Method and system for evaluating availability of new energy

Abstract

The invention discloses a method and a system for evaluating the availability of new energy, and belongs to the technical field of evaluation of the availability of the new energy. The evaluation method comprises the steps of obtaining a historical power generation output sequence of new energy, and calculating historical new energy availability of the new energy in different dimensions based on the historical power generation output sequence; taking the historical power generation output sequence as input data of a training model, taking the historical new energy availability of different dimensions as output data corresponding to the access data, and training based on the input data and the output data to obtain an evaluation model; and based on the evaluation model, obtaining a generated output sequence of the target new energy, and calculating the generated output sequence to evaluate available outputs of the target new energy in different dimensions. According to the method, multi-dimensional availability evaluation can be carried out on new energy, and support is provided for development planning and scheduling operation of regional new energy, a power grid, adjustment resources and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy available capacity assessment, and more specifically, to a method and system for assessing new energy available capacity. Background Art

[0002] Since wind and light resources cannot be stored on a large scale, they need to be used as soon as they are generated; moreover, the power generated by renewable energy is random and volatile, and the power generation efficiency is relatively low. Wind turbines or photovoltaic power stations of the same capacity have different load-carrying capabilities from conventional thermal power or hydropower units. Therefore, in the power system redundancy analysis, renewable energy capacity cannot be treated the same as conventional units, which brings difficulties to the analysis of power balance in power planning and dispatching operations.

[0003] Under the goal of clean and low-carbon energy transformation, my country's new energy installed capacity continues to grow rapidly. After a high proportion of new energy is connected to the grid, higher requirements are put forward for system safety, power supply and clean energy consumption. The operation of the power grid may face the dilemma of "difficulty in absorbing large-scale power" and "difficulty in ensuring supply of small-scale power". There is an urgent need to conduct refined quantitative analysis and evaluation of the available capacity of new energy to provide support for the development planning and dispatching operation of regional new energy, power grids and regulation resources. Summary of the invention

[0004] In view of the above problems, the present invention proposes a method for evaluating the available capacity of new energy, comprising:

[0005] Obtaining a historical power generation output sequence of new energy, and based on the historical power generation output sequence, calculating the historical new energy available capacity of different dimensions of new energy;

[0006] Using the historical power generation output sequence as input data for a training model, using the historical renewable energy available capacity in different dimensions as output data corresponding to the input and output data, and training an evaluation model based on the input data and the output data;

[0007] Based on the evaluation model, the power generation output sequence of the target new energy is obtained, and the power generation output sequence is calculated to evaluate the available output of the target new energy in different dimensions.

[0008] Optionally, obtain the historical power generation output sequence of renewable energy, including:

[0009] The power generation data related to power generation of the new energy in the historical preset time period is obtained, the power generation data is preprocessed, and a historical power generation output sequence is generated based on the preprocessed power generation data.

[0010] Optional, power generation data, including:

[0011] Actual power generation data, available power generation data, abandoned power data, installed capacity data and operating capacity data of new energy.

[0012] Optionally, preprocessing the power generation data includes:

[0013] Traverse all power generation data, remove impure data and duplicate data in all power generation data, and restore the missing data.

[0014] Optionally, different dimensions include: different area dimensions, different time dimensions and different environment dimensions.

[0015] Optionally, before calculating the historical renewable energy available capacity in different dimensions, the historical power generation output sequence is classified according to different dimensions to obtain the historical power generation output sequence in different dimensions;

[0016] Performing normalization processing on the historical power generation processing sequence;

[0017] According to different dimensions, based on the historical power generation output sequence of the corresponding dimension after scalar processing, the historical new energy available capacity of different dimensions of new energy is calculated.

[0018] Optionally, training an evaluation model based on the input data and the output data includes:

[0019] The input data and the corresponding output data are divided according to a preset ratio to obtain a training set and a validation set, the training set is input into a pre-established network model, and the network model is used to learn the calculation process of obtaining the output data by calculating the input data in the training set, so as to adjust the model parameters of the network model and obtain an initial evaluation model;

[0020] The initial evaluation model is verified with the verification set. If the verification result meets the preset requirements, the initial evaluation model is used as the evaluation model. If the verification result does not meet the preset requirements, the parameters of the initial evaluation model are adjusted until the verification result meets the preset requirements.

