Leaveraging Digital Twin & Artificial Intelligence in Consumption Forecasting System for Sustainable Luxury Yacht

被引:1
作者
Dini, Pierpaolo [1 ]
Paolini, Davide [1 ]
Minossi, Maurizio [2 ]
Saponara, Sergio [1 ]
机构
[1] Univ Pisa, Dept Informat Engn, I-56122 Pisa, Italy
[2] Videoworks Spa, I-55049 Viareggio, LU, Italy
来源
IEEE ACCESS | 2024年 / 12卷
关键词
Boats; Digital twins; Artificial intelligence; Data models; Long short term memory; Real-time systems; Atmospheric modeling; Energy consumption; Predictive models; Time series analysis; Sustainable development; Marine vehicles; long-short-term memory; digital twin; model-based design; fast simulation; time-series forecasting; Hotellerie; luxury yacht; MODEL-PREDICTIVE CONTROL; FRAMEWORK;
D O I
10.1109/ACCESS.2024.3471624
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The luxury yacht market is increasingly driven by a demand for sustainability, emphasizing the need for enhanced monitoring and optimization of energy consumption, particularly in the yacht's hotel sector. To address this demand, we propose a novel system that leverages artificial intelligence (AI) and digital twin (DT) technologies. This system integrates various network-connected sensors, smart plugs, and actuators, such as HVAC systems, lighting, and automated blinds, and is orchestrated by an advanced AI algorithm. Given the challenge of limited experimental data on energy consumption and passenger habits, our approach involves the development of a modular digital twin of the yacht. This DT enables the simulation of different configurations and operational scenarios, generating substantial virtual data for training AI models. Specifically, we employ a Long-Short-Term Memory (LSTM) neural network for time series analysis, which predicts future consumption based on current usage patterns and passenger behavior. This methodology offers a general framework applicable to various yacht designs, enhancing sustainability and operational efficiency across the industry.
引用
收藏
页码:160700 / 160714
页数:15
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