DeepSea: Leveraging AI and Weather Data for Optimal Maritime Fuel Consumption
Technology Category
- Analytics & Modeling - Predictive Analytics
Applicable Industries
- Marine & Shipping
- Renewable Energy
Use Cases
- Vehicle Performance Monitoring
About The Customer
DeepSea is a leading AI-powered maritime entity that specializes in voyage optimization. They leverage advanced technologies to develop vessel-specific performance models that can predict the impact of various nautical phenomena on a ship's performance. Their goal is to optimize maritime operations, particularly in terms of fuel consumption, by accurately predicting the effects of factors such as wind, waves, and currents. To achieve this, they require highly accurate and detailed ocean weather data, which they use to enhance their predictive models and algorithms.
The Challenge
DeepSea, a leading AI-powered maritime entity, was faced with the challenge of enhancing its vessel-specific performance models with a highly accurate and detailed source of ocean weather data. The goal was to predict the impact of wind, waves, currents, and other nautical phenomena on fuel consumption. However, the task was not without its difficulties. A significant 80% of the world's oceans remain unmapped and unobserved, presenting a considerable gap in the data required for accurate predictions. Additionally, the data on ocean wind and wave conditions that is available is often inconsistent. This is because it is gathered from instruments deployed on buoys and traveling ships, leading to discrepancies in wind-weather observations.
The Solution
To overcome these challenges, DeepSea partnered with Spire Global. By integrating Spire's weather data into its predictive voyage planning algorithm, DeepSea was able to gain global coverage across the open oceans. This allowed for a more comprehensive and accurate understanding of ocean conditions, thereby enhancing the predictive capabilities of their performance models. DeepSea then analyzed the relationship between ship speed and the increase in power demand (and consequently, fuel consumption) under moderate and heavier wind conditions. This analysis enabled them to better predict and optimize fuel consumption based on varying wind conditions.
Operational Impact
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