FORECASTING WIND DIRECTION IN ALOR SETAR USING MACHINE LEARNING TIME SERIES MODELS WITH TRIGONOMETRIC TRANSFORMATION
Abstract
Forecasting wind direction is inherently challenging due to its circular nature, where conventional numerical models often encounter discontinuities at the 0°/360° boundary. This study compares two modelling strategies for daily wind direction prediction in Alor Setar, Malaysia, using data from 2013–2017. A transformation-based approach and a direct numerical approach are compared for forecasting wind direction to assess their differences. In the transformation-based method, wind direction values are converted into sine and cosine components to preserve circularity, with predictions later reconstructed using inverse trigonometric functions. The direct approach predicts wind direction values without transformation. Three models, Prophet, Random Forest, and Holt-Winters, are applied under both strategies. Model performance is evaluated using time series plots, wind rose diagrams, and angular error metrics, including Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). Results indicate that the Random Forest model is the best model for forecasting the wind direction in Alor Setar, and the transformation-based approach produces more accurate and stable predictions, effectively capturing directional continuity, while the direct approach yields higher angular errors and fails to replicate the observed wind direction distribution. To our knowledge, this is one of the first studies in Malaysia to systematically apply transformation-based approaches for wind direction forecasting. The findings highlight the practical importance of improved wind direction prediction for renewable energy optimization, aviation safety, and environmental monitoring.
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D. Alves, F. Mendonça, S. S. Mostafa, and F. Morgado-Dias, “THE POTENTIAL OF MACHINE LEARNING FOR WIND SPEED AND DIRECTION SHORT-TERM FORECASTING: A SYSTEMATIC REVIEW,” Computers, vol. 12, no. 10, p. 206, Oct. 2023. doi: https://doi.org/10.3390/computers12100206
F. M. Noman, G. A. Alkawsi, D. Abbas, A. A. Alkahtani, S. K. Tiong, and J. Ekanayake, “COMPREHENSIVE REVIEW OF WIND ENERGY IN MALAYSIA: PAST, PRESENT, AND FUTURE RESEARCH TRENDS,” IEEE Access, vol. 8, pp. 124526–124543, 2020. doi: https://doi.org/10.1109/ACCESS.2020.3006134
A. Albani, M. Z. Ibrahim, S. S. Abdul Ghani, M. Z. Mat Rofi, and P. N. A. Taslin, “THE IMPACT STUDY OF EL NIÑO-SOUTHERN OSCILLATION TO THE WIND AND SOLAR DATA IN MALAYSIA USING THE WAVELET ANALYSIS,” Front Energy Res, vol. 8, Jan. 2021. doi: https://doi.org/10.3389/fenrg.2020.591469
E. Yatiyana, S. Rajakaruna, and A. Ghosh, “WIND SPEED AND DIRECTION FORECASTING FOR WIND POWER GENERATION USING ARIMA MODEL,” 2017 Australasian Universities Power Engineering Conference, AUPEC 2017, vol. 2017-Novem, pp. 1–6, 2018. doi: https://doi.org/10.1109/AUPEC.2017.8282494
M. Santhosh, C. Venkaiah, and D. M. V. Kumar, “CURRENT ADVANCES AND APPROACHES IN WIND SPEED AND WIND POWER FORECASTING FOR IMPROVED RENEWABLE ENERGY INTEGRATION: A REVIEW,” Jun. 01, 2020, John Wiley and Sons Inc. doi: https://doi.org/10.1002/eng2.12178
Wynnyk, Christopher M. "WIND ANALYSIS IN AVIATION APPLICATIONS." 2012 IEEE/AIAA 31st Digital Avionics Systems Conference (DASC). IEEE, 2012. https://doi.org/10.1109/DASC.2012.6383045
G. Zhang, J. Li, T. Chang, W. Zhang, and L. Song, “AUTONOMOUS NAVIGATION AND CONTROL FOR A SUSTAINABLE VESSEL: A WIND-ASSISTED STRATEGY,” Sustainable Horizons, vol. 13, p. 100117, Mar. 2025. doi: https://doi.org/10.1016/j.horiz.2024.100117
Q. Xu, et al., “A WIND VECTOR DETECTING SYSTEM BASED ON TRIBOELECTRIC AND PHOTOELECTRIC SENSORS FOR SIMULTANEOUSLY MONITORING WIND SPEED AND DIRECTION,” Nano Energy, vol. 89, p. 106382, Nov. 2021. doi: 1 https://doi.org/10.1016/j.nanoen.2021.106382.
