Green electricity storage scheduling method based on big data

Abstract

The present invention provides a big data-based green electricity storage scheduling method. The method comprises: using historical electricity consumption data and real-time natural language data from the electricity user side, predicting the historical electricity consumption data and real-time natural language data using an electricity consumption analysis model to obtain a power consumption prediction result and a power consumption interference factor; obtaining characteristic data of power plants connected to the power generation side, predicting the characteristic data of the power plants using a power supply analysis model to obtain a power generation prediction result; and determining a green electricity storage scheduling strategy based on the power consumption prediction result, the power consumption interference factor, the power generation prediction result, and the current green electricity storage capacity. The present invention has the beneficial effect of improving the efficiency of green electricity storage scheduling.

Description

Technical Field

[0001] The present invention relates to the technical field of electric green energy dispatching, and in particular to a green electricity energy storage dispatching method based on big data. Background Art

[0002] Due to the instability of green electricity (i.e., renewable energy), namely the time period of its generation and its significant impact on climate, and the fact that electricity demand on the electricity consumption side is subject to some sporadic factors, the scheduling of green electricity storage may be inaccurate, leading to power shortages or energy waste. Existing technologies currently mainly use cost and loss as scheduling targets, and do not consider the failure of scheduling strategies caused by the instability of green electricity and sporadic factors. Summary of the Invention

[0003] The main purpose of the embodiments of the present invention is to propose a green electricity storage scheduling method based on big data, which achieves scheduling efficiency and energy-saving management of green electricity storage.

[0004] One aspect of the present invention provides a green electricity storage scheduling method based on big data, characterized by comprising:

[0005] Based on the historical electricity consumption data and real-time natural language data of the electricity user side, the electricity consumption analysis model is used to predict the historical electricity consumption data and the real-time natural language data to obtain the electricity consumption prediction result and the electricity consumption interference factor;

[0006] Obtain characteristic data of the power plant connected to the power generation side, use the power supply analysis model to predict the characteristic data of the power plant, and obtain the power generation prediction result;

[0007] Determining a green electricity storage scheduling strategy based on the electricity forecast result, the electricity interference factor, the power generation forecast result, and the current green electricity storage amount;

[0008] The training process of the power consumption analysis model includes:

[0009] Acquiring time series data of the historical electricity data and acquiring historical natural language data, wherein the natural series data is used to represent electricity usage data affected by time, and the historical natural language data is used to represent text data that has an occasional impact on electricity usage;

[0010] Processing the time series data using a GRU recurrent neural network to obtain a first fusion feature, wherein the first fusion feature is used to characterize a dependency relationship between the historical electricity data and the time series data;

[0011] Processing the time series data and the historical natural language data using an NLP language model to obtain a second fusion feature, where the second fusion feature is used to characterize a dependency relationship between the time series data and the historical natural language data;

[0012] Connecting the first fusion feature and the second fusion feature to obtain a comprehensive feature, determining the power consumption analysis model based on the comprehensive feature, and determining the power consumption disturbance factor based on the comprehensive feature;

[0013] The training process of the power analysis model includes:

[0014] Get the dataset;

[0015] Perform feature engineering on the power plant dataset to identify the main features that affect power generation;

[0016] Based on the main features and the feature data, XGBoost regression training is performed on each type of power plant to obtain a corresponding basic machine learning model;

[0017] The basic machine learning model is subjected to model fusion processing to obtain the power supply analysis model.

[0018] According to the green electricity storage scheduling method based on big data, the training process of the electricity consumption analysis model includes:

[0019] Acquire time series data and historical natural language data. Time series data includes electricity consumption data, weather data, holidays, and event data on the electricity user side. Historical natural language data includes social media data, news report data, and user feedback.

[0020] Preprocess time series data and historical natural language data. The preprocessing of time series data includes missing value processing, normalization and feature engineering. Feature engineering is also used to fuse features with correlation relationships. The preprocessing of historical natural language data includes text cleaning, word segmentation and word vectorization.

