Work

Ultra-Short-Term Wind Power Prediction with Multi-Scale Feature Extraction Under IVMD

Year: 2025

Type: article

Abstract: To mitigate wind power intermittency effects on forecasting accuracy, this study proposes a novel ultra-short-term prediction method based on improved variational mode decomposition (IVMD) and multi-scale feature extraction. First, the maximum information coefficient identified meteorological features strongly correlated with wind power, such as wind speed and wind direction, thereby reducing model input dimensionality. Permutation entropy then served as the fitness function for the sparrow search algorithm (SSA), enabling adaptive IVMD parameter optimization for effective decomposition of non-stationary sequences. The resulting intrinsic mode functions and key meteorological features were input into a prediction model integrating a temporal convolutional network (TCN) and bidirectional gated recurrent unit (BiGRU) to capture global trends and local fluctuations. The SSA was reapplied to optimize TCN-BiGRU hyperparameters, enhancing adaptability. Simulations using operational data from a Xinjiang wind farm demonstrated that the proposed method achieved a coefficient of determination (R2) of 0.996, representing an absolute increase of 0.060 over the XGBoost benchmark (R2 = 0.936). This confirms significant enhancement of ultra-short-term forecasting accuracy.

Source: Processes

DOI: 10.3390/pr13082606

Authors: Jian Sun, Huakun Wei, Chuangxin Chen

Institution: China Three Gorges University

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, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,896