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L. Hui, K. L. Shi and P. G. McLaren, “Neural-NetworkBased Sensorless Maximum Wind Energy Capture with Compensated Power Coefficient,” IEEE Transactions on Industry Applications, Vol. 41, No. 6, 2005, pp. 15481556. doi:10.1109/TIA.2005.858282

has been cited by the following article:

• TITLE: Very Short-Term Generating Power Forecasting for Wind Power Generators Based on Time Series Analysis

  AUTHORS: Atsushi Yona, Tomonobu Senjyu, Funabashi Toshihisa, Chul-Hwan Kim

  KEYWORDS: Very Short-Term Ahead Forecasting; Wind Power Generation; Wind Speed Forecasting; Time Series Analysis

  JOURNAL NAME: Smart Grid and Renewable Energy, Vol.4 No.2, May 29, 2013

  ABSTRACT: In recent years, there has been introduction of alternative energy sources such as wind energy. However, wind speed is not constant and wind power output is proportional to the cube of the wind speed. In order to control the power output for wind power generators as accurately as possible, a method of wind speed estimation is required. In this paper, a technique considers that wind speed in the order of 1 - 30 seconds is investigated in confirming the validity of the Auto Regressive model (AR), Kalman Filter (KF) and Neural Network (NN) to forecast wind speed. This paper compares the simulation results of the forecast wind speed for the power output forecast of wind power generator by using AR, KF and NN.