[0021] According to the green electricity storage scheduling method based on big data, the time series data is processed using a GRU recurrent neural network to obtain a first fusion feature, including:

[0022] The GRU recurrent neural network includes an input layer, a GRU layer and a fully connected layer;

[0023] The time series data is input as a three-dimensional tensor through the input layer, the long-term dependency of the time series is captured in the three-dimensional tensor through the GRU layer, and the long-term dependency is feature fused through the fully connected layer to obtain the first fused feature, which is the time series feature.

[0024] According to the green electricity storage scheduling method based on big data, the time series data and the historical natural language data are processed using an NLP language model to obtain a second fusion feature, including:

[0025] The NLP model includes an embedding layer, a GRU layer, and a fully connected layer;

[0026] Obtain word vectors for the historical natural language data, and process the word vectors through an embedding layer to obtain semantic and contextual information related to electricity usage;

[0027] Extracting semantic and contextual information through the GRU layer of the NLP model to obtain text features;

[0028] The time series features and the text features are fused through the fully connected layer of the NLP model to obtain a second fused feature.

[0029] According to the green electricity storage scheduling method based on big data, the first fusion feature and the second fusion feature are connected to obtain a comprehensive feature, the power consumption analysis model is determined according to the comprehensive feature, and the power consumption disturbance factor is determined according to the comprehensive feature, including:

[0030] Connecting the first fusion feature and the second fusion feature to obtain a comprehensive feature, and obtaining a power consumption prediction result through the output layer;

[0031] The power consumption analysis model uses mean square error as a loss function and is trained using Adam or RMSprop optimizer, and the training process is optimized using cross validation;

[0032] The trained power consumption analysis model is analyzed using one of SHAP value and LIME to obtain the power consumption disturbance factor.

[0033] According to the green electricity storage scheduling method based on big data, obtaining a data set also includes:

[0034] The data was obtained through third-party platforms and data synthesis, where the data set includes historical power generation, meteorological data, and characteristic data of distributed green power generation equipment;

[0035] In addition, the acquired data set is subjected to data cleaning and data conversion processing; wherein data cleaning includes missing value processing and outlier detection, wherein missing value processing is performed by filling and deleting missing values, wherein data conversion includes identifying abnormal data using the Z-score statistical method; wherein data conversion includes time feature processing and category coding processing, wherein time feature processing converts time features into numerical types, wherein category coding processing includes converting power plant types into numerical types.

[0036] According to the green electricity storage scheduling method based on big data, the method further includes:

[0037] The data obtained from the third-party platform is padded and enhanced using data synthesis to obtain the data set, wherein the data enhancement is obtained by using random data generated by statistical distribution.

[0038] According to the green electricity storage scheduling method based on big data, obtaining a data set also includes:

[0039] The acquired data set is subjected to data cleaning and data conversion processing; data cleaning includes missing value processing and outlier detection, wherein missing value processing is performed by filling and deleting missing values, wherein data conversion includes identifying abnormal data using the Z-score statistical method; wherein data conversion includes time feature processing and category coding processing, wherein time feature processing converts time features into numerical types, wherein category coding processing includes converting power plant types into numerical types.

[0040] According to the green electricity storage scheduling method based on big data, XGBoost regression training is used for each type of power plant based on the main features and the feature data to obtain a corresponding basic machine learning model, including:

[0041] According to the type of the power plant, a model that meets the main characteristics and the characteristic data is selected from basic machine learning for training;

[0042] The trained basic machine learning model is used for prediction, evaluation and hyperparameter tuning, where the hyperparameter tuning includes grid search and random search, to complete the training of the basic machine learning model for each type of power plant, where the basic machine model is selected according to the power plant type and power generation characteristics of the power plant.

[0043] According to the green electricity storage scheduling method based on big data, the basic machine learning model is subjected to model fusion processing to obtain the distributed green power analysis model, including:

[0044] Decision tree regression and support vector regression are used as meta-learners, and multiple trained basic machine learning models are used as features to fit the meta-learners to obtain the distributed green power analysis model.

