Journal Article

Wind Speed and Power Ultra Short-Term Robust Forecasting Based on Takagi–Sugeno Fuzzy Model

Liu, Fang, Li, Ranran, Dreglea, Aliona

2019

Overview

Accurate wind power and wind speed forecasting remains a critical challenge in wind power systems management. This paper proposes an ultra short-time forecasting method based on the Takagi–Sugeno (T–S) fuzzy model for wind power and wind speed. The model does not rely on a large amount of historical data and can obtain accurate forecasting results though efficient linearization. The proposed method employs meteorological measurements as input. Next, the antecedent and the consequent parameters of the forecasting model are identified by the fuzzy c-means clustering algorithm and the recursive least squares method. From these components, the T–S fuzzy model is obtained. Wind farms located in China (Shanxi Province) and in Ireland (County Kerry) are considered as cases with which to validate the proposed forecasting method. The forecasting results are compared with results from the contemporary machine learning-based models including support vector machine (SVM), the combined model of SVM and empirical mode decomposition, and back propagation neural network methods. The results show that the proposed T–S fuzzy model can effectively improve the precision of the short-term wind power forecasting.

• Publisher: MDPI AG
• Subject: Accuracy / Design / Energy / Genetic algorithms / linearization / machine learning / Neural networks / Partial differential equations / Performance evaluation / Phase transitions / Short term / Statistical methods / Time series / Wavelet transforms / Weather forecasting / Wind farms / Wind power / wind power: wind speed: T–S fuzzy model: forecasting