Data prediction method based on FEEMD decomposition time sequence

Abstract

The invention discloses a data prediction method based on an FEEMD decomposition time sequence. The data prediction method comprises the steps: building an ELM prediction model, and carrying out the optimization of the ELM prediction model through the fusion of a rhododendron search algorithm; decomposing the historical data time sequence into an intrinsic mode function component and a residual component by adopting an FEEMD algorithm; decomposing real numerical signals in the intrinsic mode function component and the residual component by adopting a variational mode decomposition method; taking a result obtained by variational mode decomposition as input of an ELM prediction model to solve prediction values of an intrinsic mode function component and a residual component; and superposingthe prediction values of the eigenmode function component and the residual component to obtain a final prediction value. According to the data prediction method, the ELM prediction model is optimizedby adopting the rhododendron search algorithm, so that rapid and accurate data prediction is realized.

Description

technical field [0001] The invention relates to a data prediction method, in particular to a data prediction method based on FEEMD decomposition time series. Background technique [0002] At present, the current combined forecasting methods are mainly divided into two categories: the first category is to combine the forecast results of multiple single forecasting models by weight; the second category is to combine the time series decomposition technology with the forecasting model. Since the electric load is a non-stationary time series sensitive to external factors, the existing forecasting methods can only assume that it is a stationary signal, and it is difficult to improve the forecasting accuracy, which makes load forecasting difficult. Therefore, it is difficult to obtain the correct result by weighted combination of the prediction results of a single prediction model, and the prediction accuracy cannot meet the requirements. However, the multi-frequency components ex...

AI Technical Summary

Problems solved by technology

FEEMD overcomes the shortcomings of selecting wavelet bases and not being easy to determine the decomposition scale in WT, but the mode mixing problem limits its application range, and the fast ensemble empirical mode decomposition (FEFEMD)[8] makes up for the FEEMD The modal aliasing defect during decomposition also speeds up the decomposition speed, which is more suitable for power load decomposition in complex environments

However, when predicting each decomposed subsequence, the input features used for the forecasting model will greatly affect the accuracy and efficiency of short-term load forecasting

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