[0045] According to the green electricity storage scheduling method based on big data, a green electricity storage scheduling strategy is determined based on the electricity prediction result, the electricity interference factor, the power generation prediction result and the current green electricity storage amount, including:

[0046] Determining the electricity demand within a preset time period based on the electricity forecast result and the electricity interference factor, and determining the total power generation of the connected power plant within the preset time period based on the power generation forecast result;

[0047] According to the electricity demand, the total power generation and the current green electricity storage, a corresponding green electricity storage scheduling strategy is selected, wherein the green electricity storage scheduling strategy includes at least one of green electricity priority, energy storage system scheduling, dynamic pricing and load management.

[0048] The beneficial effects of the present invention are: through the GRU recurrent neural network and NLP model, the electricity consumption data on the electricity consumption side is predicted based on the time series, while taking into account the objective influencing factors (such as weather, time period, etc.) and accidental factors (human or accidental event influences), so that the electricity consumption on the electricity consumption side is accurately predicted; through integrated learning, different types of renewable energy power plants are used to predict the accurate power generation of different connected power plants; in green energy storage, efficient green power scheduling is carried out according to the real-time predicted power generation and demand power consumption according to the pre-determined scheduling priority. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments with reference to the accompanying drawings, in which:

[0050] Figure 1 It is a schematic diagram of the green electricity storage scheduling process of an embodiment of the present invention.

[0051] Figure 2 4 is a schematic diagram of the training process of the electricity consumption analysis model according to an embodiment of the present invention.

[0052] Figure 3 4 is a schematic diagram of the preprocessing process of the training data of the electrical analysis model according to an embodiment of the present invention.

[0053] Figure 4 1 is a schematic diagram of the time series feature extraction process according to an embodiment of the present invention.

[0054] Figure 5 It is a schematic diagram of a process for obtaining dependency relationships between time series data and historical natural language data according to an embodiment of the present invention.

[0055] Figure 6 4 is a schematic diagram of the training process of the power supply analysis model according to an embodiment of the present invention.

[0056] Figure 7 4 is a schematic diagram of the integrated learning process of an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. In the subsequent description, suffixes such as "module," "component," or "unit" used to represent elements are used solely to facilitate the description of the present invention and have no specific meaning in themselves. Therefore, "module," "component," or "unit" may be used interchangeably. "First," "second," and the like are used solely to distinguish technical features and should not be construed as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features. In this subsequent description, the consecutive numbering of method steps is for ease of review and understanding. In conjunction with the overall technical solution of the present invention and the logical relationship between the various steps, adjusting the order of implementation of the steps does not affect the technical effects achieved by the technical solution of the present invention. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present invention and should not be construed as limiting the present invention.

[0058] refer to Figure 1 ,in Figure 1 Schematic diagram of the green energy storage scheduling process in an embodiment of the present invention. It includes but is not limited to steps S100 to S300:

[0059] S100 , based on historical electricity consumption data and real-time natural language data on the electricity consumption side, use an electricity consumption analysis model to predict the historical electricity consumption data and the real-time natural language data to obtain electricity consumption prediction results and electricity consumption interference factors.

[0060] In some embodiments, the electricity consumption interference factors include weather, electricity consumption holidays and events, and some occasional times, such as electricity price adjustments, policy changes, economic activities, etc.

[0061] S200 , acquiring characteristic data of a power plant connected to the power generation side, and using a power supply analysis model to predict the characteristic data of the power plant to obtain a power generation prediction result.

[0062] In some embodiments, the characteristic data of the power plant includes power plant type, historical power generation, meteorological data, equipment status data (such as the opening and failure of the generator set) and time characteristics (such as date, hour, season).

[0063] S300: Determine a green electricity storage scheduling strategy based on electricity forecast results, electricity interference factors, power generation forecast results, and current green electricity storage capacity.

[0064] In some embodiments, the electricity demand within a preset time period is determined based on the electricity forecast results and the electricity interference factor, and the total power generation of the connected power plant within the preset time period is determined based on the power generation forecast results; based on the electricity demand, total power generation and current green electricity storage, a corresponding green electricity storage scheduling strategy is selected, wherein the green electricity storage scheduling strategy includes at least one of green electricity priority, energy storage system scheduling, dynamic pricing and load management.

