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A novel compound wind speed forecasting model based on the back propagation neural network optimized by bat

Yanbin Cui¹, Chenchen Huang², Yanping Cui³

¹Department of Mechanical Engineering, North China Electric Power University, Baoding, 071000, China.

Environmental Science and Pollution Research International

|December 30, 2019

Summary

This summary is machine-generated.

This study introduces a novel compound model to improve short-term wind speed forecasting accuracy. The model effectively captures nonlinear characteristics, outperforming existing methods for renewable energy integration.

Keywords:

Back propagation neural network Bat algorithm Fast ensemble empirical mode decomposition Phase space reconstruction Wind speed forecasting

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Published on: March 28, 2025

Area of Science:

Renewable Energy Systems
Computational Intelligence
Signal Processing

Background:

Wind power is crucial for transitioning from fossil fuels to clean energy.
Accurate wind speed forecasting is essential for large-scale integration into power grids.
Existing forecasting models struggle with the nonlinear characteristics of wind speed data.

Purpose of the Study:

To develop a novel compound model for enhanced short-term wind speed forecasting.
To improve the accuracy and efficiency of wind power integration into electrical systems.
To address the limitations of traditional forecasting methods in capturing complex wind patterns.

Main Methods:

Data preprocessing using fast ensemble empirical mode decomposition.
Phase space reconstruction for dynamic selection of input/output vectors.
Optimization of a back propagation neural network using the bat algorithm.

Main Results:

The proposed compound model effectively captures nonlinear characteristics of wind speed signals.
The model demonstrates superior performance compared to parallel forecasting models.
Accurate short-term wind speed predictions were achieved through aggregated sequential prediction.

Conclusions:

The novel compound model offers a significant improvement in short-term wind speed forecasting accuracy.
This approach facilitates more reliable integration of wind energy into the power grid.
The method provides a robust solution for handling the inherent complexities of wind speed data.