ENERGY 2100 User Manual

Overview

To view variables in the REPL, you must first activate the model's Julia environment, load ENERGY 2100's Julia package, import the packages you want to use, and import the model's global structures you want to use.

Once you have a VS Code session set up, you are ready to view variables from the REPL.

There are multiple ways to view variables from the Julia REPL, including: 

• Write variable to a CSV file using a DataFrame
• View variable in REPL using a DataFrame subset
• Write println statements in the REPL
• Run an existing output .jl file from the REPL.

The sections below provide instructions for each of these methods. 

The instructions that follow assume you have performed all the steps above to import the required functions and packages into your Julia workspace.

Write Variable to a CSV file using a DataFrame

This section provides instructions on writing to CSV files by first assigning the variable to a DataFrame, then using CSV.write to save it to a file.

To write variables to a CSV file from the Julia REPL, follow the instructions below:

1. Assign the desired variable to a DataFrame:

The example below shows a DataFrame of the residential xPkSav variable, which is stored on RInput in the database:

2. Use the CSV.write function to save the DataFrame (df) to a file:

• You can append the variable to an existing csv file by using append = true.

• If you don’t want to read any zero values, you can set the skip_zeros keyword argument to true:

df = ReadDisk(DataFrame,DB,"DBPartition/VariableName",skip_zeros = true)

df = ReadDisk(DataFrame,DB,"RInput/xPkSav",skip_zeros = true)

View Variable in REPL Using a DataFrame Subset

Most variable arrays in ENERGY 2100 are too large to view in their entirety as a DataFrame directly in the REPL. However, you can see a slice of the variable directly in the REPL using the @rsubset macro (from the DataFramesMeta package). 

The @rsubset macro allows you to filter rows of a DataFrame. To view a slice of a variable in the REPL, follow the instructions below:

1. Save the variable as a DataFrame:

2. Create a subset of the DataFrame by selecting specific elements of the variable's sets: 

df_subset = @rsubset df begin
:EC == "SingleFamilyDetached"
:Enduse == "Light"
:Area == "BC"
:Year == 2030
end

To select more than one set element in a given set, use the logical "or" operator: (||):

df_subset = @rsubset df begin
:EC == "SingleFamilyDetached" || "SingleFamilyAttached" || "MultiFamily"
:Enduse == "Light"
:Area == "BC"
:Year == 2030
end

Alternatively, to select more than one set element in a given set, you can also use the in operator, which acts as a filter to check if elements in a DataFrame column exist within a specified collection:

df_subset = @rsubset df begin
:EC in ["SingleFamilyDetached","SingleFamilyAttached","MultiFamily"]
:Enduse == "Light"
:Area == "BC"
:Year == 2030
end

To select a range of years, use the less than or equal to operator (<=): 

df_subset = @rsubset df begin
:EC in ["SingleFamilyDetached","SingleFamilyAttached","MultiFamily"]
:Enduse == "Light"
:Area == "BC"
2028 <= :Year <= 2030
end

3. View a DataFrame or DataFrame subset just by typing the name of the DataFrame into the REPL:

julia> df
46900×5 DataFrame
   Row │ Enduse   EC                    Area    Year   Value   
       │ String   String                String  Int64  Float32
───────┼───────────────────────────────────────────────────────
     1 │ Heat     SingleFamilyDetached  ON       1985     99.0
     2 │ HW       SingleFamilyDetached  ON       1985     99.0
     3 │ OthSub   SingleFamilyDetached  ON       1985     99.0
     4 │ Refrig   SingleFamilyDetached  ON       1985     99.0
     5 │ Light    SingleFamilyDetached  ON       1985     99.0
   ⋮   │    ⋮              ⋮              ⋮       ⋮       ⋮
 46897 │ Refrig   OtherResidential      ROW      2051     99.0
 46898 │ Light    OtherResidential      ROW      2051     99.0
 46899 │ AC       OtherResidential      ROW      2051     99.0
 46900 │ OthNSub  OtherResidential      ROW      2051     99.0
                                             46891 rows omitted

