Course on AI Applied to Electricity Price Forecasting
Online 300 h course in AI applied to electricity price forecasting. 6 modules covering Regulation (EU) No 1227/2011 (REMIT), Regulation (EU) 2019/943 on the internal market for electricity and Python (statsmodels, LightGBM). Assigned tutor and open enrolment.
Take the controls.
Run a real engineering trade-off from this programme. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Understand the price formation mechanisms in European electricity markets.
Build datasets with fundamental and meteorological variables.
Develop statistical and machine learning forecasting models.
Generate probabilistic forecasts for different horizons.
Evaluate models using backtesting and appropriate metrics.
Apply forecasts to bidding, storage and hedging strategies.
Develop a decision-oriented price forecasting system.
Who it's for
- Electricity market analyst looking to specialise in AI applied to electricity price forecasting.
- Energy data scientist looking to specialise in AI applied to electricity price forecasting.
- Energy trader looking to specialise in AI applied to electricity price forecasting.
- Risk analyst at a retail supplier looking to specialise in AI applied to electricity price forecasting.
- Renewable asset valuation consultant looking to specialise in AI applied to electricity price forecasting.
- Energy optimisation engineer looking to specialise in AI applied to electricity price forecasting.
- Graduates in engineering or science seeking to enter energy commercialisation and trading.
What you'll work with
- Regulation (EU) No 1227/2011 (REMIT)
- Regulation (EU) 2019/943 on the internal market for electricity
- Regulation (EU) 2015/1222 (CACM)
- Regulation (EU) 2024/1689 on artificial intelligence
- Regulation (EU) 2024/1747 on the reform of electricity market design
- ISO/IEC 23894
- Directive (EU) 2019/944
6 modules · 300 h
1.1Day-ahead and intraday markets in Europe1.2Market coupling and the EUPHEMIA algorithm1.3Aggregated supply and demand curves1.4Marginal price and merit order1.5Balancing service markets1.6Negative prices and solar cannibalisation1.7Futures and forward markets1.8Fundamentals: gas, CO₂, renewables and demand1.9Regulatory interventions and exceptional mechanisms1.10Workshop: day-ahead market clearing analysis
2.1Data sources: OMIE, ENTSO-E and REE2.2Weather forecasts for wind and solar radiation2.3Demand forecasting and calendar effects2.4Fuel and emission allowance prices2.5Interconnections and power plant availability2.6Temporal feature engineering2.7Seasonality and holiday effects2.8Treatment of extreme values2.9Training and validation splits2.10Laboratory: building a price dataset
3.1ARIMA and SARIMAX models3.2Regression with exogenous variables: LEAR3.3Gradient boosting for hourly forecasting3.4Deep neural networks for prices3.5LSTM models and temporal transformers3.6Probabilistic forecasting and quantiles3.7Regime models and price jumps3.8Model combination and ensembles3.9MAE, RMSE and sMAPE metrics3.10Practice: LEAR model versus neural network
4.1Intraday and short-term forecasting4.2Day-ahead market forecasting4.3Weekly and monthly forecasting4.4Long-term scenarios and price curves4.5Fundamental dispatch models4.6Simulation of renewable penetration scenarios4.7Balancing service price forecasting4.8Price capture for renewable technologies4.9Out-of-sample evaluation and backtesting4.10Case study: price curve for valuing a solar farm
5.1Bidding strategies for generators5.2Battery storage optimisation5.3Portfolio management for retail suppliers5.4Hedging and forward contracting5.5Valuing PPAs with price forecasts5.6Deploying models into production5.7Explainability and trust in models5.8Model risk and AI governance5.9REMIT regulation and use of inside information5.10Case study: battery arbitrage with price forecasting
6.1Market definition and horizon6.2Data acquisition and preparation6.3Statistical benchmark model6.4Machine learning model6.5Probabilistic forecasting6.6Backtesting and model comparison6.7Application to a business decision6.8Deployment plan6.9Report and presentation6.10Final review with the tutor
How you'll learn
100 % online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. No fixed timetables: students progress at their own pace within the intake, which makes it possible to combine study with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python (statsmodels, LightGBM), PyTorch, epftoolbox, ENTSO-E Transparency Platform, PLEXOS, in free, academic or evaluation versions. The practical trials are based on real procedures for building a dataset from the Iberian day-ahead market, comparing statistical models and neural networks, and optimising a battery with price forecasts. Assessment is continuous, module by module, plus the final integrative project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Understand the price formation mechanisms in European electricity markets.
Build datasets with fundamental and meteorological variables.
Develop statistical and machine learning forecasting models.
Generate probabilistic forecasts for different horizons.
Evaluate models using backtesting and appropriate metrics.
Where this takes you
Requirements
- No prior degree is required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient level of Spanish to follow technical regulatory texts.
Ready to start?
Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.
Frequently asked questions
The programme comprises 300 hours in total, spread over 3 to 5 months. That is between 10 and 15 hours per week on average, compatible with a full-time job.
You work with Python (statsmodels, LightGBM), PyTorch, epftoolbox, ENTSO-E Transparency Platform, PLEXOS. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.
An in-house SEIUM diploma upon passing the continuous assessment and the integrative project. It is in-house training, not an official qualification from the Ministry.
Yes. Modules passed on a course or postgraduate diploma in the same area are recognised towards the higher-level programme. Please check the recognition policy when you enrol.
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