A method for processing wind and solar power prediction data

Abstract

The present invention discloses a method for processing wind and solar power prediction data, which relates to the field of energy technology, and includes step one: data collection; step two: data preprocessing; step three: feature selection; step four: data set division; step five: model selection, training, and evaluation; and step six: result analysis and application. The present invention ensures the timeliness and continuity of data, provides high-quality real-time data to support prediction, selects appropriate equipment and platforms to ensure data accuracy and reliability, reduces noise fluctuations through preliminary inspection, processing missing data, normalization, and data smoothing, improves data quality, uses statistical analysis and feature selection algorithms to screen significant features, enhances model prediction performance and efficiency, and adopts a scientific data set division method to ensure that the model evaluation is stable and reliable, maintains the randomness and representativeness of the training set, validation set, and test set, prevents overfitting, and improves the generalization ability of the model.

Description

Technical Field

[0001] The present invention relates to the field of energy technology, and in particular to a method for processing wind and solar power prediction data. Background Art

[0002] Wind and solar power refers to the power output of wind energy and solar energy converted into electrical energy. In wind power generation and solar photovoltaic power generation systems, wind and solar power are key indicators for evaluating power generation efficiency and power generation. The size of wind and solar power is affected by many factors such as wind speed, solar irradiance, and equipment efficiency. In the application process of wind and solar power, data budget processing is needed to optimize performance.

[0003] The existing technology has the following problems:

[0004] Traditional wind and solar power prediction data processing methods have limited data processing capabilities when dealing with large-scale or complex data sets due to their reliance on manual programming and fixed algorithms. In terms of computational efficiency, traditional methods require a lot of computing resources and time. At the same time, traditional methods are usually unable to achieve real-time data processing and prediction, which limits their use in application scenarios that require rapid response. Summary of the invention

[0005] The present invention provides a method for processing wind and solar power prediction data to solve the problems raised in the above background technology.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] A method for processing wind and solar power prediction data, comprising the following steps:

[0008] Step 1: Data collection;

[0009] Step 2: Data preprocessing;

[0010] Step 3: Feature selection;

[0011] Step 4: Dataset division;

[0012] Step 5: Model selection, training, and evaluation;

[0013] Step 6: Result analysis and application.

[0014] A further improvement of the technical solution of the present invention is that the step 1 further includes the following steps:

[0015] A1: Set the time frame and frequency of data collection to ensure the timeliness and continuity of data;

[0016] A2: Choose appropriate data collection equipment and platforms, such as weather stations, satellite remote sensing, IoT sensors, etc.

[0017] A3: Ensure that the data collection process complies with relevant standards and specifications to ensure data quality.

[0018] A further improvement of the technical solution of the present invention is that the step 2 further includes the following steps:

[0019] B1: Conduct a preliminary check on the raw data to identify and process outliers in the data;

[0020] B2: Process missing data, such as using interpolation or statistical methods for estimation;

[0021] B3: Normalize the data to eliminate the influence of different dimensions and orders of magnitude;

[0022] B4: Apply data smoothing techniques, such as moving average method, Gaussian filtering, etc., to reduce noise and fluctuations in the data.

[0023] A further improvement of the technical solution of the present invention is that the step three further includes the following steps:

[0024] C1: Use statistical analysis methods, such as correlation coefficient and chi-square test, to evaluate the relationship between each feature and the target variable;

[0025] C2: Use feature selection algorithms, such as recursive feature elimination and model-based feature selection, to screen out features that have a significant impact on the prediction target.

[0026] A further improvement of the technical solution of the present invention is that the step 4 further includes the following steps:

[0027] D1: The entire dataset needs to be carefully divided into three different parts according to the distribution characteristics and magnitude of the data: training set, validation set, and test set. The training set is mainly used in the model training process so that the model can learn the rules and characteristics in the data; the validation set is used for parameter adjustment and model selection during the model training process to prevent the model from overfitting and ensure that the model has good generalization ability; finally, the test set is used for the final performance evaluation after the model training is completed to verify the performance of the model on unknown data;

[0028] D2: When dividing the data set, scientific methods such as cross-validation are used to ensure the randomness and representativeness of the data set division. Cross-validation is a commonly used model evaluation method. By dividing the data set into several subsets and repeating the training and validation process on different subset combinations, the stability and reliability of the model can be effectively evaluated.

