Assessment of Offshore Wind Power Potential and Wind Energy Prediction Using Recurrent Neural Networks

Abstract

In recent years, Taiwan has actively pursued the development of renewable energy, with offshore wind power assessments indicating that 80% of the world's best wind fields are located in the western seas of Taiwan. The aim of this study is to maximize offshore wind power generation and develop a method for predicting offshore wind power, thereby exploring the potential of offshore wind power in Taiwan. The research employs machine learning techniques to establish a wind speed prediction model and formulates a real-time wind power potential assessment method. The study utilizes long short-term memory networks (LSTM), gated recurrent units, and stacked recurrent neural networks with LSTM units as the architecture for the wind speed prediction model. Furthermore, the prediction models are categorized into annual and seasonal patterns based on the seasonal characteristics of the wind. The research evaluates the optimal model by analyzing the results of the two patterns to predict the power generation conditions for 1 to 12 h. The study region includes offshore areas near Hsinchu and Kaohsiung in Taiwan. The novelty of the study lies in the systematic analysis using multiple sets of wind turbines, covering aspects such as wind power potential assessment, wind speed prediction, and fixed and floating wind turbine considerations. The research comprehensively considers the impact of different offshore locations, turbine hub heights, rotor-swept areas, and wind field energy on power generation. Ultimately, based on the research findings, it is recommended to choose the SG 8.0-167 DD wind turbine system for the Hsinchu offshore area and the SG 6.0-154 wind turbine system for the Kaohsiung offshore area, serving as reference cases for wind turbine selection.