Package 'mredgebuildings'

Description

Prepare data to be used by the EDGE-Buildings model.

Author(s)

Maintainer: Robin Hasse [email protected] (ORCID)

Authors:

• Pascal Führlich [email protected]

• Antoine Levesque

• Hagen Tockhorn

See Also

Useful links:

• https://github.com/pik-piam/mredgebuildings

Description

Disaggregate regional energy demand per carrier by end use while meeting the end use shares in the aggregated region.

Usage

.disaggregate(subset, key)

Arguments

Details

The function first tries to find a solution that satisfies both the regional carrier and the overall end use constraints. If there is no solution, another optimisation is run that tries to also minimise deviations from the end use quantities but removes them from the constraints.

Description

Aggregate cellular HDD/CDD values to country-wide average (population-weighted)

Usage

aggCells(data, weight, mask)

Arguments

Value

data frame containing regionally meaned HDD/CDD values

Description

To adress loss of buildings' internal memory of previous conditions at high outside temperatures due to window opening, etc., BAIT and outside temperature are blended. The blend is active between a lower and upper temperature threshold, bLower and bUpper, which are mapped to a range of -5 to +5 of a sigmoid function (corresponding to a 1% and 99% blend). The maximum amount of blending (i.e. the amplitude of the sigmoid function) is given by a parameter bMax.

Usage

blend(bait, tas, weight)

Arguments

Value

blended raster data

Description

linear regression on historic data to determine regression parameters for surface downdwelling shortwave radiation (rsds), near-surface wind speed (sfcwind) and near-surface specific humidity (huss) with respect to near-surface air temperature (tas). The regression is done with on a simple linear model, where the historical input data covers the years of 2000-2014. For rsds and sfcwind, a simple linear relationship is assumed where for huss an exponential relation is assumed, buildig upon the non-linear relation between water vapor pressure and temperature.

Usage

calcBAITpars(model = "GFDL-ESM4")

Arguments

Value

terra SpatRaster covering one regression parameter per layer per cell

Author(s)

Hagen Tockhorn

Description

Join data from various sources to construct a consistent European building stock with a focus on floor space (million m2).

Usage

calcBuildingStock(subtype = c("residential", "commercial"))

Arguments

Value

MAgPIE object with historic building stock

Note

if the distribution of building types is missing, it is filled with the Europe-wide distribution. It might be worthwhile to map filling countries instead.

Author(s)

Robin Hasse

Description

Final energy carrier prices including energy supply cost, transport and distribution cost(T&D) and taxes (without VAT). CO2 price component and VAT are added later depending on the scenario assumptions.

Usage

calcCarrierPrices()

Author(s)

Robin Hasse

Description

floor-space specific construction cost

Usage

calcCostConstruction()

Author(s)

Robin Hasse

Description

Demolition cost

Usage

calcCostDemolition()

Author(s)

Robin Hasse

Description

floor-space specific renovation cost

Usage

calcCostRenovation(energyLadder = FALSE)

Arguments

Value

MagPIE object with floor-space specific renovation cost depending on the initial and final state of the building

Author(s)

Robin Hasse

Description

Get Population Density

Usage

calcDensity()

Value

magpie object

Author(s)

Hagen Tockhorn

Description

Calculate the regression parameters for the projection of final (FE) to useful (UE) energy conversion projection for all combinations of energy enduses and carriers. The regression parameters correspond to an asymptotic regression model and encompass the parameters Asym, R0 and lrc. The are determined using a nonlinear least-squares regression.

Usage

calcEfficiencyRegression()

Details

This approach closely follows the model by De Stercke et al. which is mainly driven by GDP per Capita.

Author(s)

Hagen Tockhorn

References

De Stercke, S. (2014). Dynamics of Energy Systems: A Useful Perspective (Interim Report, p. 68). IIASA. http://pure.iiasa.ac.at/id/eprint/11254/1/IR-14-013.pdf

Description

Takes the historic final energy demand by carriers from IEA and disaggregates it into different end uses.

Usage

calcFEbyEUEC()

Details

The disaggregation is performed such that the aggregated carrier-specific FE values of the IEA IO regions are met as a minimum requirement. In an ideal case, the enduse-specific FE shares are met as well.

