Package 'mredgebuildings'

Description

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

Author(s)

Maintainer: Robin Hasse [email protected] (ORCID)

Authors:

• Pascal Sauer [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

Adjusts energy demand estimates to meet specified carrier share targets within end-uses while preserving total carrier demands. This function applies proportional adjustments to align existing shares with target shares while maintaining energy balance constraints. It handles missing combinations, zero values, and special cases where target shares exist for carriers with no historical demand. The adjustments are applied consistently across all time periods with careful rescaling to ensure carrier totals remain unchanged.

Usage

.enforceShares(estimate, forceShares)

Arguments

Details

Note: This function requires forceShares data to contain only one period per region-enduse-carrier combination to work correctly.

Value

data frame with adjusted estimates preserving carrier totals

Author(s)

Hagen Tockhorn

Description

Fills missing values in a time series dataset using interpolation for gaps between known values and growth-based extrapolation for extending the series. The function uses linear interpolation between known values and calculates appropriate growth rates for extrapolation based on available data points. For regions with 3+ data points, it fits a log-linear model, while for regions with 2 data points, it uses a simple growth rate calculation. For regions within the extGrowthFactors data frame, the growth rates are used.

Usage

.fillTimeseries(df, extGrowthFactors = NULL, periods = 1990:2025)

Arguments

Value

A complete data frame with filled values for all region-enduse-period combinations

Description

This function aggregates selected variables at a given categorical level and replaces them with a new summarized entry in the dataset.

Usage

aggToLevel(df, variables, level, newname)

Arguments

Value

A data frame where the selected variables have been aggregated into a single variable with the specified newname.

Description

Calculates air conditioning ownership rates per household by combining data from household surveys, IEA, and Odyssee. Data sources are prioritized in the following order: Odyssee > IEA > household surveys. Returns all available historical data points without period harmonization.

Usage

calcACOwnershipRates()

Value

magpie object with all available historical data points per region

Author(s)

Hagen Tockhorn

Description

This function fills AC ownership rates for all regions from 1990 to endOfHistory using global regression parameters (alpha, beta, gamma, delta) with region-specific GDP shifts. The GDP shift (gdppopShift) is calculated for each region to horizontally shift the global S-curve along the GDP axis so that it passes through the region's most recent historical data point. This calibrates the global curve to match available historical data while maintaining the same functional form across all regions.

Usage

calcACOwnershipRatesFilled(endOfHistory = 2025)

Arguments

Details

The logistic formula used is:

$penetration = 1 / (1 + \exp(\alpha - \beta \cdot (gdppop - gdppopShift)^\delta \cdot CDD^\gamma))$

Regions without historical data use the global curve as-is (gdppopShift = 0).

Value

magpie object with filled AC ownership rates for all regions (1990-endOfHistory)

Author(s)

Hagen Tockhorn

Description

This function estimates global regression parameters for air conditioner (AC) ownership rates using a logistic function with GDP per capita and cooling degree days (CDD) as predictors. The function fits a non-linear regression model to derive parameters for the AC penetration logistic curve.

Usage

calcACOwnershipRegression()

Details

The regression model follows the logistic formula:

$penetration = 1 / (1 + \exp(\alpha - \beta \cdot gdppop^\delta \cdot CDD^\gamma))$

where:

• $\alpha$ is the intercept parameter that sets the lower boundary of AC ownership

• $\beta$ is the interaction coefficient (negated from fitted model coefficient b)

• $\gamma$ is the CDD exponent parameter

• $\delta$ is the GDP per capita exponent parameter

The function performs a two-stage estimation process:

1. Alpha is set exogenously based on a minimum ownership rate assumption

2. Linear regression on log-transformed data with fixed alpha to obtain starting values for nls()

3. Non-linear regression using nls() with alpha fixed, estimating only beta, gamma, and delta

Value

magpie object with global regression parameters (alpha, beta, gamma, delta)

Author(s)

Hagen Tockhorn

Description

BRG has extremely comprehensive (and extremely expensive) sales data. But one can recover total sales of boilers for space heating from the EHPA report.

