test cases independent of capacityexpansion.jl

Author: holgerteichgraeber

.travis.yml

@@ -19,7 +19,7 @@ jobs:
       script:
         - julia --project=test/ -e 'using Pkg; Pkg.develop(PackageSpec(path=pwd())); Pkg.instantiate()'
         - julia --project=test/ test/clustering.jl
-        - julia --project=test/ test/capacityexpansion.jl
+        - julia --project=test/ test/test_workflow_introduction.jl
       after_success:
     - stage: "Documentation"
       julia: 1.0

test/test_workflow_introduction.jl

@@ -0,0 +1,110 @@
+# To use this package
+using ClustForOpt
+using Test
+
+@testset "workflow_intro" begin
+
+  #########################
+  #= Load Time Series Data
+  #########################
+  How to load data provided with the package:
+  The data is for a Capacity Expansion Problem "CEP"
+  and for the single node representation of Germany "GER_1"
+  The original timeseries has 8760 entries (one for each hour of the year)
+  It should be cut into K=365 periods (365 days) with T=24 timesteps per period (24h per day) =#
+  data_path=normpath(joinpath(dirname(@__FILE__),"..","data","TS_GER_1"))
+  ts_input_data = load_timeseries_data(data_path; T=24, years=[2016])
+
+  #= ClustData
+  How the struct is setup:
+      ClustData{region::String,K::Int,T::Int,data::Dict{String,Array},weights::Array{Float64},mean::Dict{String,Array},sdv::Dict{String,Array}} <: TSData
+  -region: specifies region data belongs to
+  -K: number of periods
+  -T: time steps per period
+  -data: Data in form of a dictionary for each attribute `"[file name]-[column name]"`
+  -weights: this is the absolute weight. E.g. for a year of 365 days, sum(weights)=365
+  -mean: For normalized data: The shift of the mean as a dictionary for each attribute
+  -sdv: For normalized data: Standard deviation as a dictionary for each attribute
+
+  How to access a struct:
+      [object].[fieldname]                                                      =#
+  number_of_periods=ts_input_data.K
+  # How to access a dictionary:
+  data_solar_germany=ts_input_data.data["solar-germany"]
+  # How to plot data
+  #using Plots
+  # plot(Array of our data, no legend, dotted lines, label on the x-Axis, label on the y-Axis)
+  #plot_input_solar=plot(ts_input_data.data["solar-germany"], legend=false, linestyle=:dot, xlabel="Time [h]", ylabel="Solar availability factor [%]")
+
+  # How to load your own data:
+  # put your data into your homedirectory into a folder called tutorial
+  # The data should have the following structure: see ClustForOpt/data folder
+  #=
+  - Loading all `*.csv` files in the folder or the file `data_path`
+  The `*.csv` files shall have the following structure and must have the same length:
+  |Timestamp |[column names...]|
+  |[iterator]|[values]         |
+  The first column should be called `Timestamp` if it contains a time iterator
+  The other columns can specify the single timeseries like specific geolocation.
+  Each column in `[file name].csv` file will be added to the ClustData.data called `"[file name]-[column name]"`
+  - region is an additional String to specify the loaded time series data
+  - K describes the number of periods in the input data
+  - T describes the length of each period                             =#
+  load_your_own_data=false
+  if load_your_own_data
+      # Single file at the path e.g. homedir/tutorial/solar.csv
+      # It will automatically call the data 'solar' within the datastruct
+      my_path=joinpath(homedir(),"tutorial","solar.csv")
+      your_data_1=load_timeseries_data(my_path; region="none", T=24)
+      # Multiple files in the folder e.g. homedir/tutorial/
+      # Within the data struct, it will automatically call the data the names of the csv filenames
+      my_path=joinpath(homedir(),"tutorial")
+      data_path=normpath(joinpath(dirname(@__FILE__),"..","data","TS_GER_18"))
+      your_data_2 = load_timeseries_data(data_path; T=24, years=[2015])
+  end
+
+
+  #############
+  # Clustering
+  #############
+  # Quick example and investigation of the best result:
+  ts_clust_result = run_clust(ts_input_data; method="kmeans", representation="centroid", n_init=5, n_clust=5) # note that you should use n_init=1000 at least for kmeans.
+  ts_clust_data = ts_clust_result.clust_data
+  # And some plotting:
+  #plot_comb_solar=plot!(plot_input_solar, ts_clust_data.data["solar-germany"], linestyle=:solid, width=3)
+  #plot_clust_soar=plot(ts_clust_data.data["el_demand-germany"], legend=false, linestyle=:solid, width=3, xlabel="Time [h]", ylabel="Solar availability factor [%]")
+
+
+  #= Clustering options:
+  `run_clust()` takes the full `data` and gives a struct with the clustered data as the output.
+
+
+  ## Supported clustering methods
+  The following combinations of clustering method and representations are supported by `run_clust`:
+
+  Name                                                | method            | representation
+  ----------------------------------------------------|-------------------|----------------
+  k-means clustering                                  | `<kmeans>`        | `<centroid>`
+  k-means clustering with medoid representation       | `<kmeans>`        | `<medoid>`
+  k-medoids clustering (partitional)                  | `<kmedoids>`      | `<medoid>`
+  k-medoids clustering (exact) [requires Gurobi]      | `<kmedoids_exact>`| `<medoid>`
+  hierarchical clustering with centroid representation| `<hierarchical>`  | `<centroid>`
+  hierarchical clustering with medoid representation  | `<hierarchical>`  | `<medoid>`
+
+  ## Other input parameters
+
+  The input parameter `n_clust` determines the number of clusters,i.e., representative periods.
+
+  `n_init` determines the number of random starting points. As a rule of thumb, use:
+  `n_init` should be chosen 1000 or 10000 if you use k-means or k-medoids
+  `n_init` should be chosen 1 if you use k-medoids_exact or hierarchical clustering
+
+  `iterations` is defaulted to 300, which is a good value for kmeans and kmedoids in our experience. The parameter iterations does not matter when you use k-medoids exact or hierarchical clustering.
+
+  =#
+
+  # A clustering run with different options chosen as an example
+  ts_clust_result_2 = run_clust(ts_input_data; method="kmedoids", representation="medoid", n_init=100, n_clust=4, iterations=500)
+
+  @test 0==0
+end