## 1 Introduction  
The fluctuation of the oil price and unstable shipping rates have enforced ship builders and shipping companies to improve the energy efficiency of vessels [1]. Since the fuel cost is the largest portion of the operating cost of a vessel, improving energy efficiency can result in huge savings in the total operating cost [2]. Achieving good energy efficiency is also a prerequisite to cope with demanding environmental regulations because more energy-efficient vessels can reduce fuel consumption and greenhouse gas emissions [3]. Therefore, several technological solutions have been proposed to improve the energy efficiency of vessels [4]. Design optimization technology such as \"hull form optimization\" [5] or \"propeller configuration\" [6] try to design vessels to improve the fuel efficiency. On the other hand, operational optimization technologies aim to improve the operational performance by finding optimal speed and optimized voyage routes of vessels [7, 8]. For a more comprehensive list of energy saving technologies, the reader is referred to [PERSON] et al. [4].  
To measure the effectiveness of energy-saving technology, it is necessary to measure the speed/power performance of the vessel [9]. Today, many ship owners leave the responsibility of the delivery trials including speed-power trials with the shipyard [10]. However, the speed/power relationship obtained from a testing environment cannot be generalized to realistic operating conditionsand the propulsion performance of the vessel may be affected by several external factors such as wind, tide, wave or hull fouling [11]. Thus, in a dynamic environment, it is difficult to determine exactly whether the increase in energy efficiency is due to newly adopted energy-saving solutions or other factors.  
As an internationally recognized standard, ISO15016 is proposed to measure the speed/power relationship from the delivery trials [12]. ISO15016 also introduces several calculation procedures to adjust the impact of external factors on the propulsion performance. Unfortunately, those procedures are computationally expensive because many input parameters are required and their related equations are complex [13]. As an alternative measure, the Energy Efficiency Operation Index (EEOI) [14] was proposed to measure the energy efficiency of the vessel. However, EEOI is just an aggregated index that is obtained by the total amount of fuel usage and cannot be used to predict the relationship between the propulsion performance and other related factors.  
To overcome this limitation, this study proposes a data-driven approach to predict the propulsion power by learning data obtained from sensors installed on board the vessel. In this study, support vector regression (SVR) which shows excellent performance in prediction and pattern recognition is used to learn from data. For the illustration, the data obtained from an actual bulk is used to predict its propulsion power at various speeds and dynamic operational conditions. The prediction accuracy was also compared with that of ISO15016. The result shows that if the operational data are obtainable, the proposed model outperforms that of ISO15016.  
The remainder of the paper is as follows. Section 2 briefly introduces the traditional methods that have been used to predict vessel propulsion power. Section 3 explains data that are used to train the prediction model. Section 4 explains the model development procedure in detail. Section 5 discusses the prediction result and compare them with other methods. Finally, Section 6 presents the conclusion and future works.