State of the art in wind speed in England using BATS ,TBATS , Holt’s Linear and ARIMA model

Wind speed in England using BATS ,TBATS , Holt’s Linear and ARIMA model

Authors

  • Mostafa Abotaleb Department of System Programming, South Ural State University, Chelyabinsk, Russia
  • Tatiana Makarovskikh Department of System Programming, South Ural State University, Chelyabinsk, Russia
  • Aynur Yonar Selçuk University, Faculty of Science, Deparment of Statistics, Konya, Turkey
  • Amr Badr University of New England, Faculty of Science, School of Science and Technology,Australia
  • Pradeep Mishra Assistant Professor,J.N.K.V.V
  • A.J. Williams BTC College of Agriculture & Research Station, IGKV, Sarkanda, Bilaspur, Chhattisgarh, India
  • Harun Yonar Selçuk University, Faculty of Veterinary Medicine,Deparment of Biostatistics, Konya, Turkey

DOI:

https://doi.org/10.54302/mausam.v73i1.598

Keywords:

ARIMA,BATS,TBATS, Foreasting,Wind speed.

Abstract

Wind energy is one of the most important renewable energy sources in the world. Hence, the prediction of wind speed is a highly significant subject with respect to both protecting the environment and economic development. England is among the countries with an increasing interest in the potential for wind energy systems. In this study, various time series models, including BATS, TBATS, Holt’s Linear Trend, and ARIMA models were applied for wind speed prediction in England, and their performance was compared. The available wind speed data between 1994-07-07 and 2015-12-31 were divided into two parts: training data that is used to build up the models and testing data that is used to measure the validity of a model forecast. The results of the testing data indicate that the BATS and ARIMA outperform the other time series models according to the root mean square errors.

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Published

29-03-2022

How to Cite

[1]
M. . Abotaleb, “State of the art in wind speed in England using BATS ,TBATS , Holt’s Linear and ARIMA model: Wind speed in England using BATS ,TBATS , Holt’s Linear and ARIMA model”, MAUSAM, vol. 73, no. 1, pp. 129–138, Mar. 2022.

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Section

Research Papers

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