(S)FTP access
Once you receive your credentials, you can access our FTP through:
Host: calc1.cor-e.fr
(FTP) Port: 21
(SFTP) port: 2222
Coverage
Country | Live since | Wind | Solar | Demand | Price |
Austria | 2019 | ✅ | ✅ | ✅ | ✅ |
Belgium | 2019 | ✅ | ✅ | ✅ | ✅ |
Croatia | 2024 |
|
|
| ✅ |
Czech Republic | 2023 | ✅ | ✅ | ✅ | ✅ |
Hungary | 2024 |
|
|
| ✅ |
France | 2019 | ✅ | ✅ | ✅ | ✅ |
Germany | 2019 | ✅ | ✅ | ✅ | ✅ |
Italy | 2021 | ✅ | ✅ | ✅ | PUN + North ✅ |
Netherlands | 2019 | ✅ | ✅ | ✅ | ✅ |
Poland | 2023 | ✅ | ✅ | ✅ | ✅ |
Romania | 2024 |
|
|
| ✅ |
Spain | 2021 | ✅ | ✅ | ✅ | ✅ |
Slovakia | 2024 |
|
|
| ✅ |
Slovenia | 2024 |
|
|
| ✅ |
Switzerland | 2021 | ✅ | ✅ | ✅ | ✅ |
United Kingdom | 2020 | ✅ | ✅ | ✅ | ✅ |
Documentation of wind, solar and demand forecasts
Kpler short term models rely on two external weather forecast providers:
ECMWF (a.k.a. EC) - European Centre for Medium-Range Weather Forecasts
GFS - Global Forecast System of the National Oceanic and Atmospheric Administration
Each weather provider generates 4 weather runs per day (00/06/12/18) for 2 models. There is one deterministic model called OP and one ensemble providing several scenarios (30 for GFS and 50 for EC). The table below contains the update intervals of our forecasts.
Note that in addition to new weather runs, at Kpler we make additional updates for price only (cf UPD in the table) 4 times a day with the latest information available. These updates rewrite the file for the given run. Example for run 00:
a new file is generated between 6 and 7am UTC
it is then updated a first time at 10am CET
it receives a final update around 11.15am CET
Points in time seen from the latest updated are available on the FTP. The files are not rewritten after that point.
The timings on the table above are for the first forecasted hour, then it can take up to ~1h to generate a value for the last forecasted hour.
Access on the FTP
Paths for each dataset:
wind generation:
4_Supply/Forecast/Generation/COR_E/{Country}/yyyy/mmsolar generation:
4_Supply/Forecast/Generation/COR_E/{Country}/yyyy/mmdemand:
3_Demand/Forecast/Load/COR_E/{Country}/yyyy/mmSpot price:
2_Price/Forecast/Day_Ahead/COR_E/{Country}/yyyy/mm
File naming convention:
wind & solar generation:
CORE_SUPPLY_FORECAST_Generation_CORE_{Country}_{Wind|Solar}_Hourly_{EC|GFS}_{OP|ENS}_yyyymmdd{00|06|12|18}.csvdemand:
CORE_DEMAND_FORECAST_Load_CORE_{Country}_Hourly_{EC|GFS}_{OP|ENS|ENS_SCENARIOS}_yyyymmdd{00|06|12|18}.csvSpot price:
CORE_PRICE_FORECAST_DayAhead_CORE_{Country}_Hourly_{EC|GFS}_{OP|ENS|ENS_SCENARIOS}_yyyymmdd{00|06|12|18}.csv
For Ensemble forecasts, two files are published for each run:
one
{ENS}containing the control scenario "0"one
{ENS_SCENARIOS} containing the remaining scenarios (30 for GFS and 50 for EC)
Price modelling methodology
Our model is combining machine learning and time serie analysis to predict hourly prices based on specific market conditions (production, consumption, fuel prices, interconnector capacity).
This artificial intelligence method called machine learning is a method based on fundamental analysis that is effective in predicting different products based on the evolution of historical data and by combining several parameters. To predict the wholesale market price we use the following variables:
Annual shape and level of electricity demand
Installed capacities of renewable energies
Fossil and nuclear power plant capacities
Fuel (gas, coal, carbon) prices
Interconnector constraints
From the changes of these different variables, our model is able to predict for each of the future hours a balance between supply and demand on the merit order for the wholesale market and the bid curves for the reserve prices as it has been studied historically.
In order to avoid the “black box effect” our modeling is performed in two steps:
Conversion of raw weather data (°C, wind m/s, solar Watts of radiation, precipitations, cloud cover etc..) into forecasts of consumption, solar production, renewable production
Conversion of these MW into €/MWh (prices forecast)
Every country model is fed with variables from surrounding countries, but is built and runs independently. Our short term models run using GFS & EC weather data, both Operational and Ensemble scenarios. It gets updated and published every time a new full day of weather forecast is made available
Our models are retrained on a daily basis over an ever extending window going from 2015 to the most recent actual value.