Work

Short-Term Wind Power Forecasting Based on OMNIC and Adaptive Fractional Order Generalized Pareto Motion Model

Year: 2024

Type: article

Abstract: With the rapid development of renewable energy, accurately forecasting wind power is crucial for the stable operation of power systems and effective energy management. This paper proposes a short-term wind power forecasting method based on the Orthogonalized Maximal Information Coefficient (OMNIC) combined with an Adaptive Fractional Generalized Pareto Motion (fGPm) model. The method quantifies the influence of meteorological factors on wind power prediction and identifies the optimal set and number of influencing factors. The model accounts for long-range dependence (LRD) in time series data and constructs an uncertainty model using the properties and parameters of the fractional generalized Pareto distribution (GPD), significantly improving prediction accuracy under nonlinear conditions. The proposed approach was validated using a real dataset from a wind farm in northwest China and compared with other models such as Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) and Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU). Results demonstrate that the adaptive fGPm model significantly reduces wind power forecasting errors, offering high accuracy and robustness. This method provides practical value in alleviating peak voltage and frequency regulation challenges in power systems and in promoting the integration of wind power into the grid.

Source: Energies

DOI: 10.3390/en17235848

Authors: Fan Cai, Dong dong Chen, Yuesong Jiang, Tong bo Zhu

Institutions: Minnan University of Science and Technology, Beihang 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: $2,955