Simulate a dataset compliant with AMS-II.F

Generates tidy monitoring data for agricultural facilities implementing energy efficiency and fuel switching measures under AMS-II.F. The simulation produces multi-period observations with baseline and project fuel/electricity use, emission factors, production proxies, leakage placeholders, and monitoring metadata. period (MWh). (GJ). under the project (0-1). project (0-1). factors due to fuel switching (0-1). project energy parameters, production proxies, and leakage placeholders.

simulate_ams_iif_dataset(n_facilities = 6,
                                     n_periods = 12,
                                     start_year = 2023,
                                     start_month = 1,
                                     baseline_electricity_mean = 140,
                                     baseline_fuel_mean = 420,
                                     electricity_savings = 0.22,
                                     fuel_savings = 0.38,
                                     fuel_switch_reduction = 0.35)

Arguments

n_facilities

Number of agricultural facilities to simulate.

n_periods

Number of monitoring periods per facility (default monthly).

start_year

Calendar year for the first monitoring period.

start_month

Calendar month (1-12) for the first monitoring period.

baseline_electricity_mean

Mean baseline electricity consumption per

baseline_fuel_mean

Mean baseline thermal fuel energy demand per period

electricity_savings

Expected fractional reduction in electricity demand

fuel_savings

Expected fractional reduction in fuel demand under the

fuel_switch_reduction

Expected fractional reduction in thermal emission

Value

A tibble containing facility IDs, monitoring metadata, baseline and

Examples

simulate_ams_iif_dataset(n_facilities = 3)
#> # A tibble: 36 × 20
#>    facility_id monitoring_period  year month   day monitoring_date
#>    <chr>                   <int> <dbl> <dbl> <int> <date>         
#>  1 facility_01                 1  2023     1    13 2023-01-13     
#>  2 facility_01                 2  2023     2    23 2023-02-23     
#>  3 facility_01                 3  2023     3    12 2023-03-12     
#>  4 facility_01                 4  2023     4     5 2023-04-05     
#>  5 facility_01                 5  2023     5     6 2023-05-06     
#>  6 facility_01                 6  2023     6    15 2023-06-15     
#>  7 facility_01                 7  2023     7    15 2023-07-15     
#>  8 facility_01                 8  2023     8     4 2023-08-04     
#>  9 facility_01                 9  2023     9     6 2023-09-06     
#> 10 facility_01                10  2023    10     9 2023-10-09     
#> # ℹ 26 more rows
#> # ℹ 14 more variables: monitoring_label <chr>, baseline_electricity_mwh <dbl>,
#> #   baseline_fuel_energy_gj <dbl>,
#> #   baseline_electricity_emission_factor_tco2_per_mwh <dbl>,
#> #   baseline_fuel_emission_factor_tco2_per_gj <dbl>,
#> #   baseline_total_energy_mwh <dbl>, project_electricity_mwh <dbl>,
#> #   project_fuel_energy_gj <dbl>, …