PERTANIKA JOURNAL OF TROPICAL AGRICULTURAL SCIENCE

 

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Pertanika Journal of Tropical Agricultural Science, Volume J, Issue J, January J

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  • Ajayi, O. O., Ohijeagbon, O. D., Nwadialo, C. E., & Olasope, O. (2014). New model to estimate daily global solar radiation over Nigeria. Sustainable Energy Technologies and Assessments, 5, 28-36. doi: https://doi.org/10.1016/j.seta.2013.11.001

  • Akcan, S. (2017). Wind speed forecasting using time series analysis methods. Çukurova University Journal of the Faculty of Engineering and Architecture, 32(2), 161-172.

  • Barbosa de Alencar, D., de Mattos Affonso, C., Limão de Oliveira, R. C., Moya Rodriguez, J. L., Leite, J. C., & Reston Filho, J. C. (2017). Different models for forecasting wind power generation: Case study. Energies, 10(12), 1-27. doi: https://doi.org/10.3390/en10121976

  • Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307-327.

  • Chang, G. W., Lu, H. J., Hsu, L. Y., & Chen, Y. Y. (2016, July 17-21). A hybrid model for forecasting wind speed and wind power generation. In 2016 IEEE Power and Energy Society General Meeting (PESGM) (pp. 1-5). Boston, MA, USA. doi: 10.1109/PESGM.2016.7742039

  • De Freitas, N. C., Silva, M. P. D. S., & Sakamoto, M. S. (2018). Wind Speed Forecasting: A Review. International Journal of Engineering Research and Application, 8(1), 4-9. doi: 10.9790/9622-0801010409

  • Engle, R. (2001). GARCH 101: The use of ARCH/GARCH models in applied econometrics. Journal of Economic Perspectives, 15(4), 157-168. doi: 10.1257/jep.15.4.157

  • Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica: Journal of the Econometric Society, 50(4), 987-1007. doi: https://doi.org/10.2307/1912773

  • Erdem, E., Shi, J., & She, Y. (2014). Comparison of Two ARMA-GARCH Approaches for Forecasting the Mean and Volatility of Wind Speed. In International Congress on Energy Efficiency and Energy Related Materials (ENEFM2013) (pp. 65-73). Cham, Switzerland: Springer. doi: https://doi.org/10.1007/978-3-319-05521-3_9

  • Grigonytė, E., & Butkevičiūtė, E. (2016). Short-term wind speed forecasting using ARIMA model. Energetika, 62(1-2), 45-55. doi: https://doi.org/10.6001/energetika.v62i1-2.3313

  • Jamaludin, A. R., Yusof, F., Kane, I. L., & Norrulasikin, S. M. (2016, June). A comparative study between conventional ARMA and Fourier ARMA in modeling and forecasting wind speed data. In AIP Conference Proceedings (Vol. 1750, No. 1, p. 060022). New York, USA: AIP Publishing LLC. doi: https://doi.org/10.1063/1.4954627

  • Kim, E., Ha, J., Jeon, Y., & Lee, S. (2004). Ljung-Box test in unit root AR-ARCH model. Communications for Statistical Applications and Methods, 11(2), 323-327.

  • Kwiatkowski, D., Phillips, P. C., Schmidt, P., & Shin, Y. (1992). Testing the null hypothesis of stationarity against the alternative of a unit root. Journal of Econometrics, 54(1-3), 159-178.

  • Lewis, C. D. (1982). Industrial and business forecasting methods: A practical guide to exponential smoothing and curve fitting. Penang, Malaysia: Heinemann Publisher.

