Wind Speed Forecasting Using Long Short-Term Memory (LSTM) and Meteorological Variables at Tanjung Perak, Surabaya, Indonesia
DOI:
https://doi.org/10.35895/jpsi.2.2.72-86.2026Keywords:
Wind speed forecasting, Long Short-Term Memory (LSTM), Multivariate time series, Meteorological predictors, Maritime meteorologyAbstract
This study evaluated Long Short-Term Memory (LSTM) models for wind-speed prediction using meteorological observations from Tanjung Perak, Surabaya, for 2015–2024. Air temperature, relative humidity, atmospheric pressure, and rainfall were used as predictors. Data preprocessing, alternative training–test partitions (70:30, 80:20, and 90:10), and model configurations were evaluated using root mean square error (RMSE) and mean absolute error (MAE). Among the configurations reported for the 80:20 partition, 50 LSTM units and 50 epochs yielded the lowest test errors (RMSE = 1.2965 knots; MAE = 1.0175 knots). For January 2025, reported forecast errors were RMSE = 1.4253 and MAE = 1.2261 knots for 7 days; RMSE = 1.3178 and MAE = 1.0943 knots for 14 days; and RMSE = 1.2117 and MAE = 0.8886 knots for 31 days. These results refer to different verification periods and do not establish superior performance at longer forecast horizons. Multiple linear regression identified significant associations with temperature, humidity, and pressure (p < 0.001), while rainfall was not significant (p = 0.164). Further chronological and operational validation is needed.
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Copyright (c) 2026 Yesiska M Siallagan, Tugiyo Aminoto, Husnul Hamdi (Author)
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