Work

A hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction

Year: 2024

Type: article

Abstract: Efficient and accurate wind power prediction is crucial for enhancing the reliability and safety of power system operation and management. The integration of advanced data-driven forecasting methods with precise and efficient data feature decomposition and extraction techniques is necessary to improve the accuracy of wind power generation prediction, due to the inherent randomness and nonlinearity of physical states impacting wind power output, as well as the abundance of redundant information in their original measurement data. This paper proposes a novel hybrid wind power prediction model based on seasonal feature decomposition of wind energy data and enhanced feature extraction. Its effectiveness and superiority are demonstrated through comprehensive multi-model comparisons.

Source: Energy and AI

DOI: 10.1016/j.egyai.2024.100442

Authors: Weipeng Li, Yuting Chong, Xin Guo, Jun Liu

Institution: Xi'an 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: $3,200