Short term wind speed forecasting for wind turbine applications using linear prediction method

Multiple architecture system for wind speed prediction

“…The wind speed time series of the predicted sites and its neighbouring sites are used to predict the wind speed [8][9][10]. By contrast to the physical models, conventional statistical models do well in short term prediction, some of these models are: autoregressive models (AR), moving average model (MA), autoregressive moving average model (ARMA) [11], autoregressive integrated moving average model (ARIMA) [12,13], Kalman filtering [14,15] and linear approaches based on statistical regression [16]. Artificial intelligence techniques have been some of the widely adopted techniques for the purpose of wind speed prediction.…”

Generic maximum power point tracking controller for small-scale wind turbines

“…Wind speed-time series data typically exhibit autocorrelation, which can be defined as the degree of dependence on preceding values [17]. Therefore, variation of mechanically stored energy in the rotating parts ( )…”

Assessment and Performance Evaluation of a Wind Turbine Power Output

“…Wind turbine control system operators require wind speed prediction times in the range of seconds ahead. Wind speed prediction can be via numerical weather prediction (NWP) methods as summarized in previous works [17][18][19] and [1] who developed an adaptive neural fuzzy inference system (ANFIS) for wind speed forecasting. Another approach is through statistical methods as described in Reference [3] and used in Reference [8] to develop a linear prediction model effective for wind speed forecasting or the fractionally-integrated Auto-Regressive Integral and Moving Average (f-ARIMA) method adopted in Reference [20].…”