[0029] A further improvement of the technical solution of the present invention is that the step five further includes the following steps:

[0030] E1: Model selection: According to the nature of the problem and the characteristics of the data, select an appropriate prediction model, such as ARI MA, random forest, neural network, etc.;

[0031] E2: Training model: Use the training set data to train the selected model, divide the data into training set and validation set, use the training set to train the model, and evaluate the model performance on the validation set;

[0032] E3: Adjust model parameters based on the performance evaluation results on the validation set. You can use grid search, random search, Bayesian optimization and other methods to find the best hyperparameter combination.

[0033] E4: Model evaluation: Evaluate the final performance of the model on the test set. The test set should be a dataset independent of the training set and validation set to ensure the fairness and accuracy of the evaluation results.

[0034] E5: Model optimization: Based on the evaluation results on the test set, further optimize the model, which may include adjusting the model structure, adding data enhancement, using ensemble learning, etc.

[0035] E6: Deploy the model: Deploy the trained model to actual applications to perform prediction tasks and ensure the stability and accuracy of the model in actual applications.

[0036] A further improvement of the technical solution of the present invention is that the step six further includes the following steps:

[0037] F1: Analyze the forecast results and identify the periods and reasons for large forecast errors, which helps to understand the shortcomings of the model and provide direction for further improvement of the model;

[0038] F2: Apply the prediction results to the scheduling and management of wind and solar power generation systems to improve system efficiency and reliability. Through the prediction results, power generation plans can be better arranged, energy waste can be reduced, and economic benefits can be improved.

[0039] Due to the adoption of the above technical solution, the present invention has the following technical advances compared with the prior art:

[0040] 1. The present invention provides a method for processing wind and solar power prediction data. The timeliness and continuity of data can be ensured by setting step one and step two, providing high-quality, real-time data support for prediction. The accuracy and reliability of data can be guaranteed by selecting appropriate data acquisition equipment and platforms, and ensuring that the data acquisition process complies with relevant standards and specifications. The initial inspection of raw data, processing of missing data, normalization processing and application of data smoothing technology can effectively reduce noise and fluctuations in the data, effectively improve data quality, and lay a solid foundation for subsequent analysis.

[0041] 2. The present invention provides a method for processing wind and solar power prediction data. Through the settings of step two and step three, statistical analysis methods and feature selection algorithms can be used to screen out features that have a significant impact on the prediction target, thereby improving the prediction performance and efficiency of the model. Through a scientific data set division method, the stability and reliability of the model evaluation are ensured, while the randomness and representativeness of the training set, validation set, and test set are guaranteed, which helps to prevent model overfitting and improve the generalization ability of the model.

[0042] 3. The present invention provides a method for processing wind and solar power prediction data. Through the settings of step four and step five, a suitable prediction model can be selected and trained and adjusted, which can ensure that the model can accurately capture the rules and characteristics in the data. By evaluating the model performance on an independent test set, the final performance of the model can be evaluated fairly and accurately, providing a basis for model optimization, and further optimizing the model according to the evaluation results on the test set, and deploying the trained model into practical applications, which can improve the scheduling and management efficiency of wind and solar power generation systems, reduce energy waste, and improve economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 This is a flow chart of the wind and solar power prediction data processing method of the present invention. DETAILED DESCRIPTION

[0044] The present invention is further described in detail below in conjunction with embodiments:

[0045] Example 1

[0046] like Figure 1 As shown, the present invention provides a method for processing wind and solar power prediction data, and the method for processing wind and solar power prediction data includes the following steps:

[0047] Step 1: Data collection;

[0048] Step 2: Data preprocessing;

[0049] Step 3: Feature selection;

[0050] Step 4: Dataset division;

[0051] Step 5: Model selection, training, and evaluation;

[0052] Step 6: Result analysis and application.