Value

data.frame with historic energy demands

Note

For now, existing disaggregated final energy data with respect to carriers and enduses combined is replaced in the final output. However, since the Odyssee data is largely underestimating the real IEA FE targets, these shall serve as lower boundaries for the disaggregation. Such feature has been implemented in toolDisaggregate but is not running smoothly yet.

Author(s)

Hagen Tockhorn, Robin Hasse

Description

Simple estimate of total FE demand in differet building types

Usage

calcFEdemandBuildings()

Details

Schould only be used as weights not actual demand. The disaggregation to building types is too simplistic.

Author(s)

Robin Hasse

Description

Historic Final Energy (FE) Demand and FE-EU-Efficiencies are taken to compute Useful Energy (UE) Demand.

Usage

calcFEUE()

Value

data.frame with final and useful energy demand

Author(s)

Hagen Tockhorn

Description

Calculate Efficiencies of Final (FE) to Useful (UE) Energy Conversion for all combinations of Energy Carriers and Enduses. The efficiency projections are based on a model by De Stercke et al. which is mainly driven by GDP per Capita. It describes an S-shaped curve approaching assumed efficiency levels. The parameters of that curve are derived by a regression with observations of IEA data.

Usage

calcFEUEefficiencies(gasBioEquality = TRUE)

Arguments

Author(s)

Hagen Tockhorn

References

De Stercke, S. (2014). Dynamics of Energy Systems: A Useful Perspective (Interim Report, p. 68). IIASA. http://pure.iiasa.ac.at/id/eprint/11254/1/IR-14-013.pdf

Description

Data for many European countries is taken from EEA, India from IEA and other countries from Daioglou et al 2012. The result does not cover all countries and has mixed points in time depending on the region.

Usage

calcFloorspacePast()

Value

MAgPIE object with historic floor space

Note

RK: In Antoine's EDGE-B, data points associated with an GDP/POP above 70000 USD/cap are dropped here to improve the later regression. This filtering should be moved to getFloorspaceResidential where the regression is performed. Therefore, the high-income data points are kept at this stage.

Author(s)

Robin Hasse, Antoine Levesque, Hagen Tockhorn

Description

Residential floor space per capita

Usage

calcFloorspacePerCap()

Value

MagPIE object with residential floor space per capita

Author(s)

Robin Hasse

Description

Get historic GDP per Capita for SSP Scenarios

Usage

calcGDPPop()

Value

magpie object

Author(s)

Hagen Tockhorn

Description

Heating and cooling degree days based on raw outside temperature or bias-adjusted internal temperature (BAIT), driver for space heating and cooling demand in buildings.

Usage

calcHDDCDD(
  mappingFile,
  bait = FALSE,
  multiscen = FALSE,
  rasDir = NULL,
  cacheDir = NULL,
  fromSource = FALSE
)

Arguments

Value

magpie object of heating and cooling degree days

Author(s)

Robin Hasse, Hagen Tockhorn

Description

Rough assumption based on online reference values scaled with U-values from Hotmaps and heating degree days (HDD).

Usage

calcHeatingCapacity(swissFormular = FALSE)

Arguments

Details

This is a very rough estimation that neglects regional differences and many other effects. It should be replace by bottom-up calculation based on climate data.

Value

MagPIE object with floor-area specific heating capacity

Author(s)

Robin Hasse

Source

https://www.heizung.de/ratgeber/diverses/heizleistung-berechnen-gruende-und-ablauf.html

Description

Calculate installation cost or efficiency of heating systems

Usage

calcHeatingSystem(subtype = c("Purchasing cost", "Efficiency"))

Arguments

Value

MagPIE object with capacity-specific purchasing cost or efficiency of heating systems

Author(s)

Robin Hasse

Description

Number of persons per household.

Usage

calcHouseholdSize()

Value

MAgPIE object with household size

Note

THE EU Buildings Database gives one share of all dwellings with 5 or more persons. This share is weighted with 5 persons which can cause a downward bias.

Author(s)

Robin Hasse

Description

IEA EEI final energy data is processed and mapped w.r.t. carrier and enduse names.

Usage

calcIEA_EEI(subtype = c("buildings"))

Arguments

Details

As for the buildings sector, data for residential and commercial ("service") buildings is aggregated and the carrier "biomass" is split into traditional and modern biomass w.r.t. to income per capita.

