A Methodology for Fast Simulation of Energy Retrofitting Scenarios of Social Building Stock

Retrofitting the built environment is crucial for the achievement of the global sustainable development targets. Therefore, several measures, strategies, and technologies have been developed to pursue this aim and reduce the energy demand of existing buildings. Within this framework, the EU social housing stock represents a relatively small but critical and peculiar share to handle, as the involved variety of economic, social, and environmental issues makes any intervention tough and tricky. This is particularly true in Italy, where about seventy local agencies, which manage over 1 million publicly owned housing assets, are struggling with a shortage of funds to invest and a lack of adequate knowledge of the conditions of their assets. The research we carried out aims at providing managers of large housing parks with a digital tool useful for rapidly forecasting the effects of different refurbishment scenarios. This should allow planning maintenance interventions effectively, according to the available resources. The main result of the study is an algorithm that powers a predictive diagnostic tool by which the current energy behaviour of buildings can be estimated and the effects of different energy retrofitting measures on their overall performance can be simulated.