Work

Wind Speed Prediction Based on Wavelet Decomposition and the Fractal-Based LSTM Method

Year: 2026

Type: article

Abstract: Accurate wind speed prediction is essential for wind power generation and wind-resistant structural design. In this research, a short-term wind speed prediction model that combines the wavelet decomposition (WD) method and a fractal-based long short-term memory (LSTM) network is proposed. The fractal dimensions of the dataset at each level, which are decomposed using the WD method, are calculated using the box-counting method. With the dynamic learning rate in the loss function updated by fractal dimensions, the gradient-enhanced LSTM network is applied for wind speed prediction. Experimental wind speed data collected during wind field measurement experiments in Pingtan, Fujian Province, China, were used to validate the proposed wind speed prediction model. The predicted wind speed results at different considered time intervals are compared with those of the traditional LSTM method. The results suggest that the proposed method significantly improves the accuracy of wind speed forecasting.

Source: Fractal and Fractional

DOI: 10.3390/fractalfract10050322

Authors: Dandan Xia, Shaokun Shi, Yongchen Peng, Zhiqun Yuan, Li Lin

Institution: Xiamen University of Technology

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action

Open Access status: gold

Estimated APC paid: $3,258