Value

data.frame containing enduse- and carrier-resoluted energy data

Author(s)

Hagen Tockhorn

Description

Computes data for the Primary Final Useful (PFU) resolution

Usage

calcIEAPFU()

Value

IEA data as MAgPIE object aggregated to country level

Author(s)

Antoine Levesque, Robin Hasse

See Also

calcOutput

Examples

## Not run:  a <- calcOutput("IEAPFU")

Description

Parameters for the lifetime of heating systems are taken from a very detailed EIA publication for building sector appliances and equipment. The range of the building shell lifetime is taken from Skarning et al. 2017.

Usage

calcLifetimeParams(subtype)

Arguments

Value

MagPIE object with Weibull lifetime distribution parameters

Author(s)

Robin Hasse

Source

https://www.eia.gov/analysis/studies/buildings/equipcosts/pdf/full.pdf

http://dx.doi.org/10.1016/j.enbuild.2017.01.080

Description

Calculate Reference data for input matching

Usage

calcMatchingReference(subtype)

Arguments

Author(s)

Robin Hasse

Description

PFU Data is disaggregated into different thermal and non-thermal Enduses using FE Shares from Odyssee and IEA_ETP.

Usage

calcPFUDB()

Details

This was adapted from EDGE function 'getPFUDB.R'.

Value

magpie object

Author(s)

Hagen Tockhorn, Robin Hasse

Description

Population differentiated by location (urban/rural) and building type

Usage

calcPopulationBuildings()

Author(s)

Robin Hasse

Description

The renovation depth is currently calculated based on the ratio of specific useful energy demand after and before the renovation. This is valid under the assumption that there is no change in behaviour (rebound effect) which we currently make. Once adaptive behaviour is considered, we should use the ratio of U-values.

Usage

calcRenDepth()

Author(s)

Robin Hasse

Description

Specific renovation cost are estimated with a linear model with intercept w.r.t. to the renovation depth.

Usage

calcRenovationCostModel()

Details

The specific investment cost for renovation depending on the depth of renovation is calculated using a two-level statistical model fitted with data from a European Commission (EC) report on renovation. In a fist step, we fit a simple linear model with intercept to predict specific investment (USD/m2) with relative PE savings. In a second step, this linear model is scaled with one factor for each region. This scaling factor is predicted with GDP/POP using a negative exponential curve. For EU regions that are reported, we correct the result with a region-specific but time-invariant factor to best match the linear model to the data. This way, we get a linear model with intercept for each region that can be extrapolated to other regions (using GDP/POP) which still matches data of EU regions very well. The cost is finally disaggregated across residential building types (SFH, MFH) based on a rough cost factor seen in the ENTRANZE data.

Value

MAgPIE object with specific renovation cost

Author(s)

Robin Hasse

Source

https://op.europa.eu/s/xnYt

Description

Shares are calculated based on energy demands in households and services from the IEA ETP data base. Missing shares that result from missing demand data are filled with the average share across all regions and periods and then normalised to sum up to one again. Biomass is split according to GDP per Capita (see toolSplitBiomass).

Usage

calcShareETP(subtype = c("enduse", "carrier"), feOnly = FALSE)

Arguments

Value

MAgPIE object with historic shares

Author(s)

Robin Hasse, Antoine Levesque, Hagen Tockhorn

Description

Shares are calculated based on energy demands in households and services from the Odyssee data base (EU member states). In case of multi-level shares, the function gives the share w.r.t. to the last categories but for all categories. E.g. 'enduse_carrier' gives the share of each carrier in the demand from each end use. Missing shares that result from missing demand data are filled with the average share across all regions and periods and then normalised to sum up to one again. Biomass is split according to GDP per Capita (see toolSplitBiomass).

Usage

calcShareOdyssee(
  subtype = c("enduse", "carrier", "enduse_carrier"),
  feOnly = FALSE
)

Arguments

Value

MAgPIE object with historic shares

Author(s)

Robin Hasse, Antoine Levesque, Hagen Tockhorn

Description

Merges and transforms the calculated shares from the Datasets: Odyssee IEA_ETP TCEP WEO

Usage

calcShares(
  subtype = c("carrier_nonthermal", "carrier_thermal", "enduse_nonthermal",
    "enduse_thermal"),
  carrierCorrection = FALSE,
  feOnly = FALSE
)

Arguments

Details

Enduse shares are extrapolated using a linear regression where the ETP datapoint as well as selected regions from TCEP serves as origin and the entire TCEP dataset provides the growth factor.

