to perform numerical environmental prediction of new energy power generation based on the power generation-related feature words and the obtained regional meteorological information to generate numerical results of the new energy power generation environment; The fusion prediction module is used to receive the numerical results of the new energy power generation environment and the correction results of the power flow information, perform fusion prediction of the new energy power generation power, generate and output the fusion prediction results of the new energy power generation power.

2. A new energy power generation prediction system based on deep learning as claimed in claim 1, characterized in that: The power flow information real-time correction module analyzes the comprehensive influence of the power flow information under the current information framework according to the power flow information and the information framework information of the matching level, specifically by constructing a comprehensive influence evaluation model of the power flow information; The power flow information and the information framework information of the matching level are input into the comprehensive impact assessment model of the power flow information to conduct a comprehensive impact assessment, that is, according to the risk confidence of each layer of information in the hierarchical framework, the comprehensive impact of the power flow information under the current information framework is calculated as follows: in, For the current information frame E, the power flow information xi Rating, infR e,xi is the evaluation value of the data at the e-th level of the current information framework, and e is the e-th level of information framework E.

3. A new energy power generation prediction system based on deep learning as claimed in claim 2, characterized in that: The comprehensive impact assessment model of the flow power information adopts a fuzzy evaluation method to perform assessment according to the power generation correlation characteristics of the input flow power information, specifically establishes a factor set for comprehensive impact assessment of the flow power information, determines an evaluation set for comprehensive impact assessment of the flow power information, determines the weights of the power generation correlation characteristics of the flow power information, constructs a fuzzy comprehensive evaluation matrix and determines the membership function through expert scoring to form a fuzzy evaluation matrix, and converts the fuzzy vectors of each feature weight on the comprehensive impact assessment factor set of the flow power information into a fuzzy vector on the comprehensive impact assessment evaluation set of the flow power information through fuzzy transformation to obtain an assessment value of the flow power information.

4. A new energy power generation prediction system based on deep learning as claimed in claim 1, characterized in that: The correction is completed by building a power flow information correction model based on the Newton-Raphson method, which is expressed as follows: Among them, S ij It is represented as the transmission power from node i to node j, P ij represents the active power from node i to node j, Q i represents the reactive power from node i to node j, * represents conjugate, represents the ground admittance of node i, represents the branch admittance between node i and node j, V i represents the voltage of the i-th node, represents the vector value of the voltage at the i-th node, Represents the vector value of the voltage at the jth node.

5. A new energy power generation prediction system based on deep learning as claimed in claim 4, characterized in that: The real-time correction module of the power flow information inputs the basic parameters of the nodes, branches and generators of the new energy power generation system to form an admittance matrix; Assume that the system has a total of n nodes and m PQ nodes. Since there is only one balanced node, there are a total of nm-1 PV nodes. For all PQ nodes and PV nodes, establish the unbalanced equations of active power and reactive power. Then, based on the Jacobian matrix, after solving the correction equation, add the correction amount to the voltage of each node and return to continue the iterative processing.

6. A new energy power generation prediction system based on deep learning as claimed in claim 1, characterized in that: The numerical environmental prediction of new energy power generation in the regional meteorological analysis module uses a regression algorithm to analyze and calculate the numerical results of the new energy power generation environment, as follows: P=α0+α1β1+α2β2+...+α m b m +e; Where P is the predicted probability value, that is, the numerical result of the new energy power generation environment, α1 to α m are the coefficients of the linear regression model for regional meteorological analysis. The coefficients are obtained by training the historical data set. m is the eigenvector of the features associated with the generated power, and ε is the error term.

7. A new energy power generation prediction system based on deep learning as claimed in claim 1, characterized in that: The fusion prediction module performs fusion prediction of new energy power generation by combining the numerical results of the new energy power generation environment and the correction results of the power flow information by using the covariance cross fusion method, outputs the estimated value of the state after the fusion state, and determines it as the fusion prediction result of the new energy power generation; as follows: Among them, R ab is the estimated value of the state after the fusion state, n is the total number of the i-th node, and w n is the weight value of node n, and P ab is the estimated variance value after fusion, P n For Node n The local estimated variance, R n is the fusion value of node n.

8. The new energy power generation prediction system based on deep learning according to claim 1, characterized in that: When generating and outputting the fusion prediction results of renewable energy power generation, it also includes using the rolling prediction method and calling the deep belief network predict function to predict future power data.

9. The new energy power generation prediction system based on deep learning according to claim 1, characterized in that: Also includes: A new energy power generation knowledge graph construction module is used to receive the power generation related feature words of the natural language processing module under given initial conditions and boundary conditions, and associate the scheduling plan of new energy power generation with the power generation related feature words to establish a new energy power generation scheduling plan knowledge graph; The new energy power generation dispatching module is used to receive the fusion prediction results of the new energy power generation, match the new energy power generation adjustment plan in the knowledge graph of the new energy power generation dispatching plan, and perform visual presentation and output.

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