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Commit 21de23c7 authored by András KÁDÁR's avatar András KÁDÁR
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minor changes in Readme

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# USER DEFINED PARAMETERS ----
path_thisScript = "C:/Users/kadara/Documents/R_packages/dft-results-validation/R"
path_data = "C:/Users/kadara/Documents/R_packageData/dft-results-validation/results_HU00_so2025_final_run5/inputs" # path where all the DFT raw (rescaling) reports are located (in separate folders per MN)
path_report = "C:/Users/kadara/Documents/R_packageData/dft-results-validation/results_HU00_so2025_final_run5/reports" # path where to save the reports by MN
path_data = "C:/Users/kadara/Documents/R_packageData/dft-results-validation/test1" # path where all the DFT raw (rescaling) reports are located
path_report = "C:/Users/kadara/Documents/R_packageData/dft-results-validation/test1/report" # path where to save the reports by MN
climateYears.toProcess = c(2025:2060) # which climate years to include in the reports
targetYears.toProcess = c(2025) # which target years to include in the reports
sep_dataFolder_for_MNs = FALSE # TRUE: if the result files are located in a separate folder for every MN; FALSE: if the result files are located in a common folder
sep_dataFolder_for_MNs = FALSE # TRUE: if the DFT rescaling report files are located in a separate folder for every MN; FALSE: all files are in a common folder
# SETTINGS ----
......
......@@ -12,74 +12,112 @@ The purpose of this repo is to collect the reporting scripts for the DFT
results validation.
<details>
<summary>Previous statuses</summary>
### Status 2023-07-18
The original R script and Rmd report generator file is uploaded.
(created by PSE colleagues)
<summary>Previous statuses</summary>
### Status 2023-07-28
### Status 2023-07-18
New branch was created (**results_validation_wo2324**) which contains the
updated scripts designed for Winter Outlook 2023/2024.
The report has the following parts:
The original R script and Rmd report generator file is uploaded.
(created by PSE colleagues)
- `Peaks summary table` : descriptive stats of the CY-dependent monthly peaks in the WO2324 period
- `Peaks density` : visualisation of the CY-dependent monthly peaks in the WO2324 period
- `Plots all CY` : line plot of the predicted hourly load time series (for all the selected CYs)
- `Weekly plots` : separate line plot for each selected CY (all weeks on one chart)
### Status 2023-07-28
New branch was created (**results_validation_wo2324**) which contains
the updated scripts designed for Winter Outlook 2023/2024. The report
has the following parts:
### Status 2023-08-03
- `Peaks summary table` : descriptive stats of the CY-dependent
monthly peaks in the WO2324 period
- `Peaks density` : visualisation of the CY-dependent monthly peaks in
the WO2324 period
- `Plots all CY` : line plot of the predicted hourly load time series
(for all the selected CYs)
- `Weekly plots` : separate line plot for each selected CY (all weeks
on one chart)
New section was added to the report:
### Status 2023-08-03
- `Daily curves (calendar format)` : separate plot for each month (all CYs' daily load curves, calendar format)
New section was added to the report:
Besides, it is now possible to zoom-in to the plots in the html output. (R package: rintimg)
- `Daily curves (calendar format)` : separate plot for each month (all
CYs' daily load curves, calendar format)
</details>
Besides, it is now possible to zoom-in to the plots in the html output.
(R package: rintimg)
### Status 2023-12-02
A separate report was created for long-term studies (in this case for ERAA 2024). It is called **Report_for_MN_LAC.Rmd**.
The report has the following parts:
A separate report was created for long-term studies (in this case for
ERAA 2024). It is called **Report_for_MN_LAC.Rmd**. The report has the
following parts:
- `Yearly demand and peaks` :
- yearly demand table (summary statistics by targetYear)
- yearly demand (histogram by targetYear)
- yearly peak table (summary statistics by targetYear)
- yearly peak (histogram by targetYear)
- yearly demand table (summary statistics by targetYear)
- yearly demand (histogram by targetYear)
- yearly peak table (summary statistics by targetYear)
- yearly peak (histogram by targetYear)
- `Monthly peaks` :
- monthly peak table (summary statistics by targetYear+month)
- monthly peak (points by targetYear+month)
- `AVG load + load factors` :
- daily avg load per climateYear (line plot by targetYear)
- yearly load factors (histogram by targetYear)
- monthly load factors (points by month)
- `Load curves` :
- hour of peaks (points as densities by targetYear)
- representative curves (lineplot by targetYear)
Additionally, separate plots are created showing the predicted daily
curves. (Saved as PNG files in separate folder under **path_report**) -
`Calendar plots` : daily curves by targetYear + month in a monthly
calendar format - `Weekly curves` : weekly curves by climateYear
- `Monthly peaks` :
- monthly peak table (summary statistics by targetYear+month)
- monthly peak (points by targetYear+month)
- `AVG load + load factors` :
- daily avg load per climateYear (line plot by targetYear)
- yearly load factors (histogram by targetYear)
- monthly load factors (points by month)
</details>
- `Load curves` :
- hour of peaks (points as densities by targetYear)
- representative curves (lineplot by targetYear)
### Status 2025-02-18
Same Rmd file creates the reports for short-term and long-term studies
**Report_for_MN.Rmd**. Older versions of this .Rmd file can be found in
the archive folder.