Carrier shares are exported w.r.t. to all carriers per region per period. In the case of "carrierCorrection = TRUE", carrier shares are exported w.r.t. their contribution to each individual enduse. The reason for the latter is that the implemantation here is closer to EDGE-B, but didn't work well with further data processing steps (e.g. toolDisaggregate) where EC shares are not necessarily used. Hence, this case is by default disabled.

In the thermal case, the enduse "appliances" is transformed to "refrigerators" using the region-specific refrigerator share used in EDGE-B. Higher-resoluted data was not available.

Value

data.frame with historic energy demands

Note

The parameter "feOnly" is only applicable to IEA_ETP and TCEP data, since this is the necessary data to do a full disaggregation of EU and EC data, whereas Odyssee already gives disaggregated data for countries of the European Union.

As done in EDGE-B, enduse-disaggregated FE data from WEO for the regions MIE, AFR and JAP were used instead for values from IEA ETP.

Author(s)

Hagen Tockhorn, Robin Hasse

Description

Shares are calculated based on energy demands from the Odyssee data base for EU member states,

Usage

calcSharesBuildingDemand(subtype = c("enduse", "carrier"))

Arguments

Value

MAgPIE object with historic shares

Author(s)

Robin Hasse, Antoine Levesque

Description

Calculate Enduse Shares from TCEP data base

Usage

calcShareTCEP()

Value

magpie object

Author(s)

Hagen Tockhorn

Description

Calculates country surface

Usage

calcSurface()

Value

Surface by country

Author(s)

Antoine Levesque

Description

IEA's Tracking clean energy progress 2014

Usage

calcTCEP(subtype)

Arguments

Author(s)

Robin Hasse

Description

Calculate useful energy demand for space heating in buildings

Usage

calcUEdemand()

Author(s)

Robin Hasse

Description

Calculate U-value

Usage

calcUValue()

Author(s)

Robin Hasse

Description

Passes U-values read from source

Usage

calcUValueSource(subtype = c("EUBuildingsObservatory", "ETSAP"))

Arguments

Author(s)

Falk Benke, Robin Hasse

Description

The expected value of the respective climate variable (except temperature) is calculated from parameters taken from a preceding linear regression done in calcBAITpars where the respective variable is correlated with the near-surface atmospherical temperature. If no cell-resoluted parameters are given, the globally-meaned parameters from Staffel et. all 2023 are taken (see https://doi.org/10.1038/s41560-023-01341-5).

Usage

cfac(t, type, params = NULL)

Arguments

Value

counterfactuals for respective climate variable

Description

Check if time period of BAIT input data (rsds, sfc, huss) is congruent with near-surface temperature data (tas).

Usage

checkDates(baitInput, tasData)

Arguments

Value

baitInput with congruent time periods w.r.t. tasData

Description

BAIT is calculated from raster data on near-surface atmospherical temperature (tas), surface downdwelling shortwave radiation (rsds), near-surface wind speed (sfcwind) and near-surface specific humidity (huss). The latter three climate parameters are incorporated in the calculation of BAIT as the difference from their real value to the their expected value w.r.t. the near-surface temperature (see cfac). These are then incorporated in a weighted sum to account for the respective influence of each climate parameter on BAIT. The raster data containing BAIT is smoothed to account for the buildings' thermal inertia (see smooth) and blended with the near-surface temperature (see blend).

Usage

compBAIT(baitInput, tasData, weight = NULL, params = NULL)

Arguments

Value

raster object with BAIT values

Description

Assign HDD/CDD values for given ambient/limit temperature

Usage

compCellHDDCDD(temp, typeDD, tlim, factors)

Arguments

Value

raster object with HDD/CDD values

Description

HDD/CDD values are pre-calculated for an interval tlow-tup and for a set of limit temperatures tlim with a temperature resolution of 0.1C.

Usage

compHDDCDDFactors(tlow, tup, tlim, tambStd = 5, tlimStd = 5)

Arguments

Details

The respective heating/cooling degree days are calculated as the difference between the assumed ambient and a limit temperature, aggregated to a full day. The latter defines a threshold above/below which cooling/heating is assumed to be initiated.

