Abstract
Increasing energy demands and commitment to sustainable energy development for Malaysia emphasize the need for innovative solutions. This research aims to explore the potential of Ocean Thermal Energy Conversion (OTEC) plant along the Malaysian marine coast using advanced data analytics and machine learning. Data source used in this research is a hybrid ocean coordinate model with a resolution of 1/12° that ensures the oceanic data to be precise. Thorough analysis underscores Malaysia to be geographically feasible for OTEC plants. This feasibility is applicable for any cold-water depth (CWD) deeper than 500 m. Different sites fulfilling thermal gradient and CWD conditions are selected and a 150 MW (Gross) OTEC plant is modeled at the closest onshore area. A Machine Learning (ML) model to predict future temperature trends is trained for estimating the plant's power output over its lifespan. At various CWDs ranging from 500 m to 1000 m with the interval of 100 m, data is used in the ML model and subjected to extensive calculation. The results indicate the 800 m CWD to be the optimal depth for power generation. A different scenario can be seen in the economic analysis as 600 m and 700 m of CWD exhibits the shortest payback period of 14 years.