# Energy Demand and Supply Simultaneous Optimization to Design a Nearly Zero-Energy House
## 4 Conclusions  
The presented optimization studies were set up to compare the resulting optimal design of a nearly zero-energy house with respect to two different objectives (energy and financial) and considering the differences in adopting an integrated approach (simultaneous optimization of energy demand and supply), rather than a sequential approach that optimized the energy demand first and then the energy supply. It was shown how a building is a complex entity involving strict interdependence between its main subsystems: opaque and transparent envelope, determining energy demand, and the systems and renewable energy sources related to energy supply. In terms of methodology, it was shown that the integrated approach was able to appraise and optimize this interdependence with high levels of robustness. In fact, it was shown that an integrated optimization could support designers in finding a larger set of optimal (or nearly optimal) solutions compared to a sequential optimization. Therefore, the EdESSOpt method, based on simulation-based optimization techniques, allows a greater exploration of the design space and a potential greater ability to reach higher performance while making calculations more robust and manageable. In terms of results, for the energy demand side, high values of insulation thickness are selected in a way that significantly reduces the heating energy demand of the building only in case of energy optimization. In the case of financial optimization, the optimal thickness of insulation is considerably reduced, compared to the configuration of the energy optimum, demonstrating that energy efficiency measures related to the building envelope are still not cost-effective, and measures related to systems are preferred. In fact, considering the energy supply side, it was confirmed that a high-performance system, such as a reversible heat pump, is the optimal generator for both heating and cooling energies for a single-family house in the analyzed Italian context. Combined with the PV system, it allows reduction of energy consumption because of its efficiency and versatility. Integration of solar thermal collectors, to exploit solar energy, is also encouraged, although to a different extent depending on the objective function of the optimization process: if the aim is to minimize energy consumption then the algorithm identifies the maximum eligible number of solar collectors as the optimal solution. Instead, when the focus is on the cost-optimal solution, a lower number of collectors is selected. The same applies to the PV system, as optimal results coming from the global cost optimization indicate a moderate number of PV panels as the best solution. Therefore, it was shown that, in the current Italian market scenario, large installations of solar panels to guarantee a wide coverage of thermal energy needs are still not cost-effective, while the use of PV-generated electricity is slightly more cost-effective. In general, outcomes are quite different depending on whether the objective function is the optimal energy performance or global cost. This confirms that there is still a gap between energy optimum and cost optimum, and new policies should be established to reduce this gap. Furthermore, resilience of the optimal design solutions should be studied considering different scenarios related to occupant behavior, weather conditions, materials, and energy prices. Further work will integrate these aspects in the EDeSSOpt methodology presented here. Conceptualization, M.F. and E.F.; methodology, M.F. ; software, M.F. ; validation, M.F., F.P. and A.R. ; data curation, F.P. and A.R. ; writing--original draft preparation, F.P. and M.F. ; writing--review and editing, M.F. and E.F.; visualization, F.P. and A.R. ; supervision, E.F. This work has been done within the Starting Grant 2016 project funded by the Compagnia di San Paolo. The authors declare no conflict of interest.