To account for heterogenity in heating/cooling behavior, the ambient and limit temperature, tamb and tlim, are assumed to be normally distributed. This changes the calculation of a degree day to a double integration of tLimit - T_ambient_day with integration boundaries set at 3 standard deviations, tambStd and tlimStd, from tamb and tlim respectively.

As consequence, the ramp function of HDD_day = max(0, tLimit - T_ambient_day) changes to a curved function that is above zero even if the mean of T_ambient_day is above the mean of tLimit.

Value

data frame of HDD/CDD values

Description

This function calculates country-wise population-weighted HDD/CDD values for raw ambient temperature or bias-adjusted internal temperature for a given set of limit temperatures from raster data on (various) climate variables.

Usage

compStackHDDCDD(
  ftas,
  tlim,
  countries,
  pop,
  factors,
  bait,
  frsds = NULL,
  fsfc = NULL,
  fhuss = NULL,
  wBAIT = NULL,
  params = NULL,
  rasDir = NULL
)

Arguments

Details

For further processing, raster objects containing degree day data are written for an interval of ten years.

Description

Convert data from Census Hub

Usage

convertCensusHub(x, subtype)

Arguments

Value

MAgPie object with converted data

Author(s)

Robin Hasse

Description

Tidy data from Daioglou et al. 2012. Currently, only the specific floor space (m2/cap) is available from household data file. UN sources are dropped, if there is data from other sources for the same region. For the remaining data, if there is data from multiple UN Source for a region and period (i.e. different cities), the average of those sources is considered. This neglects the differences in population size of the cities. Selected data points are removed additionally.

Usage

convertDaioglou(x, subtype = "households.specific floor space")

Arguments

Value

clean MAgPIE object with unique data points

Author(s)

Robin Hasse, Antoine Levesque

Description

Convert Weibull lifetime distribution parameters for buildings

Usage

convertDeetman2020(x, subtype)

Arguments

Author(s)

Robin Hasse

Description

Convert historic floor space data from the European Environment Agency.

Usage

convertEEAfloorspace(x)

Arguments

Value

floor national floor space in million m2

Author(s)

Robin Hasse

Description

We should consider prper extrapolation

Usage

convertEnergiforsk2016(x)

Arguments

Author(s)

Robin Hasse

Description

Convert to common units.

Usage

convertEUBuildingsDB(x, subtype)

Arguments

Value

clean MAgPIE object with

Author(s)

Robin Hasse

Description

Each report of the Heat Pumps Barometer covers the two years before. There numerous cases, where reports contradict in overlapping values or there are huge jumps (probably due to changes in the accounting). This function creates smooth trajectories back-casting from the most recent data. If data overlaps, earlier data is scaled to match more recent data. In case of gaps, the trajectories are back-casted maintaining relative growth within each report. Growth rates between reports are interpolated. The Method yields mostly smooth trajectories always hitting the mos recent reported numbers.

Usage

convertEurObservER(x, subtype)

Arguments

Value

MAgPie object with converted data

Author(s)

Robin Hasse

Description

Drop EU28 aggregate and rename regions to ISO3.

Usage

convertEuropeanCommissionRenovation(x)

Arguments

Value

clean MAgPIE object with data from EU Buildings Database

Author(s)

Robin Hasse

Description

Convert buildings-related data from Eurostat.

Usage

convertEurostatBuildings(x, subtype)

Arguments

Value

MAgPie object with converted data

Author(s)

Robin Hasse

Description

Drop subnational data but keep other subgroups (i.e. rural/urban, income quartiles)

Usage

convertGDL(x, subtype)

Arguments

Value

clean MAgPIE object with data from EU Buildings Database

Author(s)

Robin Hasse

Description

Convert historic and scenario based HDD CDD data to data on ISO country level.

Usage

convertHDDCDD(x)

Arguments

Value

HDD CDD data as MAgPIE object aggregated to country level

Author(s)

Antoine Levesque

Description

The temporary data already is at EDGE-B regional resolution. It is uniformly disaggregated to country level here to comply with the madrat framework before it is aggregated to the identical resolution in fullEDGEBUILDINGS.

