Development of high-precision wind, wave and current forecast system for offshore wind energy industry in Taiwan: a two-stage method of numerical simulation and AI correction

Abstract

At the Changhwa Fuhai wind farm, the high-resolution numerical models for wind, wave and current simulations are crucial to provide daily forecasts as a decision-support system for Taiwan's offshore wind energy industry. In this study, the two-stage method, consisting of numerical simulation and deep-learning correction based on the observed data at Fuhai wind farm site, is implemented to further improve the meteorological and marine forecast accuracy. The recurrent neural network-long short-term memory (RNN-LSTM) model is adopted in the deep learning analytics. Currently, this system is capable of daily providing the next 4-day wind field forecast and the next 7-day wave and current field forecast. Through the testing, it is found that the error correction of wind simulations can reduce root mean square error (RMSE) by up to 36.7%. Furthermore, through adopting the SQL and HBase hybrid database system, a wide range of historical data as well as daily forecast data can be quickly queried and displayed on the web interface to provide important decision support to the relevant wind power companies in scheduling offshore construction plans, forecasting wind energy production, and arranging wind turbines' shutdown and maintenance sequence.