Diset

The research intends to verify -in some well-known European residential districts characterized by high levels of sustainability -the relationship between urban and building design choices and the outdoor comfort level. Generally the level of comfort outdoors is neglected since the design focuses on the buildings and not on the "empty spaces between the buildings". In this research four well-known European sustainable neighbourhoods have been selected, have been analyzed based on their design characteristics and have been simulated with ENVI-met (a CFD software) in order to verify the level of comfort outdoors -in particular the values of Predicted Mean Vote, relative humidity, air temperature, wind velocity and direction -in winter and summer climate conditions. The results of the research are: 1) verification of the level of comfort outdoors in the selected sustainable European neighbourhoods; 2) understanding the relationship between design choices and level of comfort outdoors; 3) suggestion and development of design modifications to improve the comfort outdoors in one of the selected neighbourhoods; 4) assessment of the proposed design modifications for the improved neighbourhood with further ENVI-met simulations.

Multi-objective optimization tools using genetic algorithms (GAs) are being increasingly used for improving building performances and sustainability. However, few research studies focus on district-scale solutions. In the present project, a multi-objective optimization method using genetic algorithms was applied in order to help decision makers find the optimal energy mix of a district energy system in the preliminary design phase.
A case study consisting of the new campus Albano in Stockholm (comprising lecture buildings and student residences) was used for the analysis. A wide range of energy systems was included as a design variable: wind turbines, solar thermal collectors and photovoltaic cells, ground-source heat pumps, biomass boilers, combined cooling, heating and power, district heating and district cooling. The energy provided by the chosen technologies and the district energy balances are simulated on ait annual basis using a steady-state method with an hourly resolu-tion.
Three objectives functions were to be minimized: (1) the life-cycle costs; (2) the greenhouse gas emissions; and (3) the annual non-renewable primary energy consumption of the district. The optimization process was implemented on MOBO, a multi-objective optimization tool based on genetic algorithms.
The findings include understanding the trade-offs among the three objectives and a selection of alternatives of energy supply systems to be further investigated in the detailed design phase.

Multi-objective optimization tools using genetic algorithms (GAs) are being increasingly used for improving building performances and sustainability. However, few studies focus on district-scale solutions. Here, a multi-objective optimization method using GAs was applied in order to help decision makers find the optimal energy mix of a district energy system in the preliminary design phase.
The new Albano Campus in Stockholm was used as a case-study. Having a wide range of energy systems as design variables, three objectivefunctions were to be minimized: the lifecycle costs; the annual greenhouse gas emissions; and the annual non-renewable primary energy consumption. The district energy balance is calculated using a steady-state method with an hourly resolution. The optimization processwas implemented on MOBO, a multiobjective optimization tool based on GAs. The findings include understanding the trade-offs among the three objectives and a selection of energy supply systems to investigate in the
detailed design phase.