5. Conclusion

References Forecast

[38] Average

[39] Wind

[40] Wind

[41] Wind

[42] Hourly

[43] Wind

variable

88 Time Series Analysis and Applications

hourly wind speed

speed

speed

speed

average wind speed

speed

[44] Win speed 1–6 min

Forecast horizon

1, 2 and 3-step(s)

1 h up to 10 h

1 month MSE,

(s), and 1–6 hour (s)

MAE, and MAPE

MAE, and MAPE

MAE, MSE, and MAPE

Error metric

error (DME)

1 h ME, MSE, and MAE

Time series method

Hybrid (ARIMA-

model based on ARIMA)

Hybrid (ARIMA-ANN and ARIMA-Kalman)

1 h NA ARMA-GARCH 7 years of hourly wind

Hybrid (Empirical

mode decomposition (EMD)-Least squares support vector machines (LSSVM)-AR)

RMSE ARMA 9 years of hourly wind

ANN)

1 day MAPE Hybrid (KF-ANN

Data Finding

The combination of ARIMA and ANN predicts the wind speed with more accuracy than the individual ARIMA and

The ARIMA model provides inaccurate wind speed forecasts due to its limitation to

Both hybrid methods can obtain accurate forecasts and are appropriate for nonstationary wind speed

The ARMA-GARCH model is proved efficient in capturing the trend change of wind speed mean and volatility over time

For longer term forecasting, the ARMA models with transformed and standardized data perform better than the persistence model

ARIAM models provide more sensitivity than the ANN methods to the adjustment and prediction of the wind

The proposed hybrid approach is proved more accurate than the existing forecasting approaches

speed

ANN

capture the nonlinearity of the wind speed patterns

datasets

monitoring sites in North Dakota

Daily wind speed observations from two meteorological stations in Mosul, Iraq and Johor, Malaysia

Hourly wind speed measurements from a

speed data from an observation site in Colorado, USA

speed data of five locations in Navarre,

measurements from the South Coast of Oaxaca,

1 year of wind speed data measurements in Beloit, Kansas, USA

Spain

Mexico

ARIMA 7 years of wind speed

station

1 month of wind speed measurements in three regions of Mexico

> This chapter provides a comprehensive literature review to demonstrate the application of time-series methods for renewable energy forecasting. In spite of recent developments in intelligent methods and their extensive applications for more accurate solar energy/wind power forecasting, our literature review concludes that time-series methods, individually or in combination with intelligent methods, are still viable options for short-term forecasting of intermittent renewable energy sources due to their less computational complexities.
