AMS-II.Q applicability helpers

Helpers that evaluate the AMS-II.Q applicability conditions for commercial building projects.

check_applicability_service_scope_iiq(
  baseline_summary,
  project_summary,
  service_col = "service",
  baseline_units_col = "baseline_units",
  project_units_col = "project_units"
)

check_applicability_monitoring_iiq(
  monitoring_data,
  required_cols = c(
    "baseline_energy_use_mwh",
    "baseline_emission_factor_tco2_per_mwh",
    "project_energy_use_mwh",
    "project_emission_factor_tco2_per_mwh"
  )
)

check_applicability_efficiency_gain_iiq(
  baseline_data,
  project_data,
  group_cols = NULL,
  minimum_improvement = 0.05,
  baseline_energy_col = "baseline_energy_use_mwh",
  baseline_service_col = "baseline_service_output_mwh",
  project_energy_col = "project_energy_use_mwh",
  project_service_col = "project_service_output_mwh"
)

Arguments

baseline_summary

Tibble summarising baseline services with unit counts.

project_summary

Tibble summarising project services with unit counts.

service_col

Column storing the service identifier.

baseline_units_col

Column storing baseline unit counts.

project_units_col

Column storing project unit counts.

monitoring_data

Tibble containing the combined monitoring dataset.

required_cols

Character vector of required monitoring columns.

baseline_data

Tibble containing baseline monitoring observations.

project_data

Tibble containing project monitoring observations.

group_cols

Optional character vector of grouping columns shared across datasets.

minimum_improvement

Minimum fractional improvement in energy intensity.

baseline_energy_col

Column storing baseline energy use.

baseline_service_col

Column storing baseline service output.

project_energy_col

Column storing project energy use.

project_service_col

Column storing project service output.

Value

Logical values indicating whether each applicability condition is met.

Examples

baseline <- tibble::tibble(service = c("cooling", "lighting"), baseline_units = c(4, 200))
project <- tibble::tibble(service = c("cooling", "lighting"), project_units = c(2, 200))
check_applicability_service_scope_iiq(baseline, project)
#> [1] TRUE

monitoring <- tibble::tibble(
  baseline_energy_use_mwh = c(400, 380),
  baseline_emission_factor_tco2_per_mwh = c(0.62, 0.62),
  project_energy_use_mwh = c(300, 280),
  project_emission_factor_tco2_per_mwh = c(0.58, 0.58)
)
check_applicability_monitoring_iiq(monitoring)
#> [1] TRUE

baseline_data <- tibble::tibble(
  building_id = c("A", "B"),
  baseline_energy_use_mwh = c(800, 600),
  baseline_service_output_mwh = c(720, 540)
)
project_data <- tibble::tibble(
  building_id = c("A", "B"),
  project_energy_use_mwh = c(520, 420),
  project_service_output_mwh = c(720, 540)
)
check_applicability_efficiency_gain_iiq(baseline_data, project_data, group_cols = "building_id", minimum_improvement = 0.05)
#> [1] TRUE