Abstract
Two on step ahead wind speed forecasting models were compared. A univariate model was developed using a linear autoregressive integrated moving average (ARIMA). This method’s performance is well studied for a large number of prediction problems. The other is a multivariate model developed using a nonlinear autoregressive exogenous artificial neural network (NARX). This uses the variables: barometric pressure, air temperature, wind direction and solar radiation or relative humidity, as well as delayed wind speed. Both models were developed from two databases from two sites: an hourly average measurements database from La Mata, Oaxaca, Mexico, and a ten minute average measurements database from Metepec, Hidalgo, Mexico. The main objective was to compare the impact of the various meteorological variables on the performance of the multivariate model of wind speed prediction with respect to the high performance univariate linear model. The NARX model gave better results with improvements on the ARIMA model of between 5.5% and 10. 6% for the hourly database and of between 2.3% and 12.8% for the ten minute database for mean absolute error and mean squared error, respectively.
Highlights
At the end of 2014, the worldwide installed wind energy generating capacity was 369,597 MW; Europe having 134,007 MW, of which Germany and Spain stood out with 39,165 and 22,987 MW, respectively
The results showed that the hybrid model produced higher accuracy wind speed predictions than those of the separate autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) models for all three sites
Li et al [12] presented a robust two-step method for accurate wind speed forecasting based on a Bayesian combination algorithm and three neural network models: an adaptive linear element network (ADALINE), back propagation (BP) and a radial basis function (RBF)
Summary
At the end of 2014, the worldwide installed wind energy generating capacity was 369,597 MW; Europe having 134,007 MW, of which Germany and Spain stood out with 39,165 and 22,987 MW, respectively. Li et al [12] presented a robust two-step method for accurate wind speed forecasting based on a Bayesian combination algorithm and three neural network models: an adaptive linear element network (ADALINE), back propagation (BP) and a radial basis function (RBF). The results gave much better performance for both one step and multi-step ahead wind speed forecasts than support vector machine, autoregressive and ANNs. Hocaoglu et al [21] developed a model for the artificial prediction of wind speed data, from atmospheric pressure measurements using the hidden Markov models (HMMs) technique. To achieve higher accuracy forecasts, wind speed models using non-linear auto-regressive exogenous (NARX) modeling were developed This technique uses additional exogenous variables (i.e., other than wind speed) to generate more accurate forecasts with respect to ARIMA models solely based on wind speed time series. In the generation of the NARX model, only solar radiation or relative humidity was used due to the results from a correlation study
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