[0053] Step one also includes the following steps:

[0054] A1: Set the time frame and frequency of data collection to ensure the timeliness and continuity of data;

[0055] A2: Choose appropriate data collection equipment and platforms, such as weather stations, satellite remote sensing, IoT sensors, etc.

[0056] A3: Ensure that the data collection process complies with relevant standards and specifications to ensure data quality.

[0057] Step 2 also includes the following steps:

[0058] B1: Conduct a preliminary check on the raw data to identify and process outliers in the data;

[0059] B2: Process missing data, such as using interpolation or statistical methods for estimation;

[0060] B3: Normalize the data to eliminate the influence of different dimensions and orders of magnitude;

[0061] B4: Apply data smoothing techniques, such as moving average method, Gaussian filtering, etc., to reduce noise and fluctuations in the data.

[0062] Step three also includes the following steps:

[0063] C1: Use statistical analysis methods, such as correlation coefficient and chi-square test, to evaluate the relationship between each feature and the target variable;

[0064] C2: Use feature selection algorithms, such as recursive feature elimination and model-based feature selection, to screen out features that have a significant impact on the prediction target.

[0065] Step 4 also includes the following steps:

[0066] D1: The entire dataset needs to be carefully divided into three different parts according to the distribution characteristics and magnitude of the data: training set, validation set, and test set. The training set is mainly used in the model training process so that the model can learn the rules and characteristics in the data; the validation set is used for parameter adjustment and model selection during the model training process to prevent the model from overfitting and ensure that the model has good generalization ability; finally, the test set is used for the final performance evaluation after the model training is completed to verify the performance of the model on unknown data;

[0067] D2: When dividing the data set, scientific methods such as cross-validation are used to ensure the randomness and representativeness of the data set division. Cross-validation is a commonly used model evaluation method. By dividing the data set into several subsets and repeating the training and validation process on different subset combinations, the stability and reliability of the model can be effectively evaluated.

[0068] Step five also includes the following steps:

[0069] E1: Model selection: According to the nature of the problem and the characteristics of the data, select an appropriate prediction model, such as ARI MA, random forest, neural network, etc.;

[0070] E2: Training model: Use the training set data to train the selected model, divide the data into training set and validation set, use the training set to train the model, and evaluate the model performance on the validation set;

[0071] E3: Adjust model parameters based on the performance evaluation results on the validation set. You can use grid search, random search, Bayesian optimization and other methods to find the best hyperparameter combination.

[0072] E4: Model evaluation: Evaluate the final performance of the model on the test set. The test set should be a dataset independent of the training set and validation set to ensure the fairness and accuracy of the evaluation results.

[0073] E5: Model optimization: Based on the evaluation results on the test set, further optimize the model, which may include adjusting the model structure, adding data enhancement, using ensemble learning and other methods.

[0074] E6: Deploy the model: Deploy the trained model to actual applications to perform prediction tasks and ensure the stability and accuracy of the model in actual applications.

[0075] Step six also includes the following steps:

[0076] F1: Analyze the forecast results and identify the periods and reasons for large forecast errors, which helps to understand the shortcomings of the model and provide direction for further improvement of the model;

[0077] F2: Apply the prediction results to the scheduling and management of wind and solar power generation systems to improve system efficiency and reliability. Through the prediction results, power generation plans can be better arranged, energy waste can be reduced, and economic benefits can be improved.

[0078] In this embodiment, the data collection step involves setting the time range and frequency, selecting the data acquisition equipment and platform, and ensuring that the data collection process complies with standard specifications; the data preprocessing step includes preliminary inspection, processing outliers, processing missing data, normalization processing and application of data smoothing technology; the feature selection step uses statistical analysis methods to evaluate the relationship between features and target variables, and uses feature selection algorithms to screen significant features; the model selection training and evaluation step involves selecting a prediction model, training the model, adjusting model parameters, evaluating model performance, and finally optimizing the model; the result analysis and application step includes analyzing prediction errors, identifying the causes of errors, and applying the prediction results to the dispatching management of wind and solar power generation systems.