Usage

calcBRG()

Value

MagPIE object with total boiler sales in European countries

Author(s)

Robin Hasse

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"), granularity = NULL)

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

Calculate historic construction flows

Usage

calcConstruction()

Author(s)

Robin Hasse

Description

floor-space specific construction cost

Usage

calcCostConstruction(granularity = NULL)

Arguments

Author(s)

Robin Hasse

Description

Demolition cost

Usage

calcCostDemolition()

Author(s)

Robin Hasse

Description

floor-space specific renovation cost

Usage

calcCostRenovationBS(granularity = NULL)

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

floor-space specific renovation cost

Usage

calcCostRenovationHS(energyLadder = FALSE, granularity = NULL)

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(endOfHistory = 2025)

Arguments

Value

magpie object

Author(s)

Hagen Tockhorn

Description

Discount rate including implicit discounting

Usage

calcDiscountRate(granularity = NULL)

Arguments

Author(s)

Robin Hasse

Source

https://doi.org/10.1016/j.eneco.2011.07.010

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. For electric space cooling, a bounded logistic function is used with parameters k and x0. All parameters are determined using a nonlinear least-squares regression.

Usage

calcEfficiencyRegression(gasBioEquality = TRUE)

Arguments

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

This function processes historical and scenario electricity demand data for Information and Communication Technology (ICT) and data centers (DC). The initial data comes from exogenous projections using the Digital Transformation Level (DGI) as the primary driver of demand, with IEA data serving as the historical baseline.

Usage

calcElecDemandICT(endOfHistory = 2025)

Arguments

Details

The function processes multiple demand estimates:

• Mean and medium estimate

• Lower demand boundary (DGI elasticity from historical efficiency dominant phases)

• Upper demand boundary (DGI elasticity from historical service dominant phases)

Data is downscaled from R5 to country-level using data center counts as weights, with exceptional fixed shares for China within Asia Pacific (73.28%) and for Europe's R12 sub-regions (WEU: 75%, EEU: 14%, FSU: 11%). Network demand is added using global network/DC ratios. Historical data (IEA Base) is separated and harmonized with SSP scenario projections.

Value

A magpie object containing ICT electricity demand data calculated from DC demands across regions, time periods, and scenarios.

Author(s)

Hagen Tockhorn

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

Calculate Efficiencies of Final (FE) to Useful (UE) Energy Conversion for all combinations of Energy Carriers and Enduses. Missing data points within the time interval of the original data (PFUDB) are filled with estimates from a non-linear regression w.r.t. GDP per capita. 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(endOfHistory = 2025)

Arguments

Value

MAgPIE object with historic floor space

Note

RH: 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(scenarios, granularity = NULL)

Arguments

Value

MagPIE object with residential floor space per capita

Author(s)

Robin Hasse

Description

Computes or loads annual heating and cooling degree days (HDDs and CDDs) for given socio-economic scenarios.

Usage

calcHDDCDD(scenario = "SSPs", fromSource = TRUE, endOfHistory = 2025)

Arguments

Value

A magclass object with annual degree-day values.

Note

For fromSource = FALSE, the job requires a runtime of >24h and memory > 100G.

Author(s)

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

The data is taken from the EU reference scenario technology assumptions. We map our technologies to the higher resolved PRIMES technologies. For missing commercial technologies, we assume residential values but decrease purchasing cost by 10%.

Usage

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

Arguments

Value

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

Author(s)

Robin Hasse

Description

Calculate Matrix of heating system replacement shares

Usage

calcHeatingSystemReplacement()

Value

MagPIE object with shares

Author(s)

Robin Hasse

Description

We use national data from industry association for the biggest European countries and European data sets from the EHI and EHPA.

Usage

calcHeatingSystemSales()

Value

MagPIE object with heating system sales

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"), mixData = FALSE)

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. Energy data on "space_cooling" is aggregated over all carriers into "elec".

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, granularity = NULL)

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

Extrapolate the residential heating system distribution in each vintage from DEU to other countries and to commercial buildings.

Usage

calcLTRS(refGranularity = c("typ", "sec"))

Arguments

Details

We use iterative proportional fitting to extrapolate the German residential pattern to other countries and subsectors. This data is extremely rough and should be taken with care. It is helpful in the matching nonetheless to represent interactions between vintage and heating systems while other more reliable references assure overall consistency.

