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The yachting industry is embracing sustainable practices in response to global environmental concerns, particularly those outlined in the Paris Agreement, which aim to reduce greenhouse gas emissions and limit global temperature increases. Although diesel engines are most commonly used in yachts and are a major contributor to greenhouse gas emissions, efforts are being made to minimise energy consumption through the use of alternative fuels. Auxiliary systems, such as heating, ventilation and air conditioning (HVAC), are also being studied for efficiency improvements. As yachts spend a considerable amount of time at anchor or in port, these systems account for a significant percentage of the total consumption. This thesis focuses on estimating the energy demand of HVAC systems onboard yachts. In collaboration with De Voogt Naval Architects, part of Feadship, this research uses a grey box model approach, a combination of a white box and a black box, to estimate and evaluate HVAC systems. White box models are based on first principles or physics and are transparent models. This contrasts with black box models, which model a system based solely on observed data, without any prior knowledge of the system. These models are primarily machine learning algorithms such as artificial neural networks. Black box models perform better than white box models within the range of the trained data, but white box models excel at prediction outside this range. The integration of both models can combine the advantages of both into a grey box model. The white box model described in this report is a type of predictive model that combines theoretical knowledge with empirical data to estimate the heat load for HVAC systems on yachts. The model uses theoretical principles derived from various methods, such as an ISO heat load balance, and incorporates empirical data collected from sensors installed on yachts, including temperature and humidity measurements. This data is use, Marine Technology | Ship Design