## 3 Algorithmic Errors
### AI in Energy Efficiency  
Improving energy efficiency is among the strategic plans of most shipping companies. The benefits include a reduction in fuel costs and protection of the environment. Already small changes in operating conditions can lead to substantial changes in energy consumption, making it important to continuously optimize all operations onboard a ship. As much as 25% of fuel consumption can be saved by sustainable energy consumption and optimal navigation. Modeling fuel consumption for ships, which has a significant impact on the environment and operational costs, is a crucial challenge. The study shows that machine learning methods, such as Random Forest and Tweedie models, can effectively predict fuel consumption for ships. These findings highlight the potential applications of artificial intelligence and machine learning in maritime environment management and transportation. Advanced data analysis enables decision-makers to more accurately track fuel consumption patterns, improve operational efficiency, and reduce environmental impact, contributing to sustainable development in the maritime sector [15]. There are plenty of potential areas for efficiency improvements in ship navigation, such as economical routing, time spent in harbor and at sea, speed optimization, continuous load output, optimal propeller pitch, engine efficiency, and optimal draught and stability. Such a wide range of variables to take into account means that high performance in vessel navigation can only be achieved if interrelations are well utilized. Taking into consideration the amount of data to be processed, AI algorithms can provide significant improvements in energy efficiency. In recent years, there has been a significant increase in interest regarding ship routing with consideration of weather conditions. Key challenges in this area involve optimizing the route and sailing speed for a given voyage, taking into account the specific marine environmental conditions. Traditional methodologies for solving this problem included the isochrone method, dynamic programming, calculus of variations, pathfinding algorithms, and heuristics. However, the importance of artificial intelligence and machine learning has also been growing in this field [76; 77]. The optimum route and improved efficiency can be achieved through the careful planning and execution of voyages. Thorough voyage planning needs time, but a number of different software tools are available for planning purposes. However, it is important to remember that the data input is performed by the users at specific geographical coordinates, and the information, such as wind direction and strength, swell, and cloud cover, is obtained from visual observation. Much of these data cannot therefore be obtained at night or in restricted visibility, which significantly reduces the reliability of the calculations. Another significant factor to consider in energy management is the operation of the main engine and the propeller. Optimized engine performance may incrementally improve the vessel's energy efficiency. It requires proper maintenance performed at appropriate intervals. When planning a voyage, two speed strategies can be considered--constant power and dual speed. For the constant power strategy, it is important to maintain a consistent ME (main engine) power as much as possible. Conversely, the dual-speed strategy involves operating at low power for one segment of the journey and high power for another segment. Choosing between these two strategies can be challenging in terms of efficiency and optimization. Estimating the average ME power is often complex due to various internal and external factors. These factors include fouling on the propeller and hull, the condition and design limitations of ME operations, sea conditions, wind strength, ocean currents, speed-restricted zones, and the ship's draft and trim [78]. All these elements play a crucial role in determining the optimal route and speed towards the intended port destination.