[0079] The above generally describes the present invention in detail, but it is obvious to a person skilled in the art that some modifications or improvements can be made to the present invention. Therefore, modifications or improvements that do not depart from the spirit of the present invention are within the scope of protection of the present invention.

Claims

1. A method for processing wind and solar power prediction data, characterized in that: The wind and solar power prediction data processing method comprises the following steps: Step 1: Data collection; Step 2: Data preprocessing; Step 3: Feature selection; Step 4: Dataset division; Step 5: Model selection, training, and evaluation; Step 6: Result analysis and application.

2. A method for processing wind and solar power prediction data according to claim 1, characterized in that: The step 1 also includes the following steps: A1: Set the time frame and frequency of data collection to ensure the timeliness and continuity of data; A2: Choose appropriate data collection equipment and platforms, such as weather stations, satellite remote sensing, IoT sensors, etc. A3: Ensure that the data collection process complies with relevant standards and specifications to ensure data quality.

3. The method for processing wind and solar power prediction data according to claim 1, characterized in that: The step 2 also includes the following steps: B1: Conduct a preliminary check on the raw data to identify and process outliers in the data; B2: Process missing data, such as using interpolation or statistical methods for estimation; B3: Normalize the data to eliminate the influence of different dimensions and orders of magnitude; B4: Apply data smoothing techniques, such as moving average method, Gaussian filtering, etc., to reduce noise and fluctuations in the data.

4. The method for processing wind and solar power prediction data according to claim 1, characterized in that: The step three also includes the following steps: C1: Use statistical analysis methods, such as correlation coefficient and chi-square test, to evaluate the relationship between each feature and the target variable; C2: Use feature selection algorithms, such as recursive feature elimination and model-based feature selection, to screen out features that have a significant impact on the prediction target.

5. The method for processing wind and solar power prediction data according to claim 1, characterized in that: The step 4 also includes the following steps: D1: The entire dataset needs to be carefully divided into three different parts according to the distribution characteristics and magnitude of the data: training set, validation set, and test set. The training set is mainly used in the model training process so that the model can learn the rules and characteristics in the data; the validation set is used for parameter adjustment and model selection during the model training process to prevent the model from overfitting and ensure that the model has good generalization ability; finally, the test set is used for the final performance evaluation after the model training is completed to verify the performance of the model on unknown data; D2: When dividing the data set, scientific methods such as cross-validation are used to ensure the randomness and representativeness of the data set division. Cross-validation is a commonly used model evaluation method. By dividing the data set into several subsets and repeating the training and validation process on different subset combinations, the stability and reliability of the model can be effectively evaluated.

6. The method for processing wind and solar power prediction data according to claim 1, characterized in that: The step five also includes the following steps: E1: Model selection: Select an appropriate prediction model, such as ARIMA, random forest, neural network, etc., based on the nature of the problem and the characteristics of the data; E2: Training model: Use the training set data to train the selected model, divide the data into training set and validation set, use the training set to train the model, and evaluate the model performance on the validation set; E3: Adjust model parameters based on the performance evaluation results on the validation set. You can use grid search, random search, Bayesian optimization and other methods to find the best hyperparameter combination. E4: Model evaluation: Evaluate the final performance of the model on the test set. The test set should be a dataset independent of the training set and validation set to ensure the fairness and accuracy of the evaluation results. E5: Model optimization: Based on the evaluation results on the test set, further optimize the model, which may include adjusting the model structure, adding data enhancement, using ensemble learning, etc. E6: Deploy the model: Deploy the trained model to actual applications to perform prediction tasks and ensure the stability and accuracy of the model in actual applications.

7. The method for processing wind and solar power prediction data according to claim 1, characterized in that: Step six also The following steps are involved: F1: Analyze the forecast results and identify the periods and reasons for large forecast errors, which helps to understand the shortcomings of the model and provide direction for further improvement of the model; F2: Apply the prediction results to the scheduling and management of wind and solar power generation systems to improve system efficiency and reliability. Through the prediction results, power generation plans can be better arranged, energy waste can be reduced, and economic benefits can be improved.

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