Data identification and truth value compensation method for new energy prediction

By combining the segmented two-way quartering method with a neural network, outliers in renewable energy power generation data are identified and eliminated, and true value compensation is performed, which solves the problem of uneven data quality in renewable energy power generation forecasting and improves the forecast accuracy and reliability.

CN120675035APending Publication Date: 2025-09-19天津瑞源电气有限公司
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Patent Information

Application Number
CN202510734339.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-04
Publication Date
2025-09-19

AI Technical Summary

Technical Problem

In existing renewable energy power generation prediction technologies, the quality of raw data varies greatly, traditional abnormal data identification methods are difficult to adapt to complex working conditions, and time series prediction models are highly sensitive to abnormal data, resulting in accumulated prediction errors and affecting prediction accuracy and reliability.

Method used

The segmented two-direction quartering method is used to identify and eliminate outliers, combined with neural networks for true value compensation, and the BP neural network training model is used for data filling and anomaly identification to improve data availability.

Benefits of technology

It improves the data accuracy and reliability of new energy power generation forecasts, effectively corrects abnormal data and reduces forecast errors.

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Abstract

The invention provides a data identification and true value compensation method for new energy prediction, and the method comprises the following steps: S1, inputting photovoltaic power generation data or wind power generation data; s2, setting main independent variables and dependent variables; s3, identifying and removing missing values and abnormal values to obtain data 1; s4, if the data is used for regression prediction, outputting the data 1 to carry out regression prediction; if the data is used for time sequence prediction, missing values and abnormal values adopt a neural network truth value compensation mode. For the problem of data distortion during photovoltaic or wind power generation prediction, a data identification method based on a segmented two-direction quartering method is provided for regression prediction; on the basis of regression prediction data, an abnormal data identification and truth value compensation method based on a neural network is provided for a time sequence prediction problem, and the method can improve the availability of data and improve the prediction precision of new energy.
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