Usage

convertHDDCDDtemp(x)

Arguments

Value

HDD CDD data at country resolution

Author(s)

Robin Hasse

Description

convert Hotmaps

Usage

convertHotmaps(x)

Arguments

Value

MagPIE object with converted data

Author(s)

Robin Hasse

Description

convert ISIMIP data for mredgebuildings

Usage

convertISIMIPbuildings(x, subtype)

Arguments

Value

rasterBrick object

Author(s)

Hagen Tockhorn

Description

Rename regions and convert unit

Usage

convertOdyssee(x, subtype = "households")

Arguments

Value

clean MAgPIE object

Author(s)

Robin Hasse

Description

This was adapted from EDGE function 'getPFUDB.R'.

Usage

convertPFUDB(x)

Arguments

Value

magpie object

Author(s)

Hagen Tockhorn

Description

Convert TCEP data base

Usage

convertTCEP(x, subtype)

Arguments

Value

magpie object

Author(s)

Hagen Tockhorn, Robin Hasse

Description

Clean up UN data on household size and composition

Usage

convertUNHouseholds(x, subtype)

Arguments

Value

clean MAgPIE object with

Author(s)

Robin Hasse

Description

Convert WEO enduse shares w.r.t. to global final energy demand

Usage

convertWEO(x)

Arguments

Author(s)

Hagen Tockhorn

Description

Extrapolate missing values beyond existing periods

Usage

extrapolateMissingPeriods(chunk, key, slopeOfLast = 5)

Arguments

Value

data.frame with extrapolated column key

Author(s)

Robin Hasse, Hagen Tockhorn

Description

Compute complete input data set for BRICK

Usage

fullBRICK(rev = 0)

Arguments

Author(s)

Robin Hasse

Description

Function that produces the complete ISO data set required for the EDGE - Buildings model.

Usage

fullEDGEBUILDINGS(rev = 0)

Arguments

Author(s)

Antoine Levesque, Robin Hasse, Hagen Tockhorn

See Also

readSource,getCalculations,calcOutput

Examples

## Not run: 
fullEDGEBUILDINGS()

## End(Not run)

Description

Takes the historic final energy demand by carriers from IEA and disaggregates it into different end uses.

Usage

getFEbyEUEC()

Value

data.frame with historic energy demands

Author(s)

Hagen Tockhorn

Description

Additional columns to join by that have different names can be give. The kind of joint can also be specified.

Usage

join_all(
  x,
  y,
  by = NULL,
  .direction = "left",
  exclude = "value",
  silent = TRUE,
  ...
)

Arguments

Author(s)

Robin Hasse

Description

Read in necessary climate data to calculate BAIT or calculate mean values of said climate data to fill missing data in case of temporal mismatch between near-surface atmospherical temperature and other considered climate data.

Usage

prepBaitInput(
  frsds = NULL,
  fsfc = NULL,
  fhuss = NULL,
  baitInput = NULL,
  fillWithMean = FALSE
)

Arguments

Value

named list with read-in or meaned climate data

Description

The Census Hub of the European Statistical System provides census data from 2011 on population and housing of EU member states. 2021 census data is announced to be published there too.

• typeVintage: number of dwellings of different construction periods and building types (single, two, multiple dwellings). Show data on: dwellings, location: nations, Topics: Type of building, Period of construction (select all) -> CSV (Separator: Comma)

Usage

readCensusHub(subtype)

Arguments

Value

MAgPIE object with data

Author(s)

Robin Hasse

Source

https://ec.europa.eu/CensusHub2/

Description

Compilation of survey data. Quintile 0 stands for the entire population.

• households: Global data on population, household size, floor space per capita (m2/cap), population density (cap/km2), GINI and household expenditures (PPP2005/cap). Sources are written into a dimension to allow later cleaning based on sources

• shares: Final energy shares of end uses in buildings. Sources are dropped.

Usage

readDaioglou(subtype = "households.specific floor space")

Arguments

Author(s)

Robin Hasse, Antoine Levesque

References

https://doi.org/10.1016/j.energy.2011.10.044

Description

Read Weibull lifetime distribution parameters for buildings

Usage

readDeetman2020(subtype = "residential")

Arguments

Value

MagPIE object with Weibull lifetime parameters for different world regions

Author(s)

Robin Hasse

Source

https://doi.org/10.1016/j.jclepro.2019.118658

Description

Project data: European Climate and Energy Modelling Forum

Usage

readECEMF(subtype = "FEPrices")

Arguments

Details

• FEPrices: price components w/o VAT in EUR/MWh

Author(s)

Robin Hasse

Description

Floor space for many European countries between 1990 and 2009 in thousand m2.

