Name
Title | ||
|---|---|---|
Optimal parameters selection for BP neural network based on particle swarm optimization: A case study of wind speed forecasting |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1016/j.knosys.2013.11.015 | Knowl.-Based Syst. |
Keywords | Field | DocType |
wind energy,wind speed forecast,input parameter selection,wind energy domain,6-hourly wind speed data,case study,particle swarm optimization,wind speed forecasting algorithm,wind farm operation control,daily average wind speed,bp neural network,optimal parameters selection,wind farm,wind power prediction,wind speed | Particle swarm optimization,Data mining,Mathematical optimization,Renewable energy,Wind speed,Computer science,Simulation,Power grid,Autoregressive integrated moving average,Artificial neural network,Operation scheduling,Wind power | Journal |
Volume | ISSN | Citations |
56, | 0950-7051 | 16 |
PageRank | References | Authors |
0.94 | 10 | 6 |