Correlation demand files

It is possible to use macrodrivers as a way to infer the service demand. For example, one can use the expected GDP based on purchasing power parity (GDP PPP) and population in the future per region to infer the service demand using a regressor.

To do this, a minimum of three files are required:

  1. A macrodrivers file

  2. A file which states the regression parameters

  3. A file which dictates how the demand per benchmark year is split across the timeslices.

We will go into the details of each of these files below.

Macrodrivers

An example of a shortened macrodriver file is shown below. This file contains the data for each of the years you are interested in. For example, in the file below, it contains GDP PPP in region R1, in the unit millionUS$2015 for each year. It also contains the data for the population.

Macrodrivers

Variable

RegionName

Unit

2010

2011

GDP|PPP

R1

millionUS$2015

1206919

1220599

Population

R1

million

80.0042

81.82599

Variable

This is the variable that you would like to use in the regression for the service demand.

RegionName

This is the region that the data applies to. This must correlate with the regions set in the rest of your input files, as well as the toml file.

Unit

This unit can be whatever you like, however they must be consistent across all input files.

2010, 2011, …

This is the quantity of the variable per year of the simulation.

Regression Parameters

In the regression parameters file, it is necessary to state the parameters of the regression. This can be obtained from your own dataset, where you regress the service demand against GDP PPP and populaiton, for example.

An example file is shown below:

Regression Parameters File

SectorName

FunctionType

Coeff

RegionName

electricity

gas

heat

CO2f

Residential

logistic-sigmoid

GDPexp

R1

0

0

9.94E-02

0

Residential

logistic-sigmoid

constant

R1

0

0

0.0000434

0

Residential

logistic-sigmoid

GDPscaleLess

R1

0

0

753.1068725

0

Residential

logistic-sigmoid

GDPscaleGreater

R1

0

0

672.9316672

0

SectorName

This is the sector name in which these parameters apply to.

FunctionType

This is the function type you would like to MUSE to use. MUSE allows these to be:

  • Exponential

  • ExponentialAdj

  • Logistic

  • Loglog

  • LogisticSigmoid

  • Linear

  • endogenous_demand

Your own functions can be created using the @register_regression hook, from the regressions.py file.

Coeff

This is the coefficient for the respective function type. These are explicitly defined within the regressions.py file, as they differ between functions.

RegionName

This is the region in which these parameters apply to.

Energy service (electricity, gas, heat, CO2f)

Here you can specify the coefficients for the expected demand for the respective commodity.

Timeslice share

In this file, you are able to split the energy service proportionally by timeslice.

An example file is shown below:

Timeslice share

SN

RegionName

electricity

gas

heat

CO2f

wind

1

R1

0

0

0.034835

0

0

2

R1

0

0

0.064546

0

0

3

R1

0

0

0.044569

0

0

4

R1

0

0

0.011161

0

0

5

R1

0

0

0.014145

0

0

6

R1

0

0

0.085783

0

0

SN

This is the timeslice index.

RegionName

This is the region in question for this data.

Energy service (electricity, gas, heat, CO2f, wind)

Here you specify the proportion of each energy service for each timeslice.