• AI
• Chiller
• Digital Twin model
• Energy consumption
• Forecasting
• LSTM
• Navigation
• PID
• Sustainability
• Yacht

In recent years, the intersection of artificial intelligence (AI), green energy, and luxury yacht industries has emerged as a focal point for innovation and advancement. This study delves into the development of a digital twin model fortified with AI logic and neural networks, aiming to enhance energy efficiency within the luxury yacht sector. The impetus behind this research stems from a dual recognition of market trends and environmental imperatives. As industries pivot towards AI-driven solutions and sustainable energy practices, the luxury yacht sector stands at the confluence of these transformative forces. Amidst a growing emphasis on eco-consciousness and operational excellence, the imperative to optimize energy utilization has become paramount. Against this backdrop, the deployment of digital twin technology, augmented with AI algorithms and neural networks, emerges as a strategic approach to achieving enhanced energy efficiency. By creating a virtual replica of physical assets and integrating real-time data streams, this model offers unprecedented insights into energy consumption patterns and optimization strategies. Furthermore, this research underscores the critical need for innovation within the luxury yacht industry to remain competitive and aligned with evolving market dynamics. Through the synthesis of AI methodologies and green energy principles, this thesis endeavors to contribute to the ongoing discourse on sustainable practices within the luxury yacht domain. By leveraging digital twin simulations and neural network algorithms, it seeks to delineate pathways towards greater energy efficiency, thereby addressing both market imperatives and environmental concerns. In order to carry out this analysis, we collaborated with the company Videoworks which, thanks to their experience and historical data and information concerning the components that form and make up the yacht, have allowed us to create this first prototype of a digital twin model with AI logic for energy efficiency.