A novel framework for wave power plant site selection and wave forecasting based on GIS, MCDM, and ANN methods: A case study in Hainan Island, Southern China

Abstract

Wave power plants (WPPs) hold significant promise for sustainable energy generation but face complex challenges in site selection and wave forecasting. To address these challenges, this study presents an integrated methodology that combines geographic information system (GIS), multi-criteria decision making (MCDM) and artificial neural network (ANN) techniques. The primary objective of this research is to identify optimal sites for WPP deployment and enhance the accuracy of wave power density (WPD) forecasting. The study focuses on Hainan Island, the second-largest island in China, and employs both macro-scale and micro-scale site selection methods. It establishes a comprehensive criteria system that encompasses environmental, technical, economic, and social aspects. To improve the objectivity of criteria weighting, a group decision making-analytic hierarchy process (GDM-AHP) approach is applied. The findings reveal that approximately 1.36 % (1427.93 km2) of Hainan Island's area offers suitable conditions for WPP deployment, with concentrations in the southern, northern, and eastern marine regions. Among the 27 identified alternative sites covering 208.76 km2 along the eastern coast, six optimal sites are pinpointed in Wanning, Wenchang, and Qionghai. Furthermore, the optimized ANN outperforms the standard ANN, achieving a mean relative error of 9.0 %. This research contributes to the theoretical understanding of WPP site selection and wave forecasting while offering a practical and adaptable framework for stakeholders involved in renewable energy projects, particularly in coastal areas such as Hainan Island. These results and methodologies have real-world implications for the efficient and effective deployment of wave energy projects, ultimately promoting sustainable energy generation and socioeconomic development.