Feature fusion with transformer-LSTM-A and NSGA-III for ship energy efficiency optimization in green shipping

Abstract

Green and energy-efficient maritime transport has become a strategic imperative under tightening decarbonization mandates by the International Maritime Organization (IMO). However, current ship energy efficiency optimization (EEO) frameworks often decouple fuel consumption prediction from operational decision-making, limiting real-time adaptability and integrated control. To address this gap, this study proposes a high-resolution collaborative framework that couples a Transformer-LSTM-A prediction model with a voyage-segmented NSGA-III multi-objective optimizer. An NSGA-III solves the four-objective, segment-level speed-and-trim optimization subject to practical stability and operating limits. The proposed architecture incorporates temporal attention mechanisms and VMD-enhanced multivariate inputs to accurately forecast fuel consumption rates, which then guide the segment-wise optimization of ship speed and trim. The optimization simultaneously minimizes fuel consumption, CO2 emissions, and the Energy Efficiency Operational Indicator (EEOI), while embedding soft constraints on voyage distance to preserve navigational feasibility. A real-world case study demonstrates the effectiveness of the proposed approach, achieving reductions of 4.76% in FCR, 3.04% in CO2 emissions, and 1.50% in EEOI. These results validate the framework’s potential for intelligent maritime energy management, offering a robust and scalable pathway toward low-carbon ship operations aligned with global regulatory targets.