In this study, the development and testing of a hybrid thermal energy storage (TES) including phase change material (PCM) macro-capsules inside a vertical sensible tank is presented. The storage was specifically developed for delivering hot water on board of ships. Accordingly, a commercial PCM was selected and tested. Subsequently, the hybrid TES was designed and tested under mimicked boundary conditions at lab scale, showing the possibility of increasing the volumetric energy storage density up to 30% compared to the sensible configuration. On this basis, two numerical models were developed: a detailed one, implemented in a Fluent environment, aiming at investigating the main parameters affecting the heat transfer efficiency inside the TES and a second one, implemented in an ESP-r environment to simulate the TES as a component to be implemented inside a more complex system, thus helping its accurate design and operation through a reliable modelling phase. Both models were satisfactorily validated against the experimental results, thus being made available for future investigations and design optimization.

Climate change is becoming a crucial factor to consider within human activities and the building sector is particularly influenced by aspects of internal comfort and energy. In Italy, great attention has been paid to the energy refurbishment of buildings. However, such interventions are mostly focused on reducing heating energy consumption, thus neglecting summer season performance. Moreover, climate change is barely considered during the design phase. This issue is addressed in this work which analyzes some of the most common refurbishment interventions applied to a social housing building in Trieste, while also considering internal comfort during the summer season. A dynamic analysis of the building-plant system is carried out using EnergyPlus. Fanger, UTCI and the adaptive comfort models were used to represent internal health, while three TRY data sets were generated using two GCM–RCM projections to evaluate the influence of climate change. The results show that both building insulation and climatic change affect heating consumption reaching a 70% reduction. However, building insulation does not greatly affect internal comfort, although different models show different behavior to protect against external temperatures. On the contrary, climatic change influences the percentage of hours of discomfort, with a 20% increase for all of the models. The final consideration is that people’s internal health should always be considered when carrying out refurbishment activities.

This paper investigates the influence of a stochastic variation of both energy and economic parameters in an optimization loop applied to a refurbished social housing building. Usually, energy and economic optimization procedures rely on the results of an underlying numerical deterministic model which influences both energy gains and economic figures. However, an analyst must always face the random variation of input and parameter data. The unknown data can represent poor initial information or data that can change in a long time; this is the case of fuel cost and economic indexes in particular. This paper deals with both problems for building refurbishment optimization, the former related to the initial state of a building, and the latter to the energy cost variability. Reliability analysis considers a stochastic variation of parameters looking for solutions that incorporate a risk level; in this case, it deals with optimization objectives related to different impacts on economic, environmental and health aspects. The considered building represents a social house, and the energy reduction measures involve the application of internal insulation layers to the walls and the replacement of existing windows with more efficient ones.

Climate change is affecting more and more local communities, which are now facing different hazards; in answer to this threat, specific actions at the local level should be taken. The Covenant of Mayors (CoM) is an initiative that tries to involve municipalities and communities in developing SECAPs, i.e., plans for sustainable energy and climate with the aim to develop adaptation and mitigation measures. In order to identify and evaluate hazards, the CoM developed a template relative to the current risk level and expected changes in the future. This paper develops a methodology to fill the template using a data driven approach instead of a heuristic one. The methodology was applied to the city of Trieste in northeast Italy and uses local weather station data and projections obtained from GCM-RCM models. Data were manipulated using different approaches for current risk levels and the Mann–Kendall test is proposed as a method to identify the future evolution of hazard intensity and frequency. The results showed that the developed approach could help municipalities in developing their SECAPs and in identifying the present and future evolution of hazards.