R/calcFeDemandBuildings.R

In pik-piam/mrremind: MadRat REMIND Input Data Package

#' Returns the EDGE-Buildings data as REMIND variables
#'
#' @param subtype either "FE", "FE_buildings", or "UE_buildings"
#'
#' @author Robin Hasse
calcFeDemandBuildings <- function(subtype) {

  if (!subtype %in% c("FE", "FE_buildings", "UE_buildings")) {
    stop(paste0("Unsupported subtype: ", subtype))
  }

  # Data Processing ----

  stationary <- readSource("Stationary")
  buildings  <- readSource("EdgeBuildings", subtype = "FE")

  # all 2016 values are zero
  # TODO: remove filtering, as 2016 values are available now
  buildings <- buildings[, 2016, invert = TRUE]

  # aggregate to 5-year averages to suppress volatility
  buildings <- toolAggregateTimeSteps(buildings)
  stationary <- toolAggregateTimeSteps(stationary)

  # add scenarios to stationary to match buildings scenarios by duplication
  duplicateScens <- paste0("SSP2EU_", c("NAV_act", "NAV_ele", "NAV_tec", "NAV_lce", "NAV_all", "CAMP_weak", "CAMP_strong"))
  stationary <- mbind(stationary, do.call(mbind, lapply(duplicateScens, function(to) {
    setItems(stationary[, , "SSP2EU"], 3.1, to)
  })))

  if (subtype == "FE") {

    # drop RCP dimension (use fixed RCP)
    buildings <- mselect(buildings, rcp = "fixed", collapseNames = TRUE)

  } else {

    # rename RCP scenarios in buildings
    rcps <- paste0("rcp", gsub("p", "", getItems(buildings, "rcp")))
    rcps <- gsub("rcpfixed", "none", rcps)
    getItems(buildings, "rcp") <- rcps

    # expand stationary to all RCP scenarios
    stationary <- toolAddDimensions(x = stationary, dimVals = rcps, dimName = "rcp", dimCode = 3.2)

  }

  # extrapolate years missing in buildings, but existing in stationary
  misingYearsBuildings <- setdiff(getYears(stationary), getYears(buildings))
  buildings <- time_interpolate(buildings,
                                interpolated_year = misingYearsBuildings,
                                integrate_interpolated_years = TRUE,
                                extrapolation_type = "constant")

  data <- mbind(stationary, buildings)

  # Prepare Mapping ----

  mapping <- toolGetMapping(type = "sectoral", name = "structuremappingIO_outputs.csv",
                            where = "mrcommons")

  # TODO: remove once this is in the mapping
  # add total buildings electricity demand: feelb = feelcb + feelhpb + feelrhb
  mapping <- rbind(
    mapping,
    mapping %>%
      filter(.data$REMINDitems_out %in% c("feelcb", "feelhpb", "feelrhb")) %>%
      mutate(REMINDitems_out = "feelb")
  )

  mapping <- mapping %>%
    select("EDGEitems", "REMINDitems_out", "weight_Fedemand") %>%
    na.omit() %>%
    filter(.data$EDGEitems %in% getNames(data, dim = "item")) %>%
    distinct()

  if (length(setdiff(getNames(data, dim = "item"), mapping$EDGEitems) > 0)) {
    stop("Not all EDGE items are in the mapping")
  }

  if (subtype == "FE") {

    # REMIND variables in focus: those ending with b and stationary items not in industry focus
    mapping <- mapping %>%
      filter(grepl("b$", .data$REMINDitems_out) |
               (grepl("s$", .data$REMINDitems_out)) & !grepl("fe(..i$|ind)", .data$EDGEitems))
    remindVars <- unique(mapping$REMINDitems_out)
    remindDims <- cartesian(getNames(data, dim = "scenario"), remindVars)

  } else {

    remindVars <- filter(mapping, grepl("^fe..b$|^feel..b$|^feelcb$", .data$REMINDitems_out))
    remindVars <- unique(remindVars$REMINDitems_out)


    # extend mapping for Useful Energy

    if (subtype == "UE_buildings") {
      mapping <- mapping %>%
        mutate(EDGEitems = gsub("_fe$", "_ue", .data[["EDGEitems"]]),
               REMINDitems_out = gsub("^fe", "ue", .data[["REMINDitems_out"]])) %>%
        rbind(mapping)
      remindVars <- gsub("^fe", "ue", remindVars)
    }

    remindDims <- cartesian(getNames(data, dim = "scenario"), rcps, remindVars)
  }

  # Apply Mapping ----

  remind <- new.magpie(cells_and_regions = getItems(data, dim = 1),
                       years = getYears(data),
                       names = remindDims,
                       sets = getSets(data))

  for (v in remindVars) {

    w <- mapping %>%
      filter(.data$REMINDitems_out == v) %>%
      select(-"REMINDitems_out") %>%
      as.magpie()

    tmp <- mselect(data, item = getNames(w)) * w

    tmp <- dimSums(tmp, dim = "item", na.rm = TRUE) %>%
      add_dimension(dim = 3.3, add = "item", nm = v)

    remind[, , getNames(tmp)] <- tmp
  }

  # Prepare Output ----

  # remove missing NAVIGATE scenarios
  if (subtype %in% c("FE_buildings", "UE_buildings")) {
    remind <- remind[, , grep("SSP2EU_(NAV|CAMP)_[a-z]*\\.rcp", getItems(remind, 3), value = TRUE), invert = TRUE]
  }

  # change the scenario names for consistency with REMIND sets
  getNames(remind) <- gsub("^SSP", "gdp_SSP", getNames(remind))
  getNames(remind) <- gsub("SDP", "gdp_SDP", getNames(remind))

  # change item names back from UE to FE
  if (subtype == "UE_buildings") {
    getItems(remind, "item") <- gsub("^ue", "fe", getItems(remind, "item"))
  }

  description <- switch(subtype,
    FE = "final energy demand in buildings and industry (stationary)",
    FE_buildings = "final energy demand in buildings",
    UE_buildings = "useful energy demand in buildings"
  )

  outputStructure <- switch(subtype,
    FE = "^gdp_(SSP[1-5].*|SDP.*)\\.(fe|ue)",
    FE_buildings = "^gdp_(SSP[1-5]|SDP).*\\..*\\.fe.*b$",
    UE_buildings = "^gdp_(SSP[1-5]|SDP).*\\..*\\.fe.*b$"
  )

  return(list(x = remind,
              weight = NULL,
              unit = "EJ",
              description = description,
              structure.data = outputStructure))

}