Á. López-Oriona, Y. Sun, and R. M. Crujeiras, “FUZZY CLUSTERING OF CIRCULAR TIME SERIES WITH APPLICATIONS TO WIND DATA,” Environmetrics, vol. 36, no. 2, Mar. 2025. doi: https://doi.org/10.1002/env.2902
A. Azhar and H. Hashim, “A REVIEW OF WIND CLUSTERING METHODS BASED ON THE WIND SPEED AND TREND IN MALAYSIA,” Energies (Basel), vol. 16, no. 8, p. 3388, Apr. 2023. doi: https://doi.org/10.3390/en16083388
M. S. Hanoon, et al., “WIND SPEED PREDICTION OVER MALAYSIA USING VARIOUS MACHINE LEARNING MODELS: POTENTIAL RENEWABLE ENERGY SOURCE,” Engineering Applications of Computational Fluid Mechanics, vol. 16, no. 1, pp. 1673–1689, Dec. 2022. doi: https://doi.org/10.1080/19942060.2022.2103588
Y. Han, et al., “A SHORT-TERM WIND SPEED PREDICTION METHOD UTILIZING NOVEL HYBRID DEEP LEARNING ALGORITHMS TO CORRECT NUMERICAL WEATHER FORECASTING,” Appl Energy, vol. 312, p. 118777, Apr. 2022. doi: https://doi.org/10.1016/j.apenergy.2022.118777
F. Sun and T. Jin, “A HYBRID APPROACH TO MULTI-STEP, SHORT-TERM WIND SPEED FORECASTING USING CORRELATED FEATURES,” Renew Energy, vol. 186, pp. 742–754, Mar. 2022. doi: https://doi.org/10.1016/j.renene.2022.01.041
M. Yaghoubirad, N. Azizi, M. Farajollahi, and A. Ahmadi, “DEEP LEARNING-BASED MULTISTEP AHEAD WIND SPEED AND POWER GENERATION FORECASTING USING DIRECT METHOD,” Energy Convers Manag, vol. 281, p. 116760, Apr. 2023. doi: https://doi.org/10.1016/j.enconman.2023.116760
X. Jing-Jing, H. Fei, X. Zi-Niu, and C. Xue-Ling, “BIAS CORRECTION IN WIND DIRECTION FORECASTING USING THE CIRCULAR-CIRCULAR REGRESSION METHOD,” Atmospheric and Oceanic Science Letters, vol. 7, no. 2, pp. 87–91, Jan. 2014. doi: https://doi.org/10.1080/16742834.2014.11447140
E. Erdem and J. Shi, “ARMA BASED APPROACHES FOR FORECASTING THE TUPLE OF WIND SPEED AND DIRECTION,” Appl Energy, vol. 88, no. 4, pp. 1405–1414, 2011. doi: https://doi.org/10.1016/j.apenergy.2010.10.031
N. A. Mokhtar, B. Badyalina, K. L. Chang, F. F. Ya’acob, A. F. Ghazali, and P. Shamala, “ERROR-IN-VARIABLES MODEL OF MALACCA WIND DIRECTION DATA WITH THE VON MISES DISTRIBUTION IN SOUTHWEST MONSOON,” Applied Mathematical Sciences, vol. 15, no. 9, pp. 471–479, 2021. doi: https://doi.org/10.12988/ams.2021.914521
N. D. A. Halim, K. N. A. Maulud, K. C. H. Lun, W. S. W. M. Jaafar, F. A. Mohd, and F. Khan, “AIR POLLUTION PREDICTION BASED ON CHANGES IN MONSOON WIND DIRECTION BY USING TRAJECTORY-GEOSPATIAL APPROACH,” Jurnal Kejuruteraan, vol. 35, no. 2, pp. 303–316, Mar. 2023. doi: https://doi.org/10.17576/jkukm-2023-35(2)-04
N. H. Moslim, N. A. Mokhtar, Y. Z. Zubairi, and A. G. Hussin, “UNDERSTANDING THE BEHAVIOUR OF WIND DIRECTION IN MALAYSIA DURING MONSOON SEASONS USING REPLICATED FUNCTIONAL RELATIONSHIP IN VON MISES DISTRIBUTION,” Sains Malays, vol. 50, no. 7, pp. 2035–2045, Jul. 2021. doi: https://doi.org/10.17576/jsm-2021-5007-18
N. H. Moslim, Y. Z. Zubairi, A. G. Hussin, S. F. Hassan, and N. A. Mokhtar, “UNDERSTANDING THE PATTERN OF WIND DIRECTION IN PENINSULAR MALAYSIA,” 2023, p. 020007. doi: https://doi.org/10.1063/5.0109944.
