With an increasing awareness of energy consumption and CO 2emission in the population, several initiatives to reduce CO2emissions have been presented all around the world. The main part of these initiatives is a reduction of the energy consumption for existing buildings, while the others concern the building of eco-districts with low-energy infrastructures and even zero-energy infrastructures. In this idea of reducing the energy consumption and of developing new clean areas, this master thesis will deal with the high energy quality services for new urban districts. In the scope of this master thesis project, the new concept of sustainable cities and of clusters of buildings will be approached in order to clearly understand the future challenges that the world’s population is going to face during this century. Indeed, due to the current alarming environmental crisis, the need to reduce human impacts on the environment is growing more and more and is becoming inescapable. We will present a way to react to the current situation and to counteract it thanks to new clean technologies and to new analysis approaches, like the exergy concept. Through this report, we are going to analyze the concepts of sustainable cities and clusters of buildings as systems, and focus on their energy aspects in order to set indoor climate parameters and energy supply parameters to ensure high energy quality services supplies to high performance buildings. Thanks to the approach of the exergy concept, passive and active systems such as nocturnal ventilation or floor heating and cooling systems have been highlighted in order to realize the ‘energy saving’ opportunities that our close environment offers. This work will be summarized in a methodology that will present a way to optimize the energy use of all services aspects in a building and the environmental friendly characteristics of the energy resources mix, which will supply the buildings’ low energy demands.

The spatial variation of wave climate plays a crucial role in erosion, sediment transport, and the design of management actions in coastal areas. Low energy wave systems occur frequently and over a wide range of geographical areas. There is a lack of studies assessing wave model performance in low-energy environments at a regional scale. Therefore, this research aims to model a low energy wave system using a high-resolution dataset. The specific objectives of this study involves 1) using cluster analysis and extensive field measurements to understand the spatial behavior of ocean waves, 2) develop a physics based model of wind-driven waves using high-resolution measurements, and 3) compare machine learning and physics-based models in simulating wave climates. The findings of this study indicate that clustering can effectively assess the spatial variation of the wave climate in a low energy system, with depth identified as the most important influencing factor. Additionally, the physics-based model showed varying performance across different locations within the study area, accurately simulating wave climates in some locations but not in others. Finally, the machine learning model demonstrated overall acceptable performance and accuracy in simulating wave climates and revealed better agreement with observed data in estimating central tendency compared to the physics-based model. The physics-based model performed more favorably for dispersion metrics. These findings contribute to our understanding of coastal dynamics. By providing insights into the spatial behavior of wave climates in low energy systems and comparing the performance of physics-based model and machine learning model, this research contributes to the development of effective coastal management strategies and enhances our understanding of coastal processes.