Effects of global warming on energy retrofit planning of neighborhoods under stochastic human behavior

Abstract

Buildings are responsible for around 40% of energy consumption and CO2-eq emissions at the European Union (EU) level, thus, it is widely recognized that the pathway towards sustainability should pass through an important renovation of the building stock. This study proposes a novel approach for planning the energy retrofit of neighborhoods of buildings. The main novelties introduced are: 1) considering the stochastic human behavior that deeply affects energy demand, by setting the usage profiles according to normal distributions; 2) considering the effects of global warming on energy retrofit measures (ERMs), by assuming different Representative Concentration Pathways (RCPs) as boundary conditions. The proposed approach is based on the coupling between EnergyPlus, used as dynamic energy simulator, and MATLAB®, used as postprocessing engine. The year 2035 is considered as the reference year of the analysis, because it is a mid-term time horizon. As case study, an existing neighborhood in Naples (Italy) has been investigated, with the aim to determine the ERMs combination that minimizes primary energy consumption (PEC), running cost (RC) and CO2-eq emissions. Three different RCPs have been considered: RCP 4.5 50% warming, RCP 8.5 50% warming and RCP 8.5 95% warming. For each of them, six different scenarios have been investigated – i.e., neighborhood as built and five retrofit combinations. Results show that for all the RCPs the retrofit combination that includes all ERMs – i.e., measures on the envelopes and on the primary energy systems – produces the highest reductions of PEC, RC and CO2-eq emissions.