Very-Short Term Wind Speed Forecasting Via Distance Algorithm in Machine Learning

Document Type : Research Paper


Department of Electrical Engineering, Bahcesehir University, Istanbul, Turkey.


This paper proposes distance matrices, Euclidean, and offset translation methods in machine learning prediction of wind speed. The primary aim for this research is to design forecasting models for very short-term and short-term wind speed prediction based on these two methods by using historical data on wind speed. The test data is collected at a wind power station at 10 minutes intervals. Furthermore, we evaluate the output in different time horizons in comparison to the benchmark method (persistence). To ensure the output results, comparing this method with the persistence method is essential. The proposed method performance was evaluated and compared with the conventional persistence method performance in terms of mean absolute error.


Main Subjects

[1] K. Kiranvishnu, K. Sireesha, J. Ramprabhakar, A Comparative study of wind power forecasting Techniques, March 16, 2016.
[2] Wind could supply Fifth of World Electricity by 2030, October 17, 2016.
[3] Suarabh S.Soman, Hamidreza Zareipour, Om Malik, Paras Mandal, A Review of Wind power and Wind Speed Forecasting Methods With Different Time Horizons , September 26, 2010.
[4] J.Wang,, W.yang, A novel non-linear combination for short -term wind speed forecast, Renew. Energy 143 (2019).
[5] Q.Zhou, wang, G.zhang, A combined forecasting system based on modified multi-objective optimization and submodel selection strategy for shor-term wind speed, Appl. Soft comput. J.94 (2020).
[6] P.giang, Z.Lui, J.Wang, L.Zhang, Decomposition-selection-ensemble forecasting system for energy futures price forecasting based on multi-objective-version of chaos game optimization algorithm(2021).
[7] L.liu, Q.Wang,, M.liu, A rolling gray optimization in economic prediction, comput. Intel. 32 (2016)
[8] P.Zhiang, Z.Liu, J.Wang, L.zhang, Decomposition-selection-ensemble forecasting system for energy futures price forecasting based on muli-objective version of chaos game optimization algorithm, Resour, Policy 73(2021).
[9] P.Jiang, Z.Lui, X.Niu, L.Zhang, A combined forecasting system based on statistical method, artificial neural network, and deep learning methods for short-term wind speed forecasting, Energy(2021).
[10] E.erdem, J.sgi, ARMA based approaches for forecasting the tuple of wind speed and direction, appl. Energy 88 (2011).
[11],, G.zhao, Y.dong, application of residual modification approach in seasonal ARIMA for electricity demand forecasting: a case study of china energy policy(2012).
[12] D.C.Kiplangat, K.Asokan, Ks.kumar, improved week-ahead prediction of wind speed using simple linear model with wavelet decomposition, Renew energy(2016).
[13] R.G. kavasery, K.seetharaman day-ahead wind speed  forecasting
using F-ARIMA models, renew. Energy 43(2009).
[14] N.M, Zhi, C.Q. Yuan, Y.J. Yang, forecasting china energy demand and self-sufficiently by gray forecasting model and Markov model, Int.J.Electr. power enery syst 66 (2015).
[15] Edward Beleke Sekulima, M.Anvar, Wind speed and solar irradiance forecasting techniques for enhanced renewable energy integration with the grid(2016)
[16] H. De and G.Aquah, comparison of Akaike information criterion (AIC) Bayesian information criterion (BIC) in selection of an asymmetric price relationship, jurnal of development & Agricultural economics, Vol.2, No.1, 1-6, 2010.
[17] Dong Lie, Gao Shuang “Chaos characteristic analysis on the time series of wind power generation capacity”, Acta Energiae Solaries Sinica, vol. 28, pp. 1290-1294, Nov. 2007.