Blesgen, Thomas. "ON ROTATION DEFORMATION ZONES FOR FINITE-STRAIN COSSERAT PLASTICITY." Acta Mechanica 226.7 (2015): 2421-2434. https://doi.org/10.1007/s00707-015-1326-2
A. Ukil, V. H. Shah, and B. Deck, “FAST COMPUTATION OF ARCTANGENT FUNCTIONS FOR EMBEDDED APPLICATIONS: A COMPARATIVE ANALYSIS,” in Proceedings - ISIE 2011: 2011 IEEE International Symposium on Industrial Electronics, 2011, pp. 1206–1211. doi: https://doi.org/10.1109/ISIE.2011.5984330.
L. Breiman, “RANDOM FORESTS,” 2001. https://doi.org/10.1023/A:1010933404324
M. J. Kane, N. Price, M. Scotch, and P. Rabinowitz, “COMPARISON OF ARIMA AND RANDOM FOREST TIME SERIES MODELS FOR PREDICTION OF AVIAN INFLUENZA H5N1 OUTBREAKS,” BMC Bioinformatics, vol. 15, no. 1, p. 276, Dec. 2014. doi: https://doi.org/10.1186/1471-2105-15-276
R. P. Masini, M. C. Medeiros, and E. F. Mendes, “MACHINE LEARNING ADVANCES FOR TIME SERIES FORECASTING,” Apr. 2021, [Online]. Available: http://arxiv.org/abs/2012.12802
Kalekar, Prajakta S. "TIME SERIES FORECASTING USING HOLT-WINTERS EXPONENTIAL SMOOTHING." Kanwal Rekhi school of information Technology 4329008.13 (2004): 1-13.
N. Nurhamidah, N. Nusyirwan, and A. Faisol, “FORECASTING SEASONAL TIME SERIES DATA USING THE HOLT-WINTERS EXPONENTIAL SMOOTHING METHOD OF ADDITIVE MODELS,” Jurnal Matematika Integratif, vol. 16, no. 2, p. 151, Dec. 2020. doi: https://doi.org/10.24198/jmi.v16.n2.29293.151-157
T. O. Hodson, “ROOT-MEAN-SQUARE ERROR (RMSE) OR MEAN ABSOLUTE ERROR (MAE): WHEN TO USE THEM OR NOT,” Jul. 19, 2022, Copernicus GmbH. doi: https://doi.org/10.5194/gmd-15-5481-2022
T. Chai and R. R. Draxler, “ROOT MEAN SQUARE ERROR (RMSE) OR MEAN ABSOLUTE ERROR (MAE)? -ARGUMENTS AGAINST AVOIDING RMSE IN THE LITERATURE,” Geosci Model Dev, vol. 7, no. 3, pp. 1247–1250, Jun. 2014. doi: https://doi.org/10.5194/gmd-7-1247-2014.
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