AN INSULATION THICKNESS OPTIMIZATION METHODOLOGY FOR SCHOOL BUILDINGS REHABILITATION COMBINING ARTIFICIAL NEURAL NETWORKS AND LIFE CYCLE COST

摘要

The energy efficiency of buildings, including public buildings, is a major concern for all European governments, since they are responsible for a large share of the total energy bill of the states. School buildings play an important role in these costs. The best strategy for reversing this scenario includes efforts on buildings retrofit, seeking to optimize their energy efficiency and indoor environmental quality. However, in the unfavourable economic climate we are experiencing, which requires great prudence when it comes to public investment, special attention should be given to this multi-objective optimization process. In this research, a methodology to optimize the insulation thickness of the external walls and roof on school buildings retrofit is proposed. The procedure includes the optimization of the building performance considering the following objectives: the minimization of the annual heating load; the minimization of the discomfort in the classrooms due to overheating; and the minimization of the life cycle cost of retrofitting external walls and roof. This methodology was applied to two Portuguese school buildings.