Usage

readEEAfloorspace()

Value

magpie object with floor space

Author(s)

Robin Hasse

See Also

readSource

Examples

## Not run:  a <- readSource(type = "EEAfloorspace")

Description

Read EHI Heating Market Report

Usage

readEHI(subtype)

Arguments

Value

magpie object

Author(s)

Robin Hasse

Description

Read European district heating prices

Usage

readEnergiforsk2016()

Author(s)

Robin Hasse

Description

Read in energy efficiency values from ETSAP

Usage

readETSAP()

Description

Compilation of various data sources from the European Commission. Because of the considereable size of this database, a subtype has to be provided. The subtype represents the category of items. It is possible to append selected variables for further filtering.

Usage

readEUBuildingsDB(subtype = "")

Arguments

Value

magpie object

Author(s)

Robin Hasse

See Also

readSource

Examples

## Not run:  a <- readSource(type = "EUBuildingsDB")

Description

Read in energy efficiency values of building shell in residential and non-residential buildings by the EU Buildings Observatory (2017)

Usage

readEUBuildingsObservatory()

Description

PDF market reports on Renewables in Europe

Usage

readEurObservER(subtype)

Arguments

Value

magpie object

Note

I download the report and snip a png of the table. Then I extract the data with this online tool and save it as a tabular csv in the source folder. The Tool is not fully reliable. This function throws an error if values don't sum up. You then have to correct the csv file manually.

Author(s)

Robin Hasse

Source

https://www.eurobserv-er.org/

Description

This function reads data on renovation from a pdf file of an EC report from 2019 (Tables 2 - 13). There is no period dimension. The data represents averages between 2012 and 2016.

Usage

readEuropeanCommissionRenovation(subtype = "csv")

Arguments

Value

MAgPIE object with renovation data

Author(s)

Robin Hasse

Source

https://op.europa.eu/en/publication-detail/-/publication/97d6a4ca-5847-11ea-8b81-01aa75ed71a1

See Also

readSource

Examples

## Not run:  a <- readSource(type = "EuropeanCommissionRenovation")

Description

Read various data sets from Eurostat that are used in the modelling of buildings

• nrg_inf_hptc: Heat pumps - technical characteristics by technologies

• ilc_hcmh02: Average size of dwelling by household type and degree of urbanisation

• nrg_d_hhq: Disaggregated final energy consumption in households

Usage

readEurostatBuildings(subtype)

Arguments

Value

MAgPIE object with data

Note

see https://ec.europa.eu/eurostat/web/energy/data/energy-balances for definitions of codes

Author(s)

Robin Hasse

Description

Different databases each comprising the harmonised results of regional/national surveys. Available subtypes are

• SHDI: Subnational Human development Database

• AreaDatabase: Subnational development Indicators (low/medium income countries)

Usage

readGDL(subtype)

Arguments

Value

magpie object

Author(s)

Robin Hasse

Source

https://globaldatalab.org/

References

https://doi.org/10.1038/sdata.2019.38

https://globaldatalab.org/asset/286/Smits%20GDL%20Working% 20Paper%2016-101%20v360.pdf

See Also

readSource

Examples

## Not run:  a <- readSource(type = "GDL")

Description

Read heating and cooling degree days; past and scenario data as magclass objects

Usage

readHDDCDD(subtype = 18)

Arguments

Value

magpie object HDD CDD

Author(s)

Antoine Levesque

See Also

readSource

Examples

## Not run:  a <- readSource(type="HDDCDD")

Description

Heating and cooling degree days calculated with mredgebuildings::calcHDDCDD. Calculating this data currently requires manual help. We therefore read the result as a source here. This becomes obsolete once the primary calculation is stable.

Usage

readHDDCDDtemp()

Author(s)

Robin Hasse

Description

Consistent building stock of EU28 residential and commercial sector. The data presents a snapshot, I guess in 2016 but this is not clear to me yet.

Usage

readHotmaps()

Details

There are a few duplicate rows. Due to inconsistent values we average across duplicates.

Author(s)

Robin Hasse

Description

Relevant data such as region masks, population and relevant climate data are read in. The relevant file is declared in the subtype with the full file name.

