This R package provides functions to crawl energy system data of the European Network of Transmission System Operators (ENTSO-E) at https://transparency.entsoe.eu. It is build as part of my master thesis to analyze the call durations of secondary control reserve (SCR) depending on the offered energy price. The aim is to forecast such call durations to improve existing energy management optimization algorithms which relay on assumptions and are not very accurate. For further information please get in contact or follow my blog (https://wagnertimo.github.io).
When installing this package you should at least use the R version 3.3.0 (2016-05-03). For the library dependecies see the section below. You can easily install this R package by using the install_github() function from the devtools package:
library(devtools)
install_github("wagnertimo/entsoecrawlR")Before using this R package, please check that you have installed the following R packages. Normally, with the installation of the package those dependencies will also be installed. If not, you have to do it manually.
httrxml2XMLzoolubridatetimeDatedplyrtidyrmagrittrdata.tablelogging
Total Load - Day Ahead / Actual: The function getLoadDayAheadVsActual() is implemented to retrieve the actual and forecasted total loads of each TSO and their common load values of the Netzregelverbund (source: https://transparency.entsoe.eu/load-domain/r2/totalLoadR2/show). The values are in MW in a resolution of 15 minutes where the timestamp represents the start of the quarter hour. The data is returned by days. If you only want minutes within the day, pplease subset the data yourself. The code snippet below gives an example. It is important not forgetting to activate the library and set logging off or on.
# Activate the library
library(entsoecrawlR)
# Set logging. Creates also a loggin file in the workspace. Forgetting to set a value will break the codes.
setLogging(TRUE)
# Retrieve the Load data for 2015-04-01 to 2017-05-31
loadData <- getLoadDayAheadVsActual("2015-04-01", "2017-05-31")
head(loadData)
# Output:
# DateTime ForecastLoad_50Hz ActualLoad_50Hz ForecastLoad_Amprion ActualLoad_Amprion ForecastLoad_TenneT ActualLoad_TenneT
# 1 2015-04-01 00:00:00 7519 8832 19777 20149 16736 17371
# 2 2015-04-01 00:15:00 7390 8232 19485 20112 16756 16841
# 3 2015-04-01 00:30:00 7301 7865 19247 19753 16492 16373
# 4 2015-04-01 00:45:00 7161 7840 19019 19706 16320 16322
# 5 2015-04-01 01:00:00 7100 7754 18870 19418 16146 15947
# 6 2015-04-01 01:15:00 7056 8110 18739 19230 15963 15808
# ForecastLoad_TransnetBW ActualLoad_TransnetBW ForecastLoad_Netzregelverbund Actuaload_Netzregelverbund
# 1 6589 6712 50621 53064
# 2 6463 6457 50094 51642
# 3 6261 6344 49301 50335
# 4 6197 6167 48697 50035
# 5 6071 6135 48187 49254
# 6 5994 5974 47752 49122
Generation Forecasts - Day Ahead for Wind and Solar: The function getWindSolarDayAheadGeneration() is implemented to retrieve the forecasted Wind (Onshore, Offshore) and Solar generation of each TSO and their common generation energy of the Netzregelverbund (source: https://transparency.entsoe.eu/generation/r2/dayAheadGenerationForecastWindAndSolar/show). The values are in MW in a resolution of 15 minutes where the timestamp represents the start of the quarter hour. The data is returned by days. If you only want minutes within the day, pplease subset the data yourself. The code snippet below gives an example. It is important not forgetting to activate the library and set logging off or on.
# Activate the library
library(entsoecrawlR)
# Set logging. Creates also a loggin file in the workspace. Forgetting to set a value will break the codes.
setLogging(TRUE)
# Retrieve the forecasted Wind and Solar generation data for 2015-04-01 to 2017-05-31
windSolarData <- getWindSolarDayAheadGeneration("2015-04-01", "2017-05-31")
str(windSolarData)
