Knowledge and energy retrofitting of neighborhoods and districts. A comprehensive approach coupling geographical information systems, building simulations and optimization engines

Highlights: • Aim: energy modeling and retrofit optimization of districts/neighborhoods. • Energy consumption and global cost are minimized exploring sundry retrofit levels. • Two opposite perspectives are satisfied: the public one and the private one. • The most sustainable and the cost-optimal solutions are provided. • A part of an existing neighborhood in Naples (Italy) is addressed as case study. Buildings account for about 36% of CO₂ emissions and 40% of energy consumption at EU level. Thus, a deep renovation of the building stock is fundamental to achieve the sustainability targets established worldwide. Accordingly, the study proposes an integrated approach for energy modeling and retrofit planning of districts/neighborhoods of buildings. A Geographic Information System (GIS) tool is coupled with SketchUp® to generate accurate geometrical models, while the thermophysical definition of the building envelopes is performed under DesignBuilder®. Then, EnergyPlus is used as dynamic energy simulator, while MATLAB® is the external post-processing engine. In detail, two phases are identified: 1) development of the energy model; 2) energy retrofit optimization. The approach is relevant to different scale-levels simultaneously, from a single apartment to the whole district/neighborhood as an entity. Furthemore, the use of EnergyPlus ensures high accuracy in energy predictions, while modeling the energy systems directly under MATLAB® environment strongly reduces the computational burden without affecting the results' reliability. As a case study, part of an existing neighborhood located in Naples (Italy) and representative of the Southern Italy building stock is modeled, and its retrofit is optimized. According to the Italian laws, three retrofit levels are investigated to compare their effects on selected energy, financial and environmental indicators. Finally, two optimal solutions are selected: the "nZEB" (nearly Zero-Energy Building) and the "cost-optimal" ones. The former is the one that minimizes the neighborhood's primary energy consumption, which passes from 116.9 kWh/m²a to 72.3 kWh/m²a, while the latter minimizes the global cost, which passes from 184.4 €/m² to 129.6 €/m².