Value

MagPIE object

Author(s)

Robin Hasse

Description

Calculate Reference data for input matching

Usage

calcMatchingReference(subtype)

Arguments

Author(s)

Robin Hasse

Description

Residential stock variables are given in number of dwellings, flats or houses while commercial stock is given in Floor space. This function computes all stock variables in terms of floor space considering dwelling size.

Usage

calcOdysseeStock(interpolate = FALSE)

Arguments

Details

The data can be inter- and extrapolated using IDEES floor space data. As we trust the absolute level less than Odyssee, we only consider the growth to fill values.

Value

MagPIE object with building stock data

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(granularity = NULL)

Arguments

Value

MagPIE object with population at BRICK granularity

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

Combine data on heating system sales and replacement to derive full building state transition data

Usage

calcRenovation()

Value

MagPIE object with historic renovation flows

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

The data is obtained from the 2021 IEA report "An Energy Sector Roadmap to Carbon Neutrality in China" from Figure 3.25

Usage

calcShareChina()

Author(s)

Robin Hasse

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

This function calculates the proportion of energy demand by carrier or enduse in the buildings sector, based on IEA Energy Efficiency Indicators data.

Usage

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

Arguments

Value

A data.frame with the respective shares

Author(s)

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 are handled as follows: If all data for a region is missing (no demand data at all), the shares remain NA. If a specific category (e.g., a specific carrier or end use) has no data across all periods for a region, its shares are filled with 0, assuming zero demand for that category. 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

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

Merges and transforms the calculated shares from the datasets: Odyssee 2024 IEA Energy Efficiency Indicators 2024 IEA Tracking Clean Energy Progress IEA World Energy Outlook 2014

Usage

calcSharesEU(thermal = FALSE, endOfHistory = 2025)

Arguments

Details

End-use shares are extrapolated using a log-linear regression on the existing data points while values in-between existing data are interpolated linearly. Data is merged following a strict priority hierarchy:

1. Odyssee - Provides detailed European data

2. IEA Energy Efficiency Indicators (EEI) - Selected countries, global scope

3. IEA China data

4. IEA World Energy Outlook (WEO) - Only used if EEI has fewer than 2 data points for a particular region-enduse combination

5. IEA Energy Technology Perspectives (ETP) - Only used if EEI has fewer than 2 data points and no WEO data is available

For specific regions (Africa, Middle East), data points from WEO are prioritized due to their regional specificity. These data points are extrapolated using growth factors determined by Tracking Clean Energy Progress (TCEP) to ensure consistent time series extension.

End-use shares are extrapolated using a log-linear regression on existing data points, while values between existing data points are interpolated linearly. The function preserves dataset consistency by avoiding mixing data sources when sufficient data points are available from a single source.

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

Value

data.frame with historic end-use shares for each energy carrier

Author(s)

Hagen Tockhorn, Robin Hasse

Description

Calculate Enduse Shares from TCEP data base

Usage

calcShareTCEP()

Value

magpie object

Author(s)

Hagen Tockhorn

Description

Table 10.2 in the WEO 2015 report lists the chare of regional enduses in the global final energy demand. Here, we calculate the share of enduses in the final energ demand for buildings in each region.

Usage

calcShareWEO()

Author(s)

Robin Hasse

Description

The share of heating systems that are replaced with the same technology during heating system replacement for each technology.

Usage

calcStatusQuoShare()

Details

Quantifying the status quo bias requires detailed data on heating system replacement activities with initial and final technology. There are very few and mostly rough sources on this. We therefore assume technology-specific and temporally and spatially invariant shares of initial systems that are identically replaced. This leaves enough degrees of freedom to the model to reflect region-specific stocks and preferences while representing the status quo bias. It might make sense to model the decision as a two-stage process in which agents first decide whether to just replace identically and thus follow the bias or decide for a new system independent of their old one. This is not yet reflected in this calculation.

