A Short-Term Integrated Wind Speed Prediction System Based on Fuzzy Set Feature Extraction

TL;DR

This paper introduces an integrated wind speed prediction system that combines fuzzy feature extraction with machine learning to improve accuracy and stability in forecasting complex, variable wind speeds.

Contribution

It presents a novel multiframe prediction system using fuzzy set feature extraction and dynamic model weighting for enhanced wind speed forecasting.

Findings

Demonstrated improved prediction accuracy on Penglai wind farm data

Enhanced model stability and generalization ability

Effective handling of nonlinear and nonstationary wind speed data

Abstract

Wind energy has significant potential owing to the continuous growth of wind power and advancements in technology. However, the evolution of wind speed is influenced by the complex interaction of multiple factors, making it highly variable. The nonlinear and nonstationary nature of wind speed evolution can have a considerable impact on the overall power system. To address this challenge, we propose an integrated multiframe wind speed prediction system based on fuzzy feature extraction. This system employs a convex subset partitioning approach using a triangular affiliation function for fuzzy feature extraction. By applying soft clustering to the subsets, constructing an affiliation matrix, and identifying clustering centers, the system introduces the concepts of inner and boundary domains. It subsequently calculates the distances from data points to the clustering centers by measuring both interclass and intraclass distances. This method updates the cluster centers using the membership matrix, generating optimal feature values. Building on this foundation, we use multiple machine learning methods to input the fuzzy features into the prediction model and integrate learning techniques to predict feature values. Because different datasets require different modeling approaches, the integrated weight-updating module was used to dynamically adjust model weights by setting a dual objective function to ensure the accuracy and stability of the prediction. The effectiveness of the proposed model in terms of prediction performance and generalization ability is demonstrated through an empirical analysis of data from the Penglai wind farm.