[0021] Optionally, validating the initial evaluation model with the validation set includes:

[0022] The input data in the validation set is input into the initial evaluation model for calculation to obtain validation output data, and the validation output data is compared with the output data in the validation set to obtain a validation result.

[0023] Optionally, if the verification output data is compared with the output data in the verification set and the similarity is greater than 95%, it is determined that the verification result meets the preset requirement; otherwise, it does not meet the preset requirement.

[0024] Optionally, evaluate the model, including: sequence generator;

[0025] After the evaluation model obtains the power generation data of the target renewable energy, the power generation data is preprocessed based on the sequence generator to generate a power generation output sequence of the target renewable energy.

[0026] Optionally, a sequence generator is generated by learning from a process of preprocessing power generation data.

[0027] Optionally, the power generation output sequence is calculated to evaluate the target renewable energy available output in different dimensions, including:

[0028] Calculating the power generation output sequence to obtain an output value of the evaluation model;

[0029] Correcting the output value to obtain target new energy available output in different dimensions;

[0030] The target new energy available output in different dimensions includes:

[0031] The available output of target renewable energy sources at different stations / bases / regions, the available output of target renewable energy sources at different months / seasons / years, and the available output of target renewable energy sources at different temperatures / humidities / altitudes.

[0032] In another aspect, the present invention further provides a system for evaluating the available capacity of new energy, comprising:

[0033] A data acquisition unit, used to obtain a historical power generation output sequence of new energy, and based on the historical power generation output sequence, calculate the historical new energy available capacity of different dimensions of new energy;

[0034] A training model unit, configured to use the historical power generation output sequence as input data for a training model, use the historical renewable energy available capacity of different dimensions as output data corresponding to the input and output data, and train an evaluation model based on the input data and the output data;

[0035] The output unit is used to obtain the power generation output sequence of the target new energy based on the evaluation model, and calculate the power generation output sequence to evaluate the available output of the target new energy in different dimensions.

[0036] Optionally, obtain the historical power generation output sequence of renewable energy, including:

[0037] The power generation data related to power generation of the new energy in the historical preset time period is obtained, the power generation data is preprocessed, and a historical power generation output sequence is generated based on the preprocessed power generation data.

[0038] Optional, power generation data, including:

[0039] Actual power generation data, available power generation data, abandoned power data, installed capacity data and operating capacity data of new energy.

[0040] Optionally, preprocessing the power generation data includes:

[0041] Traverse all power generation data, remove impure data and duplicate data in all power generation data, and restore the missing data.

[0042] Optionally, different dimensions include: different area dimensions, different time dimensions and different environment dimensions.

[0043] Optionally, before calculating the historical renewable energy available capacity in different dimensions, the historical power generation output sequence is classified according to different dimensions to obtain the historical power generation output sequence in different dimensions;

[0044] Performing normalization processing on the historical power generation processing sequence;

[0045] According to different dimensions, based on the historical power generation output sequence of the corresponding dimension after scalar processing, the historical new energy available capacity of different dimensions of new energy is calculated.

[0046] Optionally, training an evaluation model based on the input data and the output data includes:

[0047] The input data and the corresponding output data are divided according to a preset ratio to obtain a training set and a validation set, the training set is input into a pre-established network model, and the network model is used to learn the calculation process of obtaining the output data by calculating the input data in the training set, so as to adjust the model parameters of the network model and obtain an initial evaluation model;

[0048] The initial evaluation model is verified with the verification set. If the verification result meets the preset requirements, the initial evaluation model is used as the evaluation model. If the verification result does not meet the preset requirements, the parameters of the initial evaluation model are adjusted until the verification result meets the preset requirements.

[0049] Optionally, validating the initial evaluation model with the validation set includes:

[0050] The input data in the validation set is input into the initial evaluation model for calculation to obtain validation output data, and the validation output data is compared with the output data in the validation set to obtain a validation result.

[0051] Optionally, if the verification output data is compared with the output data in the verification set and the similarity is greater than 95%, it is determined that the verification result meets the preset requirement; otherwise, it does not meet the preset requirement.

[0052] Optionally, evaluate the model, including: sequence generator;

[0053] After the evaluation model obtains the power generation data of the target renewable energy, the power generation data is preprocessed based on the sequence generator to generate a power generation output sequence of the target renewable energy.

