Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7164673 | Energy Conversion and Management | 2014 | 10 Pages |
Abstract
Forecasting the wind speed is indispensable in wind-related engineering studies and is important in the management of wind farms. As a technique essential for the future of clean energy systems, reducing the forecasting errors related to wind speed has always been an important research subject. In this paper, an optimized hybrid method based on the Autoregressive Integrated Moving Average (ARIMA) and Kalman filter is proposed to forecast the daily mean wind speed in western China. This approach employs Particle Swarm Optimization (PSO) as an intelligent optimization algorithm to optimize the parameters of the ARIMA model, which develops a hybrid model that is best adapted to the data set, increasing the fitting accuracy and avoiding over-fitting. The proposed method is subsequently examined on the wind farms of western China, where the proposed hybrid model is shown to perform effectively and steadily.
Related Topics
Physical Sciences and Engineering
Energy
Energy (General)
Authors
Zhongyue Su, Jianzhou Wang, Haiyan Lu, Ge Zhao,