Author(s)

Robin Hasse

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 = "enduse")

Arguments

Author(s)

Robin Hasse

Description

Calculate useful energy demand for space heating in buildings

Usage

calcUEdemand(granularity = NULL)

Arguments

Author(s)

Robin Hasse

Description

Calculate U-value

Usage

calcUValue()

Author(s)

Robin Hasse

Description

Estimates regional fit parameters for projecting building envelope U-values using mixed-effects regression. Combines data from Hotmaps, MessageIX Buildings, and EU Buildings Observatory to model thermal performance across regions based on climate indicators (HDD/CDD) and economic development (GDP per capita).

Usage

calcUvalueParameters(endOfHistory = 2025)

Arguments

Details

The function uses a linear mixed-effects model with the formula: log(U-value - minU) ~ HDD + CDD + log(gdppop + 1) + (interaction terms) + (1 | region) where the interaction terms are interactions between HDD/CDD and log(gdppop + 1), respectively, and (1 | region) denotes a region-specific offset.

This captures the relationship between U-values and:

• Heating degree days (HDD)

• Cooling degree days (CDD)

• GDP per capita (gdppop)

• Interaction terms: how climate effects vary with economic development

• Regional random effects: region-specific deviations from global pattern

The function handles regional parameter estimation in two ways:

1. For regions with sufficient historical data points, parameters are directly estimated via mixed-effects regression (lmer)

2. For regions with insufficient data, the function:

   • Uses global fixed effects as a baseline

   • Calculates the difference between aggregated regional projections and their reference values

   • Applies empirical Bayes shrinkage to balance between global pattern and regional differences

   • Adjusts intercepts using these shrunken offsets to create region-specific parameters

The resulting parameters can be used to project future U-values based on climate and economic scenarios.

Value

magpie object with regional fit parameters

Author(s)

Hagen Tockhorn

Description

Simple estimate of total FE demand in different building types

Usage

calcWeightFeBuildings()

Details

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

Author(s)

Robin Hasse

Description

Convert heating systems sales in Italy from Assotermica

Usage

convertAssotermica(x)

Arguments

Value

MagPIE object with ISO 3 country codes

Author(s)

Robin Hasse

Description

Convert German heating system data from BDH

Usage

convertBDH(x)

Arguments

Value

MagPIE object with ISO 3 country codes

Author(s)

Robin Hasse

Description

Convert heating systems sales in Austria from BMK

Usage

convertBMK(x)

Arguments

Value

MagPIE object with ISO 3 country codes

Author(s)

Robin Hasse

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 data from the Federal Statistical Office of Germany

Usage

convertDestatis(x, subtype)

Arguments

Value

MagPIE object

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

Convert EHI Heating Market Report

Usage

convertEHI(x)

Arguments

Value

MagPIE object with country-level data

Author(s)

Robin Hasse

Description

Correct heat pump sales data from EHPA

Usage

convertEHPA(x)

Arguments

Value

MagPIE object with ISO 3 country codes

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

Convert heating systems sales in Spain from FEGECA

Usage

convertFEGECA(x)

Arguments

Value

MagPIE object with ISO 3 country codes

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

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 household survey data on energy carrier use for cooking, lighting and appliances ownership rates

Usage

convertHouseholdSurveys(x, subtype)

Arguments

Author(s)

Hagen Tockhorn

References

https://hdl.handle.net/10419/301069

Description

read energy carrier prices

Usage

convertIEA_OECD_Prices(x, subtype)

Arguments

Author(s)

Robin Hasse

Description

Convert Long Term Renovation Strategy

Usage

convertLTRS(x)

Arguments

Value

MagPIE object

Author(s)

Robin Hasse

Description

Convert data from MessageIX-Buildings

Usage

convertMessageIXBuildings(x, subtype)

Arguments

Description

Rename regions and convert unit

Usage

convertOdyssee(x, subtype = "250109")

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

Converts PRISMA ICT data to ISO country codes and fills missing countries. Primarily used for data center count data.