[0054] Optionally, a sequence generator is generated by learning from a process of preprocessing power generation data.

[0055] Optionally, the power generation output sequence is calculated to evaluate the target renewable energy available output in different dimensions, including:

[0056] Calculating the power generation output sequence to obtain an output value of the evaluation model;

[0057] Correcting the output value to obtain target new energy available output in different dimensions;

[0058] The target new energy available output in different dimensions includes:

[0059] The available output of target renewable energy sources at different stations / bases / regions, the available output of target renewable energy sources at different months / seasons / years, and the available output of target renewable energy sources at different temperatures / humidities / altitudes.

[0060] In yet another aspect, the present invention further provides a computing device, comprising: one or more processors;

[0061] a processor for executing one or more programs;

[0062] When the one or more programs are executed by the one or more processors, the above-described method is implemented.

[0063] In yet another aspect, the present invention further provides a computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed, the method described above is implemented.

[0064] Compared with the prior art, the present invention has the following beneficial effects:

[0065] The present invention provides an evaluation method for the available capacity of new energy, including: obtaining the historical power generation output sequence of new energy, and calculating the historical new energy available capacity of different dimensions of new energy based on the historical power generation output sequence; using the historical power generation output sequence as the input data of the training model, using the historical new energy available capacity of different dimensions as the output data corresponding to the input and output data, and training an evaluation model based on the input data and the output data; based on the evaluation model, obtaining the power generation output sequence of the target new energy, and calculating the power generation output sequence to evaluate the target new energy available output of different dimensions. The present invention can perform multi-dimensional available capacity evaluation for new energy, provide support for the development planning and dispatching operation of regional new energy, power grids, and regulation resources, etc., to ensure the stable operation of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 A flow chart of a method for evaluating the available capacity of new energy sources according to the present invention;

[0067] Figure 2 The present invention is a structural diagram of a system for evaluating the available capacity of new energy. DETAILED DESCRIPTION

[0068] Now, exemplary embodiments of the present invention are described with reference to the accompanying drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. These embodiments are provided to disclose the present invention in detail and completely and to fully convey the scope of the present invention to those skilled in the art. The terms used in the exemplary embodiments shown in the accompanying drawings are not intended to limit the present invention. In the accompanying drawings, the same units / elements are marked with the same reference numerals.

[0069] Unless otherwise specified, the terms (including technical terms) used herein have the commonly understood meanings to those skilled in the art. In addition, it is understood that the terms defined in commonly used dictionaries should be understood to have the same meanings as those in the context of the relevant fields, and should not be understood as idealized or overly formal meanings.

[0070] Embodiment 1:

[0071] The present invention proposes a method for evaluating the available capacity of new energy sources, such as Figure 1 As shown, including:

[0072] Step 1: Obtain a historical power generation output sequence of new energy, and based on the historical power generation output sequence, calculate the historical new energy available capacity of different dimensions of new energy;

[0073] Step 2: using the historical power generation output sequence as input data for a training model, using the historical renewable energy available capacity of different dimensions as output data corresponding to the input and output data, and training an evaluation model based on the input data and the output data;

[0074] Step 3: Based on the evaluation model, obtain the power generation output sequence of the target new energy, and calculate the power generation output sequence to evaluate the available output of the target new energy in different dimensions.

[0075] Among them, obtaining the historical power generation output sequence of new energy sources includes:

[0076] The power generation data related to power generation of the new energy in the historical preset time period is obtained, the power generation data is preprocessed, and a historical power generation output sequence is generated based on the preprocessed power generation data.

[0077] Among them, power generation data includes:

[0078] Actual power generation data, available power generation data, abandoned power data, installed capacity data and operating capacity data of new energy.

[0079] The preprocessing of the power generation data includes:

[0080] Traverse all power generation data, remove impure data and duplicate data in all power generation data, and restore the missing data.

[0081] Among them, different dimensions include: different regional dimensions, different time dimensions and different environmental dimensions.

[0082] Before calculating the available capacity of historical renewable energy in different dimensions, the historical power generation output sequence is classified according to different dimensions to obtain the historical power generation output sequence in different dimensions;

[0083] Performing normalization processing on the historical power generation processing sequence;

[0084] According to different dimensions, based on the historical power generation output sequence of the corresponding dimension after scalar processing, the historical new energy available capacity of different dimensions of new energy is calculated.