julia> df_subset
9×5 DataFrame
 Row │ Enduse  EC                    Area    Year   Value   
     │ String  String                String  Int64  Float32
─────┼──────────────────────────────────────────────────────
   1 │ Light   SingleFamilyDetached  BC       2028  4.77305
   2 │ Light   SingleFamilyAttached  BC       2028  1.12756
   3 │ Light   MultiFamily           BC       2028  1.89535
   4 │ Light   SingleFamilyDetached  BC       2029  5.02847
   5 │ Light   SingleFamilyAttached  BC       2029  1.20767
   6 │ Light   MultiFamily           BC       2029  2.05367
   7 │ Light   SingleFamilyDetached  BC       2030  5.00835
   8 │ Light   SingleFamilyAttached  BC       2030  1.22271
   9 │ Light   MultiFamily           BC       2030  2.102

Write println Statements in the REPL

To view a variable in the REPL using println, first read the variable and its sets into your workspace, then loop through the variable's sets and use the Julia println as described in the instructions below.

1. Identify the sets of the variable you want to view. 

You can find out more about a variable's sets by importing and using the attributes function data_attrs:

The example below shows the results of calling the data attributes function for the variable xPkSav in the industrial sector (stored on IInput):

julia> import EnergyModel.data_attrs
julia> data_attrs(DB, "IInput/xPkSav")

Dict{String, Any} with 7 entries:
  "units"             => "MW"
  "doc"               => "Exogenous Peak Savings (MW)"
  "setdesc_locations" => ["IInput/EnduseDS", "IInput/ECDS", "MainDB/AreaDS", "MainDB/YearDS"]
  "dims"              => ["Enduse", "EC", "Area", "Year"]
  "size"              => (6, 42, 25, 67)
  "ndims"             => 4
  "type"              => "variable"
  

From the output of data_attrs, we see that xPkSav is defined by the sets  "Enduse", "EC", "Area", and "Year".

2. Read the desired variable and sets into your workspace. 

By importing and using ReadDiskAndSets, you will read in the variable and sets to the names you specify on the left-hand-side of the equation as follows:

import EnergyModel.ReadDiskAndSets
(;VariableName, Set1Name, Set2Name, ...) = ReadDiskAndSets(DB, "DBPartition/VariableName")

julia> import EnergyModel.ReadDiskAndSets
julia> (;xPkSav,Enduse,EC,Area,Year) = ReadDiskAndSets(DB, "IInput/xPkSav")

3. Select sets and write desired variable values using println:

set1s = Select(Set1,["Set1Key","Set2Key",...])
set2s = Select(Set2,"Set2Key")
years = collect(Yr(year1):Yr(yearlast))
for enduse in set1 in set1s, set2 in set2s,...,year in years
    println("VariableName(",Set1[set1],",",Set2[set2],",",Year[year],")=", VariableName[set1,set2,...,year])
end

enduses = Select(Enduse,"Heat")
ecs = Select(EC,["PulpPaperMills","IronSteel"])
areas = Select(Area,"BC")
years = collect(Yr(2024):Yr(2026))
for enduse in enduses, ec in ecs, area in areas, year in years
    println("xPkSav(",Enduse[enduse],",",EC[ec],",",Area[area],",",Year[year],")=", xPkSav[enduse,ec,area,year])
end

xPkSav(Heat,PulpPaperMills,BC,2024) = 0.0
xPkSav(Heat,PulpPaperMills,BC,2025) = 1.2080482
xPkSav(Heat,PulpPaperMills,BC,2026) = 1.1113358
xPkSav(Heat,IronSteel,BC,2024) = 0.0
xPkSav(Heat,IronSteel,BC,2025) = 0.1035406
xPkSav(Heat,IronSteel,BC,2026) = 0.0951572

Run an Existing Output .jl File from the REPL

To run an output file from the REPL, follow the instructions below:

1. Include the path to the desired output file in your workspace:

2. Call the control function that is at the bottom of the output file:

Output will be generated and saved with .dta extensions in the \2020Model\out folder.

include(Output/ExcelOutput/ElectricSummary.jl")
ElectricSummary_DtaControl(DB)