Wind Speed Forecasting Using Back Propagation Artificial Neural Networks in North of Iran

In recent years, wind power generation is rapidly gaining popularity due to the major concerns about the excessive emissions and global energy crisis. In addition, this kind of power systems have shown more security options than others. Due to the highly variable and intermittent nature of the wind energy, it is crucial to achieve higher accuracy of long-term wind speed forecasts for improving the reliability and economic feasibility of power systems. The forecasting is the best standard for comparing the of algorithm with current analytical methods. By importing the intelligent algorithms, we can overcome the obstacles of prediction and eliminate the volume of which are the main problems of determining the uncertainty nature of such renewable energy systems. Hence, this paper proposes a novel methodology for long-term wind speed forecasting using back propagation artificial neural network. The neural networks are powerful tools for solving the complex problems and providing tolerable standpoint from distributed energies. Simulation result illuminates that the proposed algorithm can offer highly features of compatibility and accuracy for wind predictions in comparison with actual wind speed reports of Iran Meteorological Organization. © 2017 Journal of Energy Management and Technology