New computational workflow based on genetic algorithm for robust multi-objective optimization for the NZEB target.

Net Zero Energy Buildings (NZEBs) play an important role in energy decentralization by using new strategies to save and harvest energy. However, uncertainties concerning stability of energy production from renewables and unpredictable scenarios that may occur during building life frequently cause measured performance of the building-plants system to differ from predictions. For the achievement of NZEB target, it is crucial to investigate, early in the project, the best design alternatives, also considering uncertain circumstances. The scope of this work is to provide a new workflow based on multi-objective optimization that sets the robustness of the building-HVAC system configurations as an optimization Key Performance Indicator, along with financial aspects, also verifying optimal design solutions for NZEB target. An integrated technique that combines open-source coding language Python with dynamic energy simulation engine EnergyPlus is proposed. First obtained results on the attainment of robust energy self-sufficiency of NZEBs favour designs with lower energy demand that use ground source heat pumps GSHP and variable refrigerating flow systems VRF for air conditioning and medium-high size of photovoltaic panels, although with high investment costs.