[0085] Wherein, based on the input data and the output data, training an evaluation model comprises:

[0086] The input data and the corresponding output data are divided according to a preset ratio to obtain a training set and a validation set, the training set is input into a pre-established network model, and the network model is used to learn the calculation process of obtaining the output data by calculating the input data in the training set, so as to adjust the model parameters of the network model and obtain an initial evaluation model;

[0087] The initial evaluation model is verified with the verification set. If the verification result meets the preset requirements, the initial evaluation model is used as the evaluation model. If the verification result does not meet the preset requirements, the parameters of the initial evaluation model are adjusted until the verification result meets the preset requirements.

[0088] The initial evaluation model is verified using the verification set, including:

[0089] The input data in the validation set is input into the initial evaluation model for calculation to obtain validation output data, and the validation output data is compared with the output data in the validation set to obtain a validation result.

[0090] If the verification output data is compared with the output data in the verification set and the similarity is greater than 95%, it is determined that the verification result meets the preset requirement; otherwise, it does not meet the preset requirement.

[0091] Among them, the evaluation model includes: a sequence generator;

[0092] After the evaluation model obtains the power generation data of the target renewable energy, the power generation data is preprocessed based on the sequence generator to generate a power generation output sequence of the target renewable energy.

[0093] Among them, the sequence generator is generated by learning the processing process of preprocessing the power generation data.

[0094] The power generation output sequence is calculated to evaluate the target renewable energy available output in different dimensions, including:

[0095] Calculating the power generation output sequence to obtain an output value of the evaluation model;

[0096] Correcting the output value to obtain target new energy available output in different dimensions;

[0097] The target new energy available output in different dimensions includes:

[0098] The available output of target renewable energy sources at different stations / bases / regions, the available output of target renewable energy sources at different months / seasons / years, and the available output of target renewable energy sources at different temperatures / humidities / altitudes.

[0099] Example 2

[0100] The present invention proposes a method for evaluating the available capacity of new energy, comprising:

[0101] Step 1: Obtain a historical power generation output sequence of new energy, and based on the historical power generation output sequence, calculate the historical new energy available capacity of different dimensions of new energy;

[0102] Obtain the historical power generation output sequence of renewable energy, including:

[0103] The power generation data related to power generation of the new energy in the historical preset time period is obtained, the power generation data is preprocessed, and a historical power generation output sequence is generated based on the preprocessed power generation data.

[0104] Power generation data, including:

[0105] Actual power generation data of new energy, available power generation data, abandoned power data, installed capacity data and start-up capacity data, etc.

[0106] Preprocessing the power generation data includes:

[0107] Traverse all power generation data, remove impure data and duplicate data in all power generation data, and restore the missing data.

[0108] Different dimensions include: different regional dimensions, different time dimensions and different environmental dimensions.

[0109] Before calculating the available capacity of historical renewable energy in different dimensions, the historical power generation output sequence is classified according to different dimensions to obtain the historical power generation output sequence in different dimensions;

[0110] Performing normalization processing on the historical power generation processing sequence;

[0111] According to different dimensions, based on the historical power generation output sequence of the corresponding dimension after scalar processing, the historical new energy available capacity of different dimensions of new energy is calculated.

[0112] Step 2: using the historical power generation output sequence as input data for a training model, using the historical renewable energy available capacity of different dimensions as output data corresponding to the input and output data, and training an evaluation model based on the input data and the output data;

[0113] Based on the input data and the output data, an evaluation model is trained, including:

[0114] The input data and the corresponding output data are divided according to a preset ratio to obtain a training set and a validation set, the training set is input into a pre-established network model, and the network model is used to learn the calculation process of obtaining the output data by calculating the input data in the training set, so as to adjust the model parameters of the network model and obtain an initial evaluation model;

[0115] The initial evaluation model is verified with the verification set. If the verification result meets the preset requirements, the initial evaluation model is used as the evaluation model. If the verification result does not meet the preset requirements, the parameters of the initial evaluation model are adjusted until the verification result meets the preset requirements.

[0116] The initial evaluation model is verified using the verification set, including:

[0117] The input data in the validation set is input into the initial evaluation model for calculation to obtain validation output data, and the validation output data is compared with the output data in the validation set to obtain a validation result.

