Efficient Harbor Craft Monitoring System: Time-Series Data Analytics and Machine Learning Tools to Achieve Fuel Efficiency by Operational Scoring System

Abstract

This paper presents the analysis to reduce carbon emission from tugboat operations by utilizing a proposed unsupervised machine learning operational scoring system. The time-series analysis is performed by transforming data into a common domain for clustering. The data are collected from a tugboat to investigate the correlation between environmental and location data with fuel consumption to achieve fuel efficiency. The relevant parameters that influence the fuel consumption of the tugboat, such as fuel consumption, vessel route, vessel speed and wind metrics are collected from sensors installed onboard the ship and data provider to monitor and to gauge the vessel’s performance. The raw readings are conditioned (data cleaning and data pre-processing) before transformation to Score Dataset: the Raw mass-flowrate readings are cleaned by using the Haar wavelet; the wind raw reading is converted to wind effect data; the Location data is converted to vessel speed data. Together, they form a Score Dataset by applying the time series K-means clustering. The subsequent unsupervised learning identifies the activity labels that describe qualitatively the operations of the vessels and are obtained by using the non-time series K-mean clustering. By using the Hidden Markov Model approach, this paper attempts to explain the stochastic correlation among parameters explained earlier. The correlation is the information of newly discovered knowledge in terms of likelihood matrices, also known as the knowledge base (KB). The KB may be consumed to perform predictions. Hence, it is possible to suggest the optimal ship operation, i.e., speed that produces the optimum fuel consumption. The Score Dataset and clustering that are produced in this paper could also be used in the Artificial Neural Network for future work.