Abstract
This work was developed for forecasting wind speed data by using various statistical models, which can further be utilized for the estimation of the annual energy production of commercial wind farms. The pattern of historical data available for modeling may be linear or nonlinear. For linear time series pattern, Autoregressive Integrated Moving Average (ARIMA), ARIMAX model by using exogenous variable X and Vector Autoregressive (VAR) models have been developed. In order to achieve good performance on the nonlinear pattern, Generalized Autoregressive Score (GAS), GAS with exogenous variable (GASX) model has been developed. Wind time series data taken from different site locations of the National Renewable Energy Laboratory (NREL) repository have been utilized to develop the models. In our investigation, it has been found that the VAR model performs the best in most of the cases since it includes more than one variable for the development of the model.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
L. **e, P.M.S. Carvalho, L.F.M. Ferreira, Liu Juhua, B.H. Krogh, N. Popli, Wind integration in power systems: operational challenges and possible solu-tions. Proc. IEEE 99, 214–232 (2011)
J. Jung, R.P. Broadwater, Current status and future advances for wind speed and power forecasting. Renew. Sustain. Energy Rev. 31, 762–777 (2014)
J. Yan, Y. Liu, S. Han, Y. Wang, S. Feng, Reviews on uncertainty analysis of wind power forecasting. Renew. Sustain. Energy Rev. 52, 1322–1330 (2015)
H. Liu, Tian Hqi, Li Yfei, Comparison of two new ARIMA-ANN and ARIMA Kalman hybrid methods for wind speed prediction. Appl. Energy 98, 415–424 (2012)
J. Zhao, Z.H. Guo, Z.Y. Su, Z.Y. Zhao, X. **ao, F. Liu, An improved multi-step forecasting model based on WRF ensembles and creative fuzzy systems for wind speed. Appl. Energy 162, 808–826 (2016)
D. Yang, V. Sharma, Z. Ye, L.I. Lim, L. Zhao, A.W. Aryaputera, Forecasting of global horizontal irradiance by exponential smoothing, using decompositions. Energy 81, 111–119 (2015)
J.L. Torres, A. GarcÃa, M. De Blas, A. De Francisco, Forecast of hourly average wind speed with ARMA models in Navarre (Spain). Solar Energy 79, 65–77 (2005)
J. Hu, J. Wang, K. Ma, A hybrid technique for short-term wind speed prediction. Energy 81, 563–574 (2015)
J. Koo, G.D. Han, H.J. Choi, J.H. Shim, Wind-speed prediction and analysis based on geological and distance variables using an artificial neural network: a case study in South Korea. Energy 93, 1296–1302 (2015)
T. Fu, C. Wang, A hybrid wind speed forecasting method and wind energy resource analysis based on a swarm intelligence optimization algorithm and an artificial intelligence model. Sustainability 10(11), 1–24 (2018)
D. Liu, J. Wang, H. Wang, Short-term wind speed forecasting based on spectral clustering and optimized echo state networks. Renew Energy 78, 599–608 (2015)
V. Nikolic, S. Motamedi, S. Shamshirband, D. Petkovic, S. Ch, M. Arif, Extreme learning machine approach for sensorless wind speed estimation. Mechatronics 34, 78–83 (2015)
S. Rajagopalan, S. Santoso, Wind power forecasting and error analysis using the autoregressive moving average modeling. In: IEEE Power Energy Society General Meeting, pp. 1–6 (2009)
U.C. Buyuksahin, S. Ertekin Improving forecasting accuracy of time series data using a new ARIMA-ANN hybrid method and empirical mode decomposition. Neurocomputing (2019)
H. do Nascimento Camelo, P.S. Lucio, J.B. Vercosa L. Junior, P.C. Marques de Carvalho, D. von Glehn dos Santos, Innovative hybrid models for forecasting time series applied in wind generation based on the combination of time series models with artificial neural networks. Energy 151, 347–357 (2018)
Maria Kalli, Jim E. Griffin, Bayesian nonparametric vector autoregressive models. J. Econom. 203, 267–282 (2018)
A.K. Kushwah, R. Wadhvani, Performance Monitoring of Wind Turbine Using Advanced Statistical Methods. Sadhana, Springer, vol. 44(163) (2019)
A. Skripnikov, G. Michailidis, Regularized joint estimation of related vector autoregressive models. Comput. Stat. Data Anal. 139, 164–177 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Kumar Kushwah, A., Wadhvani, R., Kushwah, V. (2020). Statistical Time Series Models for Wind Speed Forecasting. In: Nanda, A., Chaurasia, N. (eds) High Performance Vision Intelligence. Studies in Computational Intelligence, vol 913. Springer, Singapore. https://doi.org/10.1007/978-981-15-6844-2_16
Download citation
DOI: https://doi.org/10.1007/978-981-15-6844-2_16
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-6843-5
Online ISBN: 978-981-15-6844-2
eBook Packages: EngineeringEngineering (R0)