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Kpler Short term forecasts methodology

Wind, solar generation, demand and SPOT price forecasts up to 46 days ahead

Hamza Aourach avatar
Written by Hamza Aourach
Updated over a month ago

(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

Zone

Live since

Wind

Solar

Load

Price DA

Austria

2019

✅

✅

✅

✅

Belgium

2019

✅

✅

✅

✅

Bosnia and Herzegovina

2025

✅

✅

✅

Bulgaria

2025

✅

✅

✅

✅

Croatia

2024

✅

✅

✅

✅

Czech Republic

2023

✅

✅

✅

✅

DK1

2025

✅

✅

✅

✅

DK2

2025

✅

✅

✅

✅

Estonia

2025

✅

✅

✅

✅

Finland

2025

✅

✅

✅

✅

France

2019

✅

✅

✅

✅

Georgia

2025

✅

✅

Germany

2019

✅

✅

✅

✅

Greece

2025

✅

✅

✅

✅

Hungary

2024

✅

✅

✅

✅

Ireland

2025

✅

✅

Italy

2021

✅

✅

✅

✅

IT-Calabria

2025

✅

✅

✅

✅

IT-Centre-Nort

2025

✅

✅

✅

✅

IT-Centre-South

2025

✅

✅

✅

✅

IT-North

2025

✅

✅

✅

✅

IT-Sardinia

2025

✅

✅

✅

✅

IT-Sicily

2025

✅

✅

✅

✅

IT-South

2025

✅

✅

✅

✅

Latvia

2025

✅

✅

✅

✅

Lithuania

2025

✅

✅

✅

✅

Moldova

2025

✅

Montenegro

2025

✅

✅

Netherlands

2019

✅

✅

✅

✅

NO1

2025

✅

✅

✅

NO2

2025

✅

✅

✅

NO3

2025

✅

✅

✅

NO4

2025

✅

✅

✅

NO5

2025

✅

✅

Poland

2023

✅

✅

✅

✅

Portugal

2025

✅

✅

✅

✅

Romania

2024

✅

✅

✅

✅

SE1

2025

✅

✅

✅

✅

SE2

2025

✅

✅

✅

✅

SE3

2025

✅

✅

✅

✅

SE4

2025

✅

✅

✅

✅

Serbia

2025

✅

✅

✅

Slovakia

2024

✅

✅

✅

Slovenia

2024

✅

✅

✅

Spain

2021

✅

✅

✅

✅

Switzerland

2021

✅

✅

✅

✅

United Kingdom

2020

✅

✅

✅

✅

XK

2025

✅

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/mm

  • solar generation: 4_Supply/Forecast/Generation/COR_E/{Country}/yyyy/mm

  • demand: 3_Demand/Forecast/Load/COR_E/{Country}/yyyy/mm

  • Spot 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}.csv

  • demand: CORE_DEMAND_FORECAST_Load_CORE_{Country}_Hourly_{EC|GFS}_{OP|ENS|ENS_SCENARIOS}_yyyymmdd{00|06|12|18}.csv

  • Spot 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.

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