## 2 Method Applied to BIPV Project Evaluations
### Energy Balance  
In this project, the energy balance was determined by comparing the energy production with energy consumption at different timescales and by calculating different indicators such as self-consumption ratio (SCR) and self-sufficiency ratio (SSR). Equations (3)-(6) show how the energy balance for each building was determined:  
\[\text{E}_{\text{flow}}=\text{E}_{\text{consumption}}\text{-E}_{\text{BIPV}} \tag{3}\]  
\[\text{E}_{\text{BIPV}}\text{=E}_{\text{self-consumption}}\text{+E}_{\text{ feedin}} \tag{4}\]  
\[\text{SCR}=\text{E}_{\text{self-consumption}}\text{/E}_{\text{BIPV}} \tag{5}\]  
\[\text{SSR}=\text{E}_{\text{BIPV}}\text{/E}_{\text{consumption}} \tag{6}\]  
where \(\text{E}_{\text{flow}}\) is the energy flow including all imports and exports to the grid (net-meter, in kWh), and \(\text{E}_{\text{self-consumption}}\) is the BIPV energy production consumed by the household (kWh). \(\text{E}_{\text{feedin}}\) is the energy that could not be consumed and was fed into the grid (kWh).  
\(\text{E}_{\text{consumption}}\) data of the building were either recorded data (ex: energy provider's public data [53]) or estimated with similar consumption profiles. In the latter case, the similar profile found in the time series was scaled proportionally to the building studied according to annual/monthly consumption, found in government websites (e.g., OpenEI of the US Department of Energy [54]). \(\text{E}_{\text{consumption}}\) varied at an hourly to 15 min resolution. This allowed for greater accuracy regarding the self-consumption, and therefore provided better precision of the energy neutrality code of a building. Furthermore, this helped to tailor the location and the nominal power of the PV modules to increase the self-consumption.  
The solar irradiance on the outside geometry of a building was modelled at an hourly basis over the year for the specific location by using meteorological data and BIMsolar. The Sketchup(r) Warehouse 3D building model was the basis of a building's geometry; additional modifications were added if required, notably for existing shading objects in surroundings. Once the satellite-based typical meteorological year (TMY3) data were imputed [9], the yearly average solar yield could be assessed, therefore allowing the mapping of suitable locations to install PV on the geometry itself. Depending on the local climate, certain PV technologies were chosen. For BIPV options, some specific cell technologies, including glazing, transparent, or opaque modules over the 3D geometry, were selected, and BIMsolar was used to estimate the energy production for every hour in a year based on TMY3. The choice of PV or inverters proposed from the photon database could be even further extended by creating their own cells, modules, and inverters, etc. Once the software indicated the wiring and PV modules, the estimated solar energy at an hourly resolution could be given to include losses, as in Equation (7):  
\[\text{E}_{\text{BIPV}}=(\text{E}_{\text{PVproduction}}-\text{E}_{\text{losses}}) \text{.}\text{.}\text{n}_{\text{eff}} \tag{7}\]  
where \(\text{E}_{\text{PVproduction}}\) is the production obtained with maximum power point tracking in ideal conditions without losses. \(\text{E}_{\text{losses}}\) integrates heat, shading, mismatch, and wiring losses up to the inverter. We applied the EU efficiency (\(\text{n}_{\text{eff}}\)) in order to get the final results of the energy yield \(\text{E}_{\text{BIPV}}\). These results were matched with energy load profiles.