# Output:
# 'data.frame': 96 obs. of 21 variables:
# $ DateTime : POSIXct, format: "2017-01-01 00:00:00" ...
# $ Sum_Generation_Forecast50Hz : num 5253 5221 5190 5164 5139 ...
# $ Solar_Generation_Forecast_50Hz : num 0 0 0 0 0 0 0 0 0 0 ...
# $ WindOffshore_Generation_Forecast_50Hz : num 331 331 331 331 331 331 331 331 331 331 ...
# $ WindOnshore_Generation_Forecast_50Hz : num 4922 4890 4859 4833 4808 ...
# $ Sum_Generation_ForecastAmprion : num 1366 1369 1375 1380 1383 ...
# $ Solar_Generation_Forecast_Amprion : num 0 0 0 0 0 0 0 0 0 0 ...
# $ WindOffshore_Generation_Forecast_Amprion : num 0 0 0 0 0 0 0 0 0 0 ...
# $ WindOnshore_Generation_Forecast_Amprion : num 1366 1369 1375 1380 1383 ...
# $ Sum_Generation_ForecastTenneT : num 9645 9621 9603 9580 9117 ...
# $ Solar_Generation_Forecast_TenneT : num 0 0 0 0 0 0 0 0 0 0 ...
# $ WindOffshore_Generation_Forecast_TenneT : num 3009 3007 3008 3008 3039 ...
# $ WindOnshore_Generation_Forecast_TenneT : num 6636 6614 6595 6572 6078 ...
# $ Sum_Generation_ForecastTransnetBW : num 42 43 44 45 46 48 50 52 55 59 ...
# $ Solar_Generation_Forecast_TransnetBW : num 0 0 0 0 0 0 0 0 0 0 ...
# $ WindOffshore_Generation_Forecast_TransnetBW : num NA NA NA NA NA NA NA NA NA NA ...
# $ WindOnshore_Generation_Forecast_TransnetBW : num 42 43 44 45 46 48 50 52 55 59 ...
# $ WindOffshore_Generation_Forecast_Netzregelverbund: int 0 0 0 0 0 0 0 0 0 0 ...
# $ WindOnshore_Generation_Forecast_Netzregelverbund : int 0 0 0 0 0 0 0 0 0 0 ...
# $ Solar_Generation_Forecast_Netzregelverbund : num 0 0 0 0 0 0 0 0 0 0 ...
# $ Sum_Generation_Forecast_Netzregelverbund : num 16306 16254 16212 16169 15685 ...
Actual Generation per Production Type: The function getActualGeneration() is implemented to retrieve the actual power generation per product type of each TSO and their common generation energy of the Netzregelverbund (source: https://transparency.entsoe.eu/generation/r2/actualGenerationPerProductionType/show).
The product types are Biomass, Fossil Brown coal/Lignite, Fossil Coal-derived gas, Fossil Gas, Fossil Hard coal, Fossil Oil, Fossil Oil shale, Fossil Peat, Geothermal, Hydro Pumped Storage, Hydro Run-of-river and poundage, Hydro Water Reservoir, Marine, Nuclear, Other, Other renewable, Solar, Waste, Wind Offshore, Wind Onshore. For each product type the aggregated generation and consumption is returned, as well as the total generation over all prodution types for each TSO and the Netzregelverbund.
The values are in MW in a resolution of 15 minutes where the timestamp represents the start of the quarter hour. The data is returned by days. If you only want minutes within the day, pplease subset the data yourself. The code snippet below gives an example. It is important not forgetting to activate the library and set logging off or on.
# Activate the library
library(entsoecrawlR)
# Set logging. Creates also a loggin file in the workspace. Forgetting to set a value will break the codes.
setLogging(TRUE)
# Retrieve the actual generation and consumption for each TSO and the Netzregelverbund for 2015-04-01 to 2017-05-31. 208 Variables!
actualGen <- getActualGeneration("2015-04-01", "2017-05-31")
Generation Forecast - Day ahead: The function getForecastGeneration() is implemented to retrieve the day-ahead forecasted and aggregated power generation of each TSO and their common generation energy of the Netzregelverbund (source: https://transparency.entsoe.eu/generation/r2/dayAheadAggregatedGeneration/show).
The values are in MW in a resolution of one hour where the timestamp represents the start of the hour. The data is returned by days. If you only want minutes within the day, pplease subset the data yourself. The code snippet below gives an example. It is important not forgetting to activate the library and set logging off or on.
# Activate the library
library(entsoecrawlR)
# Set logging. Creates also a loggin file in the workspace. Forgetting to set a value will break the codes.
setLogging(TRUE)
# Retrieve the actual generation and consumption for each TSO and the Netzregelverbund for 2015-04-01 to 2017-05-31. 208 Variables!
forecastGen <- getForecastGeneration("2015-04-01", "2017-05-31")
head(forecastGen)
# Excerpt of the output:
#
# DateTime Forecast_Generation_50Hz Forecast_Consumption_50Hz Forecast_Generation_Amprion ....
# 1 2017-01-01 00:00:00 11312 NA 19583 ....
# 2 2017-01-01 01:00:00 11057 NA 18933 ....
# 3 2017-01-01 02:00:00 10849 NA 18931 ....
# 4 2017-01-01 03:00:00 10881 NA 19045 ....
# 5 2017-01-01 04:00:00 10204 NA 19352 ....
# 6 2017-01-01 05:00:00 9861 NA 19238 ....
Data for the energy loads (day-ahead forecast and actual loads) are only retrievable since 2015-01-01.