Course in Renewable Generation Forecasting with AI
300 h online course in renewable generation forecasting with AI. 6 modules covering Regulation (EU) 2024/1689 (Artificial Intelligence Act), Regulation (EU) 2017/1485 (transmission system operation) and Python (scikit-learn). Assigned tutor and open enrolment.
Take the controls.
Run a real engineering trade-off from this field. Move the sliders, watch the model react in real time and beat the challenge.
What you will be able to do
Identify forecasting needs by horizon and use.
Prepare SCADA and meteorological data for model training.
Apply machine learning and deep learning models to solar and wind forecasting.
Generate probabilistic forecasts with calibrated intervals.
Evaluate models using error metrics and economic value.
Deploy and maintain models in production using MLOps practices.
Develop a forecasting system for a renewable plant or portfolio.
Who it's for
- Energy data scientist who wants to specialise in Renewable Generation Forecasting with AI.
- Renewable forecasting engineer who wants to specialise in Renewable Generation Forecasting with AI.
- Machine learning engineer who wants to specialise in Renewable Generation Forecasting with AI.
- Market operations analyst who wants to specialise in Renewable Generation Forecasting with AI.
- Applied meteorology specialist who wants to specialise in Renewable Generation Forecasting with AI.
- MLOps developer who wants to specialise in Renewable Generation Forecasting with AI.
- Graduates in engineering or sciences seeking to enter the renewables sector.
What you'll work with
- Regulation (EU) 2024/1689 (Artificial Intelligence Act)
- Regulation (EU) 2017/1485 (transmission system operation)
- IEC 61724-1 (photovoltaic performance monitoring)
- IEC 61400-12-1 (wind turbine power curves)
- ISO/IEC 42001 (artificial intelligence management systems)
- ISO/IEC 23053 (framework for AI systems using machine learning)
- Regulation (EU) 2016/679 (data protection)
6 modules · 300 h
1.1Forecasting horizons1.2Uses in the market and operations1.3Sources of uncertainty1.4Plant SCADA data1.5Numerical weather prediction (NWP) models1.6Satellite cloud data1.7Sky cameras1.8MAE, RMSE and skill score metrics1.9Persistence reference models1.10Case study: imbalance costs of a wind farm
2.1SCADA data cleaning2.2Curtailment detection2.3Interpolation of NWP outputs2.4Derived physical variables2.5Time and calendar variables2.6Normalisation by installed capacity2.7Temporal data partitioning2.8Handling missing data2.9Reproducible data pipelines2.10Workshop: data pipeline in Python
3.1Linear and ridge regression3.2Random forest models3.3Gradient boosting with XGBoost and LightGBM3.4Support vector machines3.5Learned power curve3.6Hyperparameter selection3.7Temporal cross-validation3.8Interpretability with SHAP3.9Model comparison3.10Laboratory: wind forecasting with LightGBM
4.1LSTM and GRU networks4.2Temporal convolutional networks4.3Transformers for time series4.4Nowcasting with satellite imagery4.5Quantile regression with gradient boosting4.6Prediction intervals4.7Conformal prediction intervals4.8Ramp forecasting4.9Forecasting with regional aggregation4.10Practical: intraday solar forecasting with LSTM
5.1MLOps practices in energy5.2Retraining and drift5.3Integration with EMS5.4Cloud services5.5Error monitoring5.6Forecasting for portfolios5.7Communicating uncertainty5.8Economic evaluation of forecasting5.9Model governance and ethics5.10Case study: deploying a forecasting service
6.1Defining the use case6.2Dataset6.3Reference model6.4Proposed learning models6.5Error evaluation6.6Final probabilistic forecast6.7Economic value in imbalances6.8Deployment 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. There are no fixed timetables: students progress at their own pace within the intake, which makes it possible to combine the programme with work.
Each module combines theory, guided reading of the original standard and applied exercises. The working software is Python (scikit-learn), PyTorch, LightGBM, MLflow, pvlib, in free, academic or evaluation versions. Tests are studied using real procedures for training forecasting models with SCADA and NWP data, evaluating probabilistic forecasts and deploying models in production. Assessment is continuous by module, plus a final integrative project.
Build it end to end
Your final project follows the real engineering workflow, step by step, reviewed by your tutor.
Identify forecasting needs by horizon and use.
Prepare SCADA and meteorological data for model training.
Apply machine learning and deep learning models to solar and wind forecasting.
Generate probabilistic forecasts with calibrated intervals.
Evaluate models using error metrics and economic value.
Where this takes you
Requirements
- No prior degree is required.
- Internet connection and a computer capable of running the programme's software.
- Sufficient 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 totals 300 hours spread over 3 to 5 months. That is an average of 10 to 15 hours a week, compatible with full-time work.
You work with Python (scikit-learn), PyTorch, LightGBM, MLflow, pvlib. Free, academic or evaluation versions are used, and the tutor indicates the free alternative where one exists.
A SEIUM own 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 in a Course or Postgraduate Diploma in the same area are recognised towards the higher programme. Check the recognition policy when you enrol.
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