Usage

convertPRISMA_ICT(x, subtype)

Arguments

Value

A magpie object with converted country codes and filled countries

Author(s)

Hagen Tockhorn

Description

Convert Pulsen survey on Swedish heat pump market

Usage

convertPulsen(x)

Arguments

Value

MagPIE object with corrected region

Author(s)

Robin Hasse

Description

Convert building life time data from Sandberg et al. 2016

Usage

convertSandberg(x)

Arguments

Value

MagPIE object with full region converage

Author(s)

Robin Hasse

Description

Convert heating systems stock and sales in Poland from SPIUG

Usage

convertSPIUG(x)

Arguments

Value

MagPIE object with ISO 3 country codes

Author(s)

Robin Hasse

Description

Convert TCEP data base

Usage

convertTCEP(x, subtype = "enduse")

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 heating systems sales in France from Uniclima

Usage

convertUniclima(x)

Arguments

Value

MagPIE object with ISO 3 country codes

Author(s)

Robin Hasse

Description

Convert VHK data

Usage

convertVHK(x)

Arguments

Value

MagPIE object

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

Replace dots by dashes

Usage

dot2Dash(x, rev = FALSE)

Arguments

Value

character vector with replaced symbols

Author(s)

Robin Hasse

Description

Convert EUR in given year to internal unit

Usage

eur2usd(x = 1, year = 2020, iso3c = "DEU", ...)

Arguments

Value

value in constant 2017 US$MER

Author(s)

Robin Hasse

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, granularity = NULL)

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

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 heating systems sales in Italy from Assotermica

Usage

readAssotermica(subtype = NULL)

Arguments

Value

magpie object

Author(s)

Robin Hasse

Description

Read German heating system data from BDH

Usage

readBDH()

Value

MagPIE object with yearly sales of heating systems in Germany

Author(s)

Robin Hasse

Description

Read buildings characteristics data from the Built Environment Analysis Model (BEAM^2)

Usage

readBEAM2()

Author(s)

Hagen Tockhorn

Description

Read heating systems sales in Austria from BMK

Usage

readBMK()

Value

MagPIE object with yearly sales of heating systems in Germany

Author(s)

Robin Hasse

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

Read data from the Federal Statistical Office of Germany

Usage

readDestatis(subtype)

Arguments

Value

MagPIE object

Author(s)

Robin Hasse

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 = NULL)

Arguments

Value

magpie object

Author(s)

Robin Hasse

Description

This data was provided upon request. It is free for non-profit organisations but we are not allowed to share it.

Usage

readEHPA(subtype = "hpSales")

Arguments

Value

MagPIE object with HP sales data for EU member states

Author(s)

Robin Hasse

Description

Read European district heating prices

Usage

readEnergiforsk2016()

Author(s)

Robin Hasse

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

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

• nrg_d_hhq: Disaggregated final energy consumption in households

• lfst_r_lfsd2hh: Number of households by degree of urbanisation (2006-2023)

• ilc_hcmh02: Average size of dwelling by household type and degree of urbanisation (2012)

• ilc_lvho31: Average size of dwelling by household composition and degree of urbanisation (2020, 2023)

• cens_21dwbnr_r3: Dwellings by type of building, number of rooms or useful floor space (2021)

• sts_cobp_a: Building permits (1992 - 2024)

• ilc_lvhe02: Persons living in private households by heating system used in the dwelling, household composition and degree of urbanisation (2023)

• ilc_lvhe04: Persons heating their dwelling with district heating by used energy source, household composition and degree of urbanisation (2023)

• ilc_lvhe05: Persons heating their dwelling with other than district heating by used energy source, household composition and degree of urbanisation (2023)

• hbs_car_t315: Household characteristics by degree of urbanisation (1988-2020)

• ilc_lvho01: Distribution of population by degree of urbanisation, dwelling type and income group (2003 - 2024)

• ilc_lvho03: Average number of rooms per person by tenure status and dwelling type (2003 - 2024)

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

Read heating systems sales in Spain from FEGECA

Usage

readFEGECA(subtype = NULL)

Arguments

Value

magpie object

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 historical and projected degree-day data

Usage

readHDDCDD(subtype = "ssp2")

Arguments

Author(s)

Hagen Tockhorn

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

Read household survey data on energy carrier use for cooking, lighting and appliances ownership rates

