PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Karakus O., Kuruoglu E. E., Altinkaya M. A. One-day ahead wind speed/power prediction based on polynomial autoregressive model. In: IET Renewable Power Generation, vol. 11 (11) pp. 1430 - 1439. IET, 2017.
 
 
Abstract
(English)
Wind has been one of the popular renewable energy generation methods in the last decades. Foreknowledge of power to be generated from wind is crucial especially for planning and storing the power. It is evident in various experimental data that wind speed time series has non-linear characteristics. It has been reported in the literature that nonlinear prediction methods such as artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS) perform better than linear autoregressive (AR) and AR moving average models. Polynomial AR (PAR) models, despite being non-linear, are simpler to implement when compared with other non-linear AR models due to their linear-in-the-parameters property. In this study, a PAR model is used for one-day ahead wind speed prediction by using the past hourly average wind speed measurements of eşme and Bandon and performance comparison studies between PAR and ANN-ANFIS models are performed. In addition, wind power data which was published for Global Energy Forecasting Competition 2012 has been used to make power predictions. Despite having lower number of model parameters, PAR models outperform all other models for both of the locations in speed predictions as well as in power predictions when the prediction horizon is longer than 12 h.
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8049625
DOI: 10.1049/iet-rpg.2016.0972
Subject Wind energy
Wind speed forecasting
Polynomial autoregressive models
I.5.4 PATTERN RECOGNITION. Applications. Signal processing
G.3 PROBABILITY AND STATISTICS. Stochastic processes
60G25 Prediction theory


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