In order to optimising the sea traffic network efficiency, improving the safety of shipping and the protection of the environment, it is useful to model the sea network and its spatio-temporal characteristics of the ship patterns. These maritime patterns could also be an a-priori set of knowledge for the upcoming Maritime Autonomous Surface Ships (MASS) which are starting to navigate our seas with or without remote human controls. The above concepts are crucial and essential elements for defining and understanding the Maritime Situational Awareness (MSA). Nowadays the applied methodologies for modelling the maritime traffic use large scale of database for extracting the patterns. The Knowledge Discovery from Data (KDD), strictly connected with Data Mining (DM) is growing significantly to modelling the behaviour of the vessels in relations to their surroundings. This is just one example that confirms the growing up of the cloud computing usage for maritime applications too. Besides these applications there are also a continuous and fast evolution of the IT services, which more often than not means data centre scale-ups with consequent improve of power consumptions. This paper is a case study based on real world data assessing a multi-objective energy consumption analysis. It is based on the comparison between the traditional air conditioning structures known as Heating, Ventilation and Air Conditioning (HVAC) and the Free Cooling Technique (FCT) in order to reduce the data centre power consumption keeping the same number of computational calculations performed.