Intelligent Prediction of Capacity Margin in Different Time Periods based on Wavelet Analysis Algorithm

Abstract

In order to understand the intelligent prediction of capacity margin in different time periods, the author proposes a research on intelligent prediction of capacity margin in different time periods based on wavelet analysis. The author first uses wavelet decomposition and neural networks as tools to predict electricity prices in different time periods. The changes in the electricity price sequence during different time periods are relatively single, which is conducive to the learning and training of neural networks, thereby improving prediction accuracy. Secondly, compare the predicted results of time slot capacity margin based on wavelet analysis technology with the actual values. Finally, the experimental results indicate that the average relative percentage error of short-term electricity price prediction can reach 11.40%. The intelligent Jun page measurement method for capacity margin based on wavelet analysis proposed by the author can effectively improve the prediction accuracy of power grid capacity margin, and has strong practicality and effectiveness.