Usage

readISIMIPbuildings(subtype)

Arguments

Details

If the file name includes a suffix in the form of an integer such as _<int>.filetype, the file is split into a single year period, e.g. <filename>_2001_2010_2.nc will return data for the second year of the 2001-2010 period, here 2002.

Note

folder structure in inputdata/sources/ISIMIPbuildings is expected to be: country masks : var/ population : var/scenario other : var/scenario/model

currently, this function only reads data from ISIMIP3b

Author(s)

Hagen Tockhorn

Description

Copied from website: The Odyssee database contains detailed energy consumption by end-use and their drivers as well as energy efficiency and CO2-related indicators. Latest available data is provided by national representatives, such as energy agencies or statistical organization, from all EU countries as well as Norway, Serbia, Switzerland and the United Kingdom.

Usage

readOdyssee(subtype = "households")

Arguments

Value

magpie object

Note

To download new data, log into the website, select all items of all levels and download the data 'column-orientated csv'.

Variables are labels with the item code but full names can be found in the source data

Author(s)

Pascal Führlich, Robin Hasse

Source

https://odyssee.enerdata.net/database/

Description

This was adapted from EDGE function 'getPFUDB.R'.

Usage

readPFUDB()

Value

magpie object

Author(s)

Hagen Tockhorn

Description

Read TCEP data base

Usage

readTCEP(subtype)

Arguments

Value

magpie object

Author(s)

Hagen Tockhorn, Robin Hasse

Description

Compilation of indicators on household size and membership composition. The estimates are based on 814 unique data points from 172 countries or areas, representing approximately 97 per cent of the world’s population in 2019, collected between 1960 to 2018.

Usage

readUNHouseholds(subtype)

Arguments

Value

magpie object

Author(s)

Robin Hasse

Source

https://www.un.org/development/desa/pd/data/household-size-and-composition

See Also

readSource

Examples

## Not run:  a <- readSource(type = "UNHouseholds")

Description

Read WEO enduse shares w.r.t. to global final energy demand

Usage

readWEO(subtype)

Arguments

Author(s)

Hagen Tockhorn

Description

Smooth data over preceding two days

Usage

smooth(r, weight)

Arguments

Value

smoothed raster data

Description

Introduce refrigerators as appliances

Usage

toolAddThermal(df, mapping, fridgeShare, feOnly = TRUE, shareOf = NULL)

Arguments

Author(s)

Hagen Tockhorn

Description

Calculate Shares

Usage

toolCalcShares(df, colShare)

Arguments

Value

data.frame with calculated shares for colShare

Author(s)

Hagen Tockhorn

Description

This function expects a MAgPIE object with ISO country codes in the spatial dimension. It applies toolCountryFill but fills with the average across regions.

Usage

toolCountryFillAvg(x, ...)

Arguments

Author(s)

Robin Hasse

Description

This function takes energy demand data that differentiates only by carrier and disaggregates it further such that all relevant combinations of carrier and end use are differentiated. It ensures that given end use shares (across all carriers) are met.

Usage

toolDisaggregate(
  data,
  enduseShares,
  outliers = NULL,
  exclude = NULL,
  dataDisagg = NULL,
  regionMapping = NULL
)

Arguments

Details

The function essentially performs a quadratic optimisation. The constraints make sure that both the carrier quantities and the end use shares are met. This generally leaves infinite solutions (in problematic cases none). To get to one disaggregation, the deviation from a given distribution across all relevant combinations of carriers and end uses is minimised.

Author(s)

Hagen Tockhorn, Robin Hasse

Description

This is adapted from EDGE-B Function "splitBiomass.R" by Antoine Levesque.

Usage

toolSplitBiomass(df, dfGDPpop, min = 10000, max = 15000, varName = "biomass")

Arguments

Details

Any value belonging to the carrier defined by "varName" will be split into the default carrier names "biomod" and "biotrad" according to weights given by "dfGDPpop".

Value

factor.data.frame

Author(s)

Hagen Tockhorn

Description

Takes MAgPIE object with a variable dimension that has the unit as suffix separated with '_' and converts the unit according to a unit conversion table.

Usage

toolUnitConversion(x, unitConversion, dim = 3.1, removeUnit = FALSE)

Arguments

Author(s)

Robin Hasse

Description

Exchange rate between USD and EUR

Usage

usd2eur(year = 2020)

Arguments

Value

MER EUR in USD in given year

Author(s)

Robin Hasse