Power consumer load prediction method and device based on support vector machine, equipment and storage medium

Abstract

The invention provides a power consumer load prediction method and device based on a support vector machine, electronic equipment and a computer readable storage medium. The method comprises the stepsof obtaining a to-be-predicted time period and a condition data set corresponding to the to-be-predicted time period; preprocessing the condition data in the condition data set, and normalizing the preprocessed condition data set to obtain an input parameter set; inputting the input parameter set into a support vector machine prediction model to enable the support vector machine prediction modelto obtain a prediction result according to the input parameter set; and performing reverse normalization processing on the prediction result to obtain a load prediction value set corresponding to theto-be-predicted time period. According to the method, when the load is predicted, the condition data is preprocessed and normalized, so that the condition data features are more unified, and the calculation speed is higher; and a support vector machine prediction model is adopted to perform load prediction, so that the accuracy is higher than that of a traditional method.

Description

Technical field [0001] This application relates to the field of data prediction, and in particular to a support vector machine-based power user load prediction method, device, electronic equipment, and computer-readable storage medium. Background technique [0002] Power load forecasting for power users is an important part of economic dispatch in the power system. Accurate power load forecasting can enable power users to reasonably arrange power consumption time and reduce power costs. With the development of social economy and power market, power load forecasting becomes more and more important, and the requirements for the accuracy of power load forecasting are also getting higher and higher. At present, the traditional power user load forecasting methods based on support vector machines are often not accurate and have large errors. Summary of the invention [0003] The embodiments of the present application provide a support vector machine-based power user load forecasting me...

AI Technical Summary

Problems solved by technology

At present, the traditional load forecasting methods based on support vector machines are often not accurate enough and have large errors.

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