[0065] In some embodiments, reference Figure 2 The training process diagram of the power consumption analysis model shown includes but is not limited to steps S400 to S700:

[0066] S400, obtaining time series data of historical electricity data and historical natural language data, where the natural series data is used to represent electricity consumption data affected by time, and the historical natural language data is used to represent text data that has occasional effects on electricity consumption.

[0067] In some embodiments, reference Figure 3 The schematic diagram of the preprocessing process of the training data of the electroanalysis model shown includes but is not limited to steps S410 to S430:

[0068] S410, acquiring time series data and historical natural language data, wherein the time series data includes electricity consumption data, weather data, holiday and event data on the electricity user side, and the historical natural language data includes social media data, news report data, and user feedback;

[0069] S420, preprocessing the time series data and historical natural language data, wherein the preprocessing of the time series data includes missing value processing, normalization and feature engineering, wherein feature engineering is also used to fuse features with correlation relationships; wherein the preprocessing of the historical natural language data includes text cleaning, word segmentation processing and word vectorization processing.

[0070] In some embodiments, electricity consumption data includes collecting historical electricity consumption data, which is usually recorded by hour or minute; weather data includes temperature, humidity, precipitation, and wind speed, which are obtained by calling the meteorological API interface on the electricity consumption side (target area); among them, holiday and event data include public holidays and specific activities that affect electricity demand.

[0071] In some embodiments, natural language data includes: social media data, which can be captured through crawler technology to collect social media posts, comments, etc. related to electricity consumption; news reports, which collect news articles about electricity demand, policy changes, economic activities, etc.; user feedback, which includes user evaluation and feedback information on electricity services.

[0072] In some embodiments, time series data preprocessing includes: missing value processing, using interpolation or filling methods to process missing data; normalization, normalizing the data to the range of [0, 1], this method is used to improve the model convergence speed; feature engineering, extracting features (such as time series features, lag features, etc.), and merging weather data with electricity consumption data.

[0073] In some embodiments, natural language data preprocessing includes: text cleaning, removing special characters, punctuation marks, and stop words; word segmentation, dividing the text into words; and word vectorization, converting the text into a vector representation using a pre-trained word vector (such as Word2Vec, GloVe) or a model such as BERT.

[0074] S500: Process the time series data using a GRU recurrent neural network to obtain a first fusion feature, where the first fusion feature is used to characterize the dependency relationship between the historical electricity data and the time series data.

[0075] In some embodiments, reference Figure 4 The schematic diagram of the time series feature extraction process shown in FIG. 5 includes but is not limited to steps S510 to S520:

[0076] S510, constructing a GRU recurrent neural network, wherein the GRU recurrent neural network includes an input layer, a GRU layer, and a fully connected layer;

[0077] S520, input the time series data as a three-dimensional tensor through the input layer, capture the long-term dependency of the time series on the three-dimensional tensor through the GRU layer, and perform feature fusion on the long-term dependency through the fully connected layer to obtain a first fused feature, which is a time series feature.

[0078] S600: Using an NLP language model to process the time series data and the historical natural language data to obtain a second fusion feature, where the second fusion feature is used to characterize the dependency relationship between the time series data and the historical natural language data.

[0079] In some embodiments, reference Figure 5 The flowchart of the dependency relationship between time series data and historical natural language data shown in FIG. 1 includes but is not limited to steps S610 to S630:

[0080] S610, constructing an NLP model, wherein the NLP model includes an embedding layer, a GRU layer, and a fully connected layer;

[0081] S620, obtaining word vectors for historical natural language data, and processing the word vectors through an embedding layer to obtain semantic and contextual information related to electricity usage;

[0082] S630, extracting semantic and contextual information through the GRU layer of the NLP model to obtain text features;

[0083] S640: Fusing the time series features and the text features through a fully connected layer of the NLP model to obtain a second fused feature.

[0084] S700 , connecting the first fusion feature and the second fusion feature to obtain a comprehensive feature, determining a power consumption analysis model based on the comprehensive feature, and determining a power consumption disturbance factor based on the comprehensive feature.

[0085] In some embodiments, a comprehensive feature is obtained by connecting the first fusion feature and the second fusion feature, and a power consumption prediction result is obtained through the output layer; the power consumption analysis model uses mean square error as the loss function, and is trained using Adam or RMSprop optimizer, and the training process is optimized using cross-validation; the trained power consumption analysis model is analyzed using one of SHAP value and LIME to obtain a power consumption disturbance factor.