[0118] If the verification output data is compared with the output data in the verification set and the similarity is greater than 95%, it is determined that the verification result meets the preset requirement; otherwise, it does not meet the preset requirement.

[0119] Evaluation models, including: sequence generator;

[0120] After the evaluation model obtains the power generation data of the target renewable energy, the power generation data is preprocessed based on the sequence generator to generate a power generation output sequence of the target renewable energy.

[0121] The sequence generator is generated by learning the process of preprocessing the power generation data.

[0122] Step 3: Based on the evaluation model, obtain the power generation output sequence of the target new energy, and calculate the power generation output sequence to evaluate the available output of the target new energy in different dimensions.

[0123] The power generation output sequence is calculated to evaluate the target renewable energy available output in different dimensions, including:

[0124] Calculating the power generation output sequence to obtain an output value of the evaluation model;

[0125] Correcting the output value to obtain target new energy available output in different dimensions;

[0126] The target new energy available output in different dimensions includes:

[0127] The available output of target renewable energy sources at different stations / bases / regions, the available output of target renewable energy sources at different months / seasons / years, and the available output of target renewable energy sources at different temperatures / humidities / altitudes.

[0128] Embodiment 3:

[0129] The present invention also proposes an evaluation system 200 for the available capacity of new energy sources, such as Figure 2As shown, including:

[0130] The data acquisition unit 201 is used to obtain the historical power generation output sequence of the new energy, and calculate the historical new energy available capacity of different dimensions of the new energy based on the historical power generation output sequence;

[0131] A training model unit 202 is used to use the historical power generation output sequence as input data of a training model, use the historical renewable energy available capacity of different dimensions as output data corresponding to the input and output data, and train an evaluation model based on the input data and the output data;

[0132] The output unit 203 is used to obtain the power generation output sequence of the target new energy based on the evaluation model, and calculate the power generation output sequence to evaluate the available output of the target new energy in different dimensions.

[0133] Among them, obtaining the historical power generation output sequence of new energy sources includes:

[0134] The power generation data related to power generation of the new energy in the historical preset time period is obtained, the power generation data is preprocessed, and a historical power generation output sequence is generated based on the preprocessed power generation data.

[0135] Among them, power generation data includes:

[0136] Actual power generation data, available power generation data, abandoned power data, installed capacity data and operating capacity data of new energy.

[0137] The preprocessing of the power generation data includes:

[0138] Traverse all power generation data, remove impure data and duplicate data in all power generation data, and restore the missing data.

[0139] Among them, different dimensions include: different regional dimensions, different time dimensions and different environmental dimensions.

[0140] Before calculating the available capacity of historical renewable energy in different dimensions, the historical power generation output sequence is classified according to different dimensions to obtain the historical power generation output sequence in different dimensions;

[0141] Performing normalization processing on the historical power generation processing sequence;

[0142] According to different dimensions, based on the historical power generation output sequence of the corresponding dimension after scalar processing, the historical new energy available capacity of different dimensions of new energy is calculated.

[0143] Wherein, based on the input data and the output data, training an evaluation model comprises:

[0144] The input data and the corresponding output data are divided according to a preset ratio to obtain a training set and a validation set, the training set is input into a pre-established network model, and the network model is used to learn the calculation process of obtaining the output data by calculating the input data in the training set, so as to adjust the model parameters of the network model and obtain an initial evaluation model;

[0145] The initial evaluation model is verified with the verification set. If the verification result meets the preset requirements, the initial evaluation model is used as the evaluation model. If the verification result does not meet the preset requirements, the parameters of the initial evaluation model are adjusted until the verification result meets the preset requirements.

[0146] The initial evaluation model is verified using the verification set, including:

[0147] The input data in the validation set is input into the initial evaluation model for calculation to obtain validation output data, and the validation output data is compared with the output data in the validation set to obtain a validation result.

[0148] If the verification output data is compared with the output data in the verification set and the similarity is greater than 95%, it is determined that the verification result meets the preset requirement; otherwise, it does not meet the preset requirement.

[0149] Among them, the evaluation model includes: a sequence generator;

[0150] After the evaluation model obtains the power generation data of the target renewable energy, the power generation data is preprocessed based on the sequence generator to generate a power generation output sequence of the target renewable energy.

[0151] Among them, the sequence generator is generated by learning the processing process of preprocessing the power generation data.

