Interpreting results#

App.result() returns a plain Python dict, JSON-serializable, with no pandas or numpy types. The same dict is written by the CLI’s --output flag.

Top-level keys#

Key	Description
`n_modules`	Number of PV modules used in the simulation
`pv_kwp`	System DC nameplate capacity (kWp)
`battery_kwh`	Battery capacity (kWh)
`pv_production_kwh`	Year 1 PV production
`consumption_kwh`	Year 1 load
`self_consumption_kwh`	Year 1 PV directly self-consumed
`grid_import_kwh`	Year 1 energy bought from the grid
`grid_export_kwh`	Year 1 energy sold to the grid
`grid_independence_pct`	Year 1 grid independence ratio
`self_consumption_pct`	Year 1 self-consumption ratio
`total_investment_eur`	Total CAPEX
`payback_year`	First year with positive cumulative NPV (`None` if not reached)
`npv_savings_eur`	Cumulative NPV savings over the projection horizon
`lcoe_eur_kwh`	Levelized cost of electricity from system CAPEX, O&M, simulated replacements, and discounted PV production
`monthly`	Year 1 monthly energy balance rows
`financial`	Yearly financial projection rows (year 0 = investment)
`yearly`	Per-year breakdown of production, load, imports, exports

Battery-specific keys#

Present only when battery_kwh > 0:

Key	Description
`battery_soh_end_pct`	State of health at the end of the projection horizon
`battery_replacements`	Total number of replacements over the projection
`battery_replacement_cost_eur`	Total replacement cost

Emissions keys#

Present only when emissions_country is set:

Key	Description
`co2_avoided_year1_kg`	Year 1 CO2 savings
`co2_avoided_total_kg`	Lifetime CO2 savings over the projection horizon

Multi-array systems#

When pv_arrays is set, the result also contains a pv_arrays list echoing each array’s configuration (modules, module, tilt, azimuth).

Monthly and yearly breakdowns#

`monthly`#

A list of 12 dicts, one per month of year 1:

{
    "month": "Jan",
    "pv_kwh": 245.3,
    "consumption_kwh": 412.5,
    "self_consumption_kwh": 180.2,
    "import_kwh": 232.3,
    "export_kwh": 65.1,
    "grid_independence_pct": 43.7,
}

`yearly`#

A list of one dict per simulation year (length projection_years). Each row contains the same fields as monthly aggregated to a year, plus soh_pct when a battery is present.

`financial`#

A list of dicts with one row per year (year 0 is the investment row):

{"year": 0, "balance": -8500.0, "reference": 0.0}
{"year": 1, "balance": -7950.4, "reference": 0.0, "cost_with_system": 542.1, "cost_without_system": 1092.5}
# ...

balance is the cumulative NPV savings; cost_with_system and cost_without_system are the cumulative discounted costs of operating with and without the BREOS-sized system. The crossover point of balance ≥ 0 defines payback_year.