[0086] In some embodiments, reference Figure 6 The training process diagram of the power analysis model shown includes but is not limited to steps S800 to S1000:

[0087] S800: Acquire a data set, where the data set includes the power plant type, historical power generation, meteorological data, and time characteristics of power generation equipment status data of the power plant, and perform feature engineering processing on the power plant data set to obtain main features that affect power generation.

[0088] In some embodiments, the method further includes data cleaning and data conversion processing on the acquired data set; wherein data cleaning includes missing value processing and outlier detection, wherein missing value processing is performed by filling and deleting missing values, wherein data conversion includes identifying abnormal data using the Z-score statistical method; wherein data conversion includes time feature processing and category coding processing, wherein time feature processing converts time features into numerical types, wherein category coding processing includes converting power plant types into numerical types.

[0089] In some embodiments, data obtained from a third-party platform is padded and augmented using data synthesis to obtain a dataset, wherein the data augmentation is obtained using random data generated from a statistical distribution. The third-party platform is a power company, meteorological service provider, or agency that provides power plants, and the random data is generated based on a statistical distribution (such as a normal distribution, a uniform distribution, etc.). For example, new power generation data can be generated based on the distribution of historical power generation. It is understood that data synthesis is only used for model training.

[0090] In some embodiments, reference Figure 7 The schematic diagram of the integrated learning process shown includes but is not limited to steps S810 to S820:

[0091] S810, selecting a model that meets the main features and feature data from basic machine learning according to the power plant type for training.

[0092] S820, performing prediction, evaluation, and hyperparameter tuning on the trained basic machine learning model, wherein the hyperparameter tuning includes grid search and random search, to complete the training of the basic machine learning model for each type of power plant, wherein the basic machine model is selected according to the power plant type and power generation characteristics of the power plant.

[0093] In some embodiments, the power generation characteristics are the characteristics of different green energy sources, including power generation time, seasonal changes, wind speed changes, water volume, etc.

[0094] In some embodiments, wind power generation is integrated with multiple decision trees through random forests, which is suitable for processing complex nonlinear relationships, and uses support vector machines to classify or regress by constructing hyperplanes, which is suitable for small samples and high-dimensional data, and through long short-term memory networks (LSTMs) suitable for processing time series data, which can capture the temporal characteristics of wind speed changes.

[0095] In some embodiments, tidal power generation uses the K-nearest neighbor algorithm (KNN), that is, prediction is made by finding the nearest neighbor samples, which is suitable for small data sets. Tidal power generation can use a decision tree, considering its periodicity, to perform classification and regression problems, and finally ensemble learning uses AdaBoost.

[0096] Through the above embodiments, the characteristics of different green energy sources can be fully utilized to improve the accuracy of power generation prediction.

[0097] In some embodiments, it is also possible to use the distribution weights of different green energy sources. For example, a mountainous city is mainly powered by wind power, supplemented by hydropower. Therefore, weights can be assigned to different sub-learners, that is, the sub-learner for wind power supply has a higher weight, while the sub-learner for hydropower generation has a lower weight.

[0098] S900, based on the main features and characteristic data, uses XGBoost regression training for each type of power plant to obtain the corresponding basic machine learning model.

[0099] In some embodiments, decision tree regression and support vector regression are used as meta-learners, and multiple trained basic machine learning models are used as features to fit the meta-learners to obtain a distributed green power analysis model.

[0100] S1000 performs model fusion processing on the basic machine learning model to obtain a power supply analysis model.

[0101] The beneficial effects of the present invention are: through the GRU recurrent neural network and NLP model, the electricity consumption data on the electricity consumption side is predicted based on the time series, while taking into account the objective influencing factors (such as weather, time period, etc.) and accidental factors (human or accidental event influences), so that the electricity consumption on the electricity consumption side is accurately predicted; through integrated learning, different types of renewable energy power plants are used to predict the accurate power generation of different connected power plants; in green energy storage, efficient green power scheduling is carried out according to the real-time predicted power generation and demand power consumption according to the pre-determined scheduling priority.

