Algorithm design for wind prediction in Berakit Bay, Bintan Island using Long Short-Term Memory (LSTM) method

Abstract

Abstract Wind speed is a crucial parameter alongside coastal areas, especially Indonesia. Above average wind speed can cause harmful effects on human activities. This study uses wind speed data from Berakit Bay, Bintan Island is a potential location for coastal community settlement, fisheries, and tourist activities. The wind parameter then predicted using the Long Short-Term Memory or LSTM algorithm. This algorithm is able to study long-term dependencies by converting simple nervous system designs into specialized blocks containing cells. It is suitable to be applied to long-term wind predictions where the wind speed at this time is very influential with the wind speed in the future. In preparing the LSTM, the data preprocessing and the architecture used will determine the prediction results. In this study, four different architectures were made in order to determine the most optimal architecture. The results show that the LSTM architecture is able to obtain a relatively good RMSE value of 1.87 and an accuracy of 39.40% with the use of two LSTM layers, 256 units in the first layer and 128 in the second layer. The LSTM algorithm in predicting wind can also be applied to other areas in Indonesia.