simulate_ams_iid_dataset.RdGenerates tidy monitoring data for industrial facilities implementing energy efficiency and fuel switching measures under AMS-II.D.
simulate_ams_iid_dataset(
n_facilities = 6,
n_periods = 12,
start_year = 2023,
start_month = 1,
baseline_fuel_mean = 1100,
baseline_fuel_sd = 160,
efficiency_improvement = 0.15,
fuel_switch_reduction = 0.12
)Number of industrial facilities to simulate.
Number of monitoring periods per facility (default monthly).
Calendar year for the first monitoring period.
Calendar month (1-12) for the first monitoring period.
Mean baseline fuel consumption per period.
Standard deviation of baseline fuel consumption per period.
Expected fractional increase in thermal efficiency delivered by the project (0-1).
Expected fractional reduction in emission factor due to fuel switching (0-1).
A tibble containing facility IDs, monitoring metadata, baseline and project parameters, and derived leakage placeholders.
simulate_ams_iid_dataset(n_facilities = 3)
#> # A tibble: 36 × 18
#> 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 12 more variables: monitoring_label <chr>, baseline_fuel_quantity <dbl>,
#> # baseline_ncv_gj_per_unit <dbl>, baseline_emission_factor_tco2_per_gj <dbl>,
#> # baseline_efficiency <dbl>, project_fuel_quantity <dbl>,
#> # project_ncv_gj_per_unit <dbl>, project_emission_factor_tco2_per_gj <dbl>,
#> # project_efficiency <dbl>, electricity_emissions_tco2e <dbl>,
#> # useful_heat_output <dbl>, leakage_emissions_tco2e <dbl>