  • Lojowska, A., Kurowicka, D., Papaefthymiou, G., & van der Sluis, L. (2010, June 14-17). Advantages of ARMA-GARCH wind speed time series modeling. In 2010 IEEE 11th International Conference on Probabilistic Methods Applied to Power Systems (pp. 83-88). Singapore. doi: 10.1109/PMAPS.2010.5528979

  • Lujano-Rojas, J. M., Bernal-Agustín, J. L., Dufo-López, R., & Domínguez-Navarro, J. A. (2011). Forecast of hourly average wind speed using ARMA model with discrete probability transformation. In M. Zhu (Ed.), Electrical Engineering and Control (pp. 1003-1010). Heidelberg, Germany: Springer. doi: https://doi.org/10.1007/978-3-642-21765-4_125

  • Masseran, N. (2016). Modeling the fluctuations of wind speed data by considering their mean and volatility effects. Renewable and Sustainable Energy Reviews, 54, 777-784. doi: https://doi.org/10.1016/j.rser.2015.10.071

  • Miswan, N. H., Said, R. M., Hussin, N. H., Hamzah, K., & Ahmad, E. Z. (2015). Comparative performance of ARIMA and DES models in forecasting electricity load demand in Malaysia. International Journal of Electrical and Computer Sciences IJECS-IJENS, 16(1), 6-9.

  • Moreno, J. J. M., Pol, A. P., Abad, A. S., & Blasco, B. C. (2013). Using the R-MAPE index as a resistant measure of forecast accuracy. Psicothema, 25(4), 500-506.

  • Norrulashikin, S. M., Yusof, F., & Kane, I. L. (2018). Meteorological multivariable approximation and prediction with classical VAR-DCC approach. Sains Malaysiana, 47(2), 409-417.

  • Petinrin, J. O., & Shaaban, M. (2015). Renewable energy for continuous energy sustainability in Malaysia. Renewable and Sustainable Energy Reviews, 50, 967-981. doi: https://doi.org/10.1016/j.rser.2015.04.146

  • Radziukynas, V., & Klementavicius, A. (2014, October 14). Short-term wind speed forecasting with ARIMA model. In 2014 55th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON) (pp. 145-149). Riga, Latvia. doi: 10.1109/RTUCON.2014.6998223

  • Sharma, R., & Singh, D. (2018). A review of wind power and wind speed forecasting. Journal of Engineering Research and Application, 8(7), 1-9. doi: 10.9790/9622-0807030109

  • Sharma, S. K., & Ghosh, S. (2016). Short-term wind speed forecasting: Application of linear and non-linear time series models. International Journal of Green Energy, 13(14), 1490-1500. doi: https://doi.org/10.1080/15435075.2016.1212200

  • Sjölander, P. (2011). A stationary unbiased finite sample ARCH-LM test procedure. Applied Economics, 43(8), 1019-1033. doi: https://doi.org/10.1080/00036840802600046

  • Wang, J., Hu, J., Ma, K., & Zhang, Y. (2015). A self-adaptive hybrid approach for wind speed forecasting. Renewable Energy, 78, 374-385. doi: https://doi.org/10.1016/j.renene.2014.12.074

  • Wang, W., Van Gelder, P. H. A. J. M., Vrijling, J. K., & Ma, J. (2005). Testing and modelling autoregressive conditional heteroskedasticity of streamflow processes. Nonlinear Processes in Geophysics, 12(1), 55-66.

  • Yan, J., Guoqing, H., Xinyan, P., & Yongle, L. (2016). Method of short-term wind speed forecasting based on generalized autoregressive conditional heteroscedasticity model. Journal of Southwest Jiaotong University, 51(4), 663-669.

  • Yaziz, S. R., Azizan, N. A., Zakaria, R., & Ahmad, M. H. (2013, December 1-6). The performance of hybrid ARIMA-GARCH modeling in forecasting gold price. In 20th International Congress on Modelling and Simulation (pp. 1201-1207). Adelaide, Australia.

  • Yürekli, K., Kurunç, A., & Öztürk, F. (2005). Testing the residuals of an ARIMA model on the Cekerek Stream Watershed in Turkey. Turkish Journal of Engineering and Environmental Sciences, 29(2), 61-74.

  • Yusof, F., Kane, I. L., & Yusop, Z. (2013). Hybrid of ARIMA-GARCH modeling in rainfall time series. Jurnal Teknologi, 63(2), 27-34.

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