Multi-model integrated load prediction method based on wavelet transform

Abstract

The invention relates to a multi-model integrated load prediction method based on wavelet transform, which is divided into four stages: 1, on the basis of considering multiple influence factors, historical load related data is subjected to a maximum information coefficient feature selection technology to obtain a feature candidate set with high correlation; 2, in order to obtain a stable load sequence and improve the prediction precision, multiple wavelet transforms are integrated into a multi-prediction model; and 3, each load correlation sequence after wavelet function decomposition is trained by an intelligent prediction sub-model, and the sub-models provide different predictions in the same hour; 4, combining the optimal prediction of each time period and providing a final prediction result by adopting online secondary learning in an integrated decision process. According to the method, the load prediction precision can be further improved on the basis of various single predictionmodels. The method is high in generalization ability, can adapt to various environments, has high applicability, and is beneficial to reducing the operation cost of a power system.

Description

technical field [0001] The invention relates to a load forecasting technology, in particular to a multi-model integrated load forecasting method based on wavelet transform. Background technique [0002] Load forecasting is crucial to energy planning and security. Based on load forecasting, a series of power system operations including power system scheduling, planning, power price adjustment and maintenance can be performed. High-precision load forecasting is an important guarantee for improving the utilization rate of power generation equipment and the effectiveness of economic dispatch. Today's artificial intelligence technology is playing an increasingly important role in the field of load forecasting. Artificial neural networks, support vector machines, and deep learning are popular technologies in the field of short-term load forecasting, all of which have improved the accuracy of load forecasting to a certain extent. However, the above single model has its specific a...

AI Technical Summary

Problems solved by technology

[0007] The present invention aims at the problem of low forecasting accuracy and limited application range of a single algorithm, and proposes a multi-model integrated load forecasting method based on wavelet transform. Forecasting model to further improve the short-term load forecasting accuracy of the model

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