Internship | Generative models for intraday electricity prices

StudentJob; Rijswijk; On-site; EUR 5,800 - 7,900; Full time

Benefits offered by this job

615 EUR monthly internship allowance

Travel expense contribution

Jong TNO membership

Laptop provided

Eight hours of leave per internship MT

Job summary

TNO is offering an internship at their energy systems team in the Netherlands, focusing on conditional generative models for intraday price scenarios. You will work with ML experts at the interface of electricity markets and energy system modelling. The role blends data science with practical energy market research.

You will design, train and evaluate models, build datasets, derive conditioning features and benchmark architectures while communicating results clearly to the team.

Qualifications

• Solid grounding in machine learning, in particular generative or probabilistic models.
• Strong Python skills; experience with a deep learning framework such as PyTorch is an advantage.
• Familiarity with time series modelling and probabilistic forecasting.
• Interest in electricity markets and energy systems; prior knowledge is not required.
• Ability to work independently and communicate technical results clearly.

Responsibilities

• Design, train and evaluate a conditional generative model for intraday prices or day-ahead to intraday spreads.
• Build a dataset from historical market and system data.
• Derive conditioning features describing system state and flexibility using TulipaEnergyModel.jl.
• Implement and compare architectures for conditional generative models.
• Benchmark against baselines like quantile regression, copulas, or historical resampling.
• Evaluate with proper scoring rules and test generalisation to unseen configurations.
• Translate findings into clear technical conclusions and recommendations.

Skills

Python

Deep learning

Time series

Probabilistic forecasting

ML

Education

Computer Science / AI

Data Science or Applied Mathematics

Electrical Engineering

Econometrics or Statistics

Tools

PyTorch

Job description

About this position

As wind and solar shares grow, more of the value of batteries, electrolysers and other flexible assets comes from short-term markets. Yet most energy system models represent only the day-ahead market, and therefore misprice flexibility.

Intraday prices, where represented, are typically derived from historical correlations with day-ahead prices. Such approaches implicitly assume that price formation is independent of system composition. Because entering storage and flexibility shift the relationship between day-ahead and intraday prices, historically calibrated models misrepresent flexibility value precisely in the high-flexibility scenarios they are intended to evaluate.

This internship applies conditional generative models, GANs, diffusion models or similar, to generate intraday price scenarios that respond to a future system configuration. The results feed into stochastic and rolling-horizon energy system optimisation.

You will work with a multidisciplinary team at TNO at the interface of machine learning, electricity markets and energy system modelling.

What will be your role?

You will design, train and evaluate a conditional generative model for intraday prices or day-ahead to intraday spreads.

Typical activities include:

• Building a dataset from historical market and system data.
• Deriving conditioning features that describe system state and flexibility, using an energy system model (TulipaEnergyModel.jl).
• Implementing and training a conditional generative model, and comparing architectures.
• Benchmarking against simpler baselines such as quantile regression, copulas or historical resampling.
• Evaluating with proper scoring rules, and testing whether the model generalises to system configurations outside the training data.
• Translating findings into clear technical conclusions and recommendations.

You will have space to bring your own ideas to the approach.

What we expect from you

For this internship we are looking for a student with a background in:

• Computer Science / Artificial Intelligence;
• Data Science or Applied Mathematics;
• Electrical Engineering;
• Econometrics or Statistics;
• Or a related field.

Preferred qualifications:

• Solid grounding in machine learning, in particular generative or probabilistic models.
• Strong Python skills; experience with a deep learning framework such as PyTorch is an advantage.
• Familiarity with time series modelling and probabilistic forecasting.
• Interest in electricity markets and energy systems; prior knowledge is not required.
• Ability to work independently and communicate technical results clearly.

We are looking for a curious and proactive student who enjoys open-ended technical problems.

What you'll get in