Additionally, separate plots are created showing the predicted daily curves. (Saved as PNG files in separate folder under **path_report**)
- `Calendar plots` : daily curves by targetYear + month in a monthly calendar format
- `Weekly curves` : weekly curves by climateYear
The report has the following parts:
- `Yearly demand and peaks` :
- yearly demand table (summary statistics by targetYear)
- yearly demand (histogram by targetYear)
- yearly peak table (summary statistics by targetYear)
- yearly peak (histogram by targetYear)
- `Monthly peaks` :
- monthly peak table (summary statistics by targetYear+month)
- monthly peak (points by targetYear+month)
- `AVG load + load factors` :
- daily avg load per climateYear (line plot by targetYear)
- yearly load factors (histogram by targetYear)
- monthly load factors (points by month)
- `Load curves` :
- hour of peaks (points as densities by targetYear)
- representative curves (lineplot by targetYear)
Additionally, separate plots are created showing the predicted daily
curves. (Saved as PNG files in separate folder under **path_report**) -
`Calendar plots` : daily curves by targetYear + month in a monthly
calendar format - `Weekly curves` : weekly curves by climateYear
## Preliminary settings
### Folder structure
**DATA**
**DATA**
Before running the script make sure to store the DFT rescaling reports
in a structured way. This is how Data&Models team collect these reports
on Sharefile. For example the following structure is good:
in a structured way. Basically, there are two possible ways to do that.
#### Case 1
You can see that the reports are in separate folders by Market Node.
Only one additional layer in the folder hierarchy is allowed: if needs
to be called ENTSOE. This is how Data&Models team collect these reports
on Sharefile.
├── [path_data (the root folder where you stored the data)]
│ ├── ENTSOE
......@@ -96,14 +134,28 @@ on Sharefile. For example the following structure is good:
│ │ ├── rescaling_report_UK00_2023_NationalTrends.xlsx
│ │ ├── rescaling_report_UK00_2024_NationalTrends.xlsx
**REPORTS**
#### Case 2
The DFT rescaling reports are simply collected in a single common
folder.
├── rescaling_report_AL00_2023_NationalTrends.xlsx
├── rescaling_report_AL00_2024_NationalTrends.xlsx
.
.
.
├── rescaling_report_UK00_2024_NationalTrends.xlsx
├── rescaling_report_UK00_2024_NationalTrends.xlsx
**REPORTS**
Also make sure to create a new folder for the reports. It has to be
different than the root folder for the data.
├── [path_report (the root folder where you want to have the reports)]
When saving the reports the script will create a subfolder for each processed market node. Thus, the folder structure will be the following:
When saving the reports the script will create a subfolder for each
processed market node. Thus, the folder structure will be the following:
├── [path_report (the root folder where you want to have the reports)]
│ ├── AL00
......@@ -115,7 +167,21 @@ When saving the reports the script will create a subfolder for each processed ma
│ ├── UK00
│ │ ├── report_wo2324_UK00_[YYYYMMDD].html
Please note that the script **will not create** an intermediate ENTSOE folder for the relevant market nodes.
Please note that the script **will not create** an intermediate ENTSOE
folder for the relevant market nodes even if the DFT rescaling files
were placed as described in *Case 1*.
### DFT Rescaling report files
There are some things to note regarding the DFT rescaling files:
- The name of the file:
- it needs to start with the prefix *rescaling_report*
- the included market nodes need to be listed between
*rescaling_report* and *Demand_Total* seperated by `_`
(underscore)
- The name of the sheets must be the same as the market node names in
the filename.
### Parameters
......@@ -125,12 +191,15 @@ running the script:
- `path_thisScript` : this is the location (full path) of the scripts
- `path_data` : this is the root folder (full path) of the data (see
the folder structure above)
- `path_report` : this is the folder (full path) where the script will store the reports (it
needs to be a separate folder)
- `climateYears.toProcess` : these are the *climate years* that we want to include in the report.
- `targetYears.toProcess` : these are the *target years* that we want to include in the report.
- `sep_dataFolder_for_MNs` : logical, **TRUE** if the load files are located in a *separate folder* for every MN; **FALSE** if the load files are located in a *common folder*
- `path_report` : this is the folder (full path) where the script will
store the reports (it needs to be a separate folder)
- `climateYears.toProcess` : these are the *climate years* that we
want to include in the report.
- `targetYears.toProcess` : these are the *target years* that we want
to include in the report.
- `sep_dataFolder_for_MNs` : logical, **TRUE** if the load files are
located in a separate folder for every MN (*Case 1*); **FALSE** if
the load files are located in a common folder (*Case 2*)
## Known issues
......
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