Title :
Short term wind speed prediction using artificial neural networks
Author :
Lodge, Alexandra ; Xiao-Hua Yu
Author_Institution :
Dept. of Electr. Eng., California Polytech. State Univ., San Luis Obispo, CA, USA
Abstract :
As an alternative to fossil fuels, wind is a plentiful, clean, and renewable natural resource for energy. Essentially, power generation from wind depends on wind speed; thus, wind speed prediction becomes increasingly important for modern wind farm management and supply-demand balancing in the Smart Grid. However, wind speed is generally very difficult to estimate, due to its non-stationary and intermittent nature. In this paper, an approach based on artificial neural network (ANN) is developed. The neural network is trained and tested using data from the National Wind Technology Center. Computer simulation results show that the proposed neural network model can successfully predict wind speed in real-time.
Keywords :
neural nets; power engineering computing; wind power; ANN; National Wind Technology Center; artificial neural networks; fossil fuels; renewable natural resource; short term wind speed prediction; smart grid; supply-demand balancing; wind farm management; wind power generation; Artificial neural networks; Biological neural networks; Temperature measurement; Training; Wind speed; Wind speed prediction; artificial neural networks;
Conference_Titel :
Information Science and Technology (ICIST), 2014 4th IEEE International Conference on
Conference_Location :
Shenzhen
DOI :
10.1109/ICIST.2014.6920535