4.5 Article

One-day ahead wind speed/power prediction based on polynomial autoregressive model

Journal

IET RENEWABLE POWER GENERATION
Volume 11, Issue 11, Pages 1430-1439

Publisher

INST ENGINEERING TECHNOLOGY-IET
DOI: 10.1049/iet-rpg.2016.0972

Keywords

polynomials; autoregressive processes; fuzzy neural nets; fuzzy reasoning; power engineering computing; wind power plants; one-day ahead wind speed prediction; one-day ahead wind power prediction; polynomial autoregressive model; renewable energy generation methods; PAR model; hourly average wind speed measurements; ANN; ANFIS; adaptive neuro fuzzy inference system; artificial neural network

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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 Ceme 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 12h.

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