Ricerc@Sapienza

Nowadays, as the role of energy retrofit on the existing building stock is recognized towards energy savings and emissions’ reductions, the actions to be undertaken towards this aim require complex decisions, in terms of the choice among active and passive strategies and among often conflicting objectives of the retrofit. Depending on the actor of the retrofit (e.g., private, public), the main objective could be minimizing the investment, minimizing the energy demand or cost, or minimizing emissions. To facilitate the selection of the optimal retrofit actions, here the application of active archive non-dominated sorting genetic algorithm (aNSGA-II) towards multi-objective optimization is illustrated. The results of the algorithm implementation are analyzed with respect to a residential building located in Rome, Italy. The genes (i.e., the implemented strategies) are described and the optimal solution in the R4 space is discussed, alongside with considerations about the solutions pertaining to the Pareto frontier. The applied method allowed to considerably lower the computational time and identifying the multi-objective optimal solution, which was able to reduce by 49.2% annual energy demand, by 48.8% annual energy costs, by 45.2% CO2 emissions while still maintaining almost 60% lower investment cost with respect to other criterion-optimal solutions.