Usage

readHouseholdSurveys(subtype = c("cooking", "lighting", "appliances"))

Arguments

Author(s)

Hagen Tockhorn

References

https://hdl.handle.net/10419/301069

Description

Read AC ownership rates per household from IEA Sources

Usage

readIEA_acOwnership()

Author(s)

Hagen Tockhorn

Description

The data is obtained from the 2021 IEA report "An Energy Sector Roadmap to Carbon Neutrality in China" from Figure 3.25

Usage

readIEA_China()

Author(s)

Robin Hasse

Description

Read IEA Global Weighted Average of Air Conditioning Efficiencies

Usage

readIEA_coolingEfficiencies()

Author(s)

Hagen Tockhorn

Description

read energy carrier prices

Usage

readIEA_OECD_Prices(subtype)

Arguments

Author(s)

Robin Hasse

Description

In the LTRS, EU member states have to describe their building stock. Unfortunately, this isn't standardised in any way so it is hard to make use of this data. So far, we only use data from two figures in the German LTRS.

Usage

readLTRS()

Value

MagPIE object

Author(s)

Robin Hasse

Description

Read data from MessageIX-Buildings

Usage

readMessageIXBuildings(subtype = "uvalue")

Arguments

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 = "260305")

Arguments

Value

magpie object

Note

To download new data, log into the website, select all items of all levels and download the data as CSV (1 observation = 1 row, delimeter: ","). If this exceeds the download limit, download multiple files, e.g. by splitting along periods.

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

Reads ICT and data center electricity demand data from PRISMA project Excel file. Supports different regional resolutions and data center counts.

Usage

readPRISMA_ICT(subtype)

Arguments

Value

A magpie object containing the requested ICT/data center data

Author(s)

Hagen Tockhorn

Description

Read Pulsen survey on Swedish heat pump market

Usage

readPulsen()

Value

MagPIE object

Author(s)

Robin Hasse

Source

https://skvp.se/skvpold/statistik/pulsen

Description

Read building life time data from Sandberg et al. 2016

Usage

readSandberg()

Value

MagPIE object with life time parameters of buildings

Author(s)

Robin Hasse

Source

https://doi.org/10.1016/j.enbuild.2016.05.100

Description

Read heating systems stock and sales in Poland from SPIUG

Usage

readSPIUG()

Value

magpie object

Author(s)

Robin Hasse

Description

Read TCEP data base

Usage

readTCEP(subtype = "enduse")

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 heating systems sales in France from Uniclima

Usage

readUniclima()

Value

magpie object

Author(s)

Robin Hasse

Description

multiple EU studies mostly on different categories of appliances

Usage

readVHK(subtype)

Arguments

Value

MagPIE object

Author(s)

Robin Hasse

Description

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

Usage

readWEO(subtype)

Arguments

Author(s)

Hagen Tockhorn

Description

Introduce refrigerators as appliances

Usage

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

Arguments

Author(s)

Hagen Tockhorn

Description

Aggregate BRICK input data to lower granularity

Usage

toolAggregateBrick(x, granularity = NULL, weight = NULL)

Arguments

Value

named list with aggregated value and weights

Author(s)

Robin Hasse

Description

Calculate Shares

Usage

toolCalcShares(df, colShare)

Arguments

Value

data.frame with calculated shares for colShare

Author(s)

Hagen Tockhorn

Description

Extrapolate from data on selected technologies to all technologies required

Usage

toolCompleteRenFlows(x, from, to)

Arguments

Details

We assume that there is a high status quo probability that people just do identical replacement. If they don't, they chose the system with probabilities proportional to the general shares.

Value

MagPIE object

Author(s)

Robin Hasse

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,
  forceShares = 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

Provides all mappings needed to aggregate BRICK input data to a lower granularity.

Usage

toolGetBrickMapping(granularity)

Arguments

Value

named list of brick dimension aggregation mappings

Author(s)

Robin Hasse

Description

Takes MAgPIE object with a variable dimension and a unit dimension, and converts the unit according to a unit conversion table.

Usage

toolUnitConversion(x, unitConversion, removeUnit = FALSE)

Arguments

Author(s)

Robin Hasse