Abstract
The wind power forecasting along with the prior knowledge of wind speed has become very important for the efficient functioning of wind power generation and effective management of risk and revenue. Several single approach models are there for forecasting of wind power, i.e., ARIMA, support vector machine (SVM), artificial neural networks (ANN), extreme learning machine (ELM), etc., but hybridization of these models is considered as an effective alternative for forecasting. In the proposed work, the hybridized model combining ARIMA and artificial neural network (ANN) is presented in order to provide a better prediction of wind power. The wind speed data of Denmark is used for evaluation of the proposed model. From the result obtained, it becomes evident that the hybridization of ARIMA and ANN is better in forecasting the wind power as compared to the two models working separately for wind power forecasting.
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
References
Ye, R., Suganthan, P.N., Srikanth, N., Sarkar, S.: A hybrid ARIMA-DENFIS method for wind speed forecasting. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–6 (2013). https://doi.org/10.1109/FUZZ-IEEE.2013.6622503
Soman, S.S., Zareipour, H., Malik, O., Mandal, P.: A review of wind power and wind speed forecasting methods with different time horizons, North American Power Symposium. pp. 1–8 (2010). https://doi.org/10.1109/NAPS.2010.5619586
Radziukynas, V., Klementavičius, A.: Short-term wind speed forecasting with ARIMA model. IEEE. 55th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON). pp. 145–149 (2014). https://doi.org/10.1109/RTUCON.2014.6998223
Hill, D.C., McMillan, D., Bell, K.R.W., Infield, D.: Application of auto-regressive models to U.K. wind speed data for power system impact studies. IEEE Trans. Sustain. Energy 3(1), 134–141 (2012). https://doi.org/10.1109/TSTE.2011.2163324
Eshel, G.: The Yule Walker equations for the AR coefficients. University of South Carolina, 215. Tech. Rep. 1–8 (2010). [Online]. Available: http://www.stat.sc.edu/~vesselin/STAT520_YW.pdf
Eldali, F.A., Hansen, T.M., Suryanarayanan, S., Chong, E.K.: Employing ARIMA models to improve wind power forecasts: a case study in ERCOT, IEEE. North American Power Symposium (NAPS), pp.1–6 (2016). https://doi.org/10.1109/NAPS.2016.7747861
Pai, P.-F., Lin, C.-S.: “A hybrid arima and support vector machines model in stock price forecasting”. Omega 33(6), 497–505 (2005)
Min, Y., Bin, W., Liang-Li, Z., Xi, C.: “Wind speed forecasting based on EEMD and ARIMA”. IEEE, pp. 1299–1302 (2015). https://doi.org/10.1016/j.omega.2004.07.024
Carolin, M., Fernandez, M.E.: “Analysis of wind power generation and prediction using ANN: a case study”. Renew. Energy 33, 986–992 (2008) https://doi.org/10.1016/j.renene.2007.06.013
Catalão, J.P.S., Pousinho, H.M.I., Mendes, V.M.F.: “Short-term wind power forecasting in Portugal by neural networks and wavelet transform”. Renewable Energy 36, 1245–1251 (2011) https://doi.org/10.1016/j.renene.2010.09.016
Catalao, J.P.S., Pousinho, H.M.I., Mendes, V.M.F.: “An artificial neural network approach for short-term wind power forecasting in Portugal”. 15th International Conference on Intelligent System Applications to Power Systems, pp. 1–5 (2009) https://doi.org/10.1109/isap.2009.5352853
Kasabov, N.K., Song, Q.: “DENFIS: dynamic evolving neural-fuzzy inference system and its application for time-series prediction”. IEEE Trans. Fuzzy Syst. 10 (2), 144–154 (2002) https://doi.org/10.1109/91.995117
Kariniotakis, G.N., Stavrakakis, G.S., Nogaret, E.F.: “Wind power forecasting using advanced neural networks models”. IEEE Trans. Energy. Conver. 11(4), 762–767 (1996) https://doi.org/10.1109/60.556376
Cadenas, E., Rivera, W.: “Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA-ANN model”. Renew. Energy 35(12), 2732–2738 (2010) https://doi.org/10.1016/j.renene.2010.04.022
Chang, G.W., Lu, H.J., Hsu, L.Y., Chen, Y.Y.: “A hybrid model for forecasting wind speed and wind power generation”. IEEE Power and Energy Society General Meeting (PESGM), pp. 1–5 (2016) https://doi.org/10.1109/pesgm.2016.7742039
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Singh, P.K., Singh, N., Negi, R. (2019). Wind Power Forecasting Using Hybrid ARIMA-ANN Technique. In: Hu, YC., Tiwari, S., Mishra, K., Trivedi, M. (eds) Ambient Communications and Computer Systems. Advances in Intelligent Systems and Computing, vol 904. Springer, Singapore. https://doi.org/10.1007/978-981-13-5934-7_19
Download citation
DOI: https://doi.org/10.1007/978-981-13-5934-7_19
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-5933-0
Online ISBN: 978-981-13-5934-7
eBook Packages: EngineeringEngineering (R0)