[0152] The power generation output sequence is calculated to evaluate the target new energy available output in different dimensions, including:

[0153] Calculating the power generation output sequence to obtain an output value of the evaluation model;

[0154] Correcting the output value to obtain target new energy available output in different dimensions;

[0155] The target new energy available output in different dimensions includes:

[0156] The available output of target renewable energy sources at different stations / bases / regions, the available output of target renewable energy sources at different months / seasons / years, and the available output of target renewable energy sources at different temperatures / humidities / altitudes.

[0157] The present invention can conduct multi-dimensional available capacity evaluation for new energy, provide support for the development planning and dispatching operation of regional new energy, power grid and regulation resources, so as to ensure the stable operation of the power grid.

[0158] Embodiment 4:

[0159] Based on the same inventive concept, the present invention also provides a computer device, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding functions, so as to implement the steps of the method in the above embodiment.

[0160] Embodiment 5:

[0161] Based on the same inventive concept, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both a built-in storage medium in a computer device and an extended storage medium supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the steps of the method in the above embodiment.

[0162] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The schemes in the embodiments of the present invention may be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0163] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0164] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0165] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0166] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0167] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for evaluating the available capacity of new energy, characterized in that: include: Obtaining a historical power generation output sequence of new energy, and based on the historical power generation output sequence, calculating the historical new energy available capacity of different dimensions of new energy; Using the historical power generation output sequence as input data for a training model, using the historical renewable energy available capacity in different dimensions as output data corresponding to the input and output data, and training an evaluation model based on the input data and the output data; Based on the evaluation model, the power generation output sequence of the target new energy is obtained, and the power generation output sequence is calculated to evaluate the available output of the target new energy in different dimensions.

2. The evaluation method according to claim 1, characterized in that: The obtaining of the historical power generation output sequence of new energy sources includes: The power generation data related to power generation of the new energy in the historical preset time period is obtained, the power generation data is preprocessed, and a historical power generation output sequence is generated based on the preprocessed power generation data.

3. The evaluation method according to claim 2, characterized in that: The power generation data includes: Actual power generation data, available power generation data, abandoned power data, installed capacity data and operating capacity data of new energy.

4. The evaluation method according to claim 2, characterized in that: The preprocessing of the power generation data comprises: Traverse all power generation data, remove impure data and duplicate data in all power generation data, and restore the missing data.

5. The evaluation method according to claim 1, characterized in that: The different dimensions include: different regional dimensions, different time dimensions and different environmental dimensions.

6. The evaluation method according to claim 1, characterized in that: Before calculating the historical renewable energy available capacity in different dimensions, the historical power generation output sequence is classified according to different dimensions to obtain the historical power generation output sequence in different dimensions; Performing normalization processing on the historical power generation processing sequence; According to different dimensions, based on the historical power generation output sequence of the corresponding dimension after scalar processing, the historical new energy available capacity of different dimensions of new energy is calculated.

7. The evaluation method according to claim 1, characterized in that: The step of training an evaluation model based on the input data and the output data comprises: The input data and the corresponding output data are divided according to a preset ratio to obtain a training set and a validation set, the training set is input into a pre-established network model, and the network model is used to learn the calculation process of obtaining the output data by calculating the input data in the training set, so as to adjust the model parameters of the network model and obtain an initial evaluation model; The initial evaluation model is verified with the verification set. If the verification result meets the preset requirements, the initial evaluation model is used as the evaluation model. If the verification result does not meet the preset requirements, the parameters of the initial evaluation model are adjusted until the verification result meets the preset requirements.

8. A system for evaluating the available capacity of new energy, characterized in that: include: A data acquisition unit, used to obtain a historical power generation output sequence of new energy, and based on the historical power generation output sequence, calculate the historical new energy available capacity of different dimensions of new energy; A training model unit, configured to use the historical power generation output sequence as input data for a training model, use the historical renewable energy available capacity of different dimensions as output data corresponding to the input and output data, and train an evaluation model based on the input data and the output data; The output unit is used to obtain the power generation output sequence of the target new energy based on the evaluation model, and calculate the power generation output sequence to evaluate the available output of the target new energy in different dimensions.

9. A computer device, characterized in that: include: one or more processors; a processor for executing one or more programs; When the one or more programs are executed by the one or more processors, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed, the method according to any one of claims 1 to 7 is implemented.

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