Swarm intelligence
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Locally recurrent neural networks for wind speed prediction using spatial correlation
Information Sciences: an International Journal
BP neural network with rough set for short term load forecasting
Expert Systems with Applications: An International Journal
Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting
Knowledge-Based Systems
Short term wind speed prediction based on evolutionary support vector regression algorithms
Expert Systems with Applications: An International Journal
Combination of artificial neural-network forecasters for prediction of natural gas consumption
IEEE Transactions on Neural Networks
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As a clean and renewable energy source, wind energy has been increasingly gaining global attention. Wind speed forecast is of great significance for wind energy domain: planning and design of wind farms, wind farm operation control, wind power prediction, power grid operation scheduling, and more. Many wind speed forecasting algorithms have been proposed to improve prediction accuracy. Few of them, however, have studied how to select input parameters carefully to achieve desired results. After introducing a Back Propagation neural network based on Particle Swam Optimization (PSO-BP), this paper details a method called IS-PSO-BP that combines PSO-BP with comprehensive parameter selection. The IS-PSO-BP is short for Input parameter Selection (IS)-PSO-BP, where IS stands for Input parameter Selection. To evaluate the forecast performance of proposed approach, this paper uses daily average wind speed data of Jiuquan and 6-hourly wind speed data of Yumen, Gansu of China from 2001 to 2006 as a case study. The experiment results clearly show that for these two particular datasets, the proposed method achieves much better forecast performance than the basic back propagation neural network and ARIMA model.