[0102] An embodiment of the present invention further provides an electronic device, the electronic device including a processor and a memory;

[0103] The memory stores a program;

[0104] The processor executes a program to perform the aforementioned green electricity storage scheduling method based on big data; the electronic device has the function of carrying and running the software system for green electricity storage scheduling based on big data provided by an embodiment of the present invention, for example, a personal computer, a minicomputer, a main frame, a workstation, a network or distributed computing environment, a separate or integrated computer platform, or communicates with a charged particle tool or other imaging device, etc.

[0105] An embodiment of the present invention further provides a computer-readable storage medium, wherein the storage medium stores a program, and the program is executed by a processor to implement the green electricity storage scheduling method based on big data as described above.

[0106] In some optional embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided in an exemplary manner for the purpose of providing a more comprehensive understanding of the technology. The disclosed method is not limited to the operation and logic flow presented herein. Optional embodiments are contemplated in which the order of the various operations is changed and the sub-operations described as a part of a larger operation are performed independently.

[0107] An embodiment of the present invention further discloses a computer program product or computer program, which includes computer instructions stored in a computer-readable storage medium. A processor of a computer device can read the computer instructions from the computer-readable storage medium and execute the computer instructions, causing the computer device to implement the aforementioned big data-based green electricity storage scheduling method.

[0108] Furthermore, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise indicated, one or more of the functions and / or features described may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It will also be understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present invention. More specifically, given the properties, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of the module will be understood within the ordinary skill of an engineer. Therefore, a person skilled in the art using ordinary skill will be able to implement the present invention set forth in the claims without undue experimentation. It will also be understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0109] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, 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. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0110] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0111] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0112] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0113] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0114] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

[0115] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of this application.

Claims

1. A green electricity storage scheduling method based on big data, characterized in that: include: Based on the historical electricity consumption data and real-time natural language data of the electricity user side, the electricity consumption analysis model is used to predict the historical electricity consumption data and the real-time natural language data to obtain the electricity consumption prediction result and the electricity consumption interference factor; Obtain characteristic data of the power plant connected to the power generation side, use the power supply analysis model to predict the characteristic data of the power plant, and obtain the power generation prediction result; Determining a green electricity storage scheduling strategy based on the electricity consumption forecast result, the electricity consumption interference factor, the power generation forecast result, and the current green electricity storage amount; The training process of the power consumption analysis model includes: Acquiring time series data of the historical electricity data and acquiring historical natural language data, wherein the time series data is used to represent electricity usage data affected by time, and the historical natural language data is used to represent text data that has an occasional impact on electricity usage; Processing the time series data using a GRU recurrent neural network to obtain a first fusion feature, wherein the first fusion feature is used to characterize a dependency relationship between the historical electricity data and the time series data; Processing the time series data and the historical natural language data using an NLP language model to obtain a second fusion feature, where the second fusion feature is used to characterize a dependency relationship between the time series data and the historical natural language data; Connecting the first fusion feature and the second fusion feature to obtain a comprehensive feature, determining the power consumption analysis model based on the comprehensive feature, and determining the power consumption disturbance factor based on the comprehensive feature; The training process of the power analysis model includes: Get the dataset; Perform feature engineering on the power plant dataset to identify the main features that affect power generation; Based on the main features and the feature data, XGBoost regression training is performed on each type of power plant to obtain a corresponding basic machine learning model; The basic machine learning model is subjected to model fusion processing to obtain the power supply analysis model.

2. The green electricity storage scheduling method based on big data according to claim 1 is characterized in that: The training process of the power consumption analysis model includes: Acquire time series data and historical natural language data. Time series data includes electricity consumption data, weather data, holidays, and event data on the electricity user side. Historical natural language data includes social media data, news report data, and user feedback. Preprocess time series data and historical natural language data. The preprocessing of time series data includes missing value processing, normalization and feature engineering. Feature engineering is also used to fuse features with correlation relationships. The preprocessing of historical natural language data includes text cleaning, word segmentation and word vectorization.

3. The green electricity storage scheduling method based on big data according to claim 2 is characterized in that: The GRU recurrent neural network is used to process the time series data to obtain a first fusion feature, including: The GRU recurrent neural network includes an input layer, a GRU layer and a fully connected layer; The time series data is input as a three-dimensional tensor through the input layer, the long-term dependency of the time series is captured in the three-dimensional tensor through the GRU layer, and the long-term dependency is feature fused through the fully connected layer to obtain the first fused feature, which is the time series feature.

4. The green electricity storage scheduling method based on big data according to claim 3 is characterized in that: The NLP language model is used to process the time series data and the historical natural language data to obtain a second fusion feature, including: The NLP language model includes an embedding layer, a GRU layer and a fully connected layer; Obtain word vectors for the historical natural language data, and process the word vectors through an embedding layer to obtain semantic and contextual information related to electricity usage; Extracting semantic and contextual information through the GRU layer of the NLP language model to obtain text features; The time series features and the text features are fused through the fully connected layer of the NLP language model to obtain a second fused feature.

5. The green electricity storage scheduling method based on big data according to claim 4 is characterized in that: The connecting of the first fusion feature and the second fusion feature to obtain a comprehensive feature, determining the power consumption analysis model according to the comprehensive feature, and determining the power consumption disturbance factor according to the comprehensive feature include: Connecting the first fusion feature and the second fusion feature to obtain a comprehensive feature, and obtaining a power consumption prediction result through the output layer; The power consumption analysis model uses mean square error as a loss function and is trained using Adam or RMSprop optimizer, and the training process is optimized using cross validation; The trained power consumption analysis model is analyzed using one of SHAP value and LIME to obtain the power consumption disturbance factor.

6. The green electricity storage scheduling method based on big data according to claim 1 is characterized in that: The acquiring of the data set further includes: The data was obtained through third-party platforms and data synthesis, where the data set includes historical power generation, meteorological data, and characteristic data of distributed green power generation equipment; In addition, the acquired data set is subjected to data cleaning and data conversion processing; wherein data cleaning includes missing value processing and outlier detection, wherein missing value processing is performed by filling and deleting missing values, wherein data conversion includes identifying abnormal data using the Z-score statistical method; wherein data conversion includes time feature processing and category coding processing, wherein time feature processing converts time features into numerical types, wherein category coding processing includes converting power plant types into numerical types.

7. The green electricity storage scheduling method based on big data according to claim 6 is characterized in that: The method further comprises: The data obtained from the third-party platform is padded and enhanced using data synthesis to obtain the data set, wherein the data enhancement is obtained by using random data generated by statistical distribution.

8. The green electricity storage scheduling method based on big data according to claim 6 is characterized in that: According to the main features and the feature data, XGBoost regression training is used for each type of power plant to obtain a corresponding basic machine learning model, including: According to each type of power plant, a model that meets the main characteristics and the characteristic data is selected from basic machine learning for training; The trained basic machine learning model is used for prediction, evaluation and hyperparameter tuning, where the hyperparameter tuning includes grid search and random search, to complete the training of the basic machine learning model for each type of power plant, where the basic machine model is selected according to the power plant type and power generation characteristics of the power plant.

9. The green electricity storage scheduling method based on big data according to claim 1, characterized in that: The performing model fusion processing on the basic machine learning model to obtain the power supply analysis model includes: Decision tree regression and support vector regression are used as meta-learners, and multiple trained basic machine learning models are used as features to fit the meta-learners to obtain the power analysis model.

10. The green electricity storage scheduling method based on big data according to claim 1, characterized in that: The determining of a green electricity storage scheduling strategy based on the electricity consumption forecast result, the electricity consumption interference factor, the power generation forecast result, and the current green electricity storage amount includes: Determining the electricity demand within a preset time period based on the electricity consumption forecast result and the electricity consumption interference factor, and determining the total power generation of the connected power plant within the preset time period based on the power generation forecast result; According to the electricity demand, the total power generation and the current green electricity storage, a corresponding green electricity storage scheduling strategy is selected, wherein the green electricity storage scheduling strategy includes at least one of green electricity priority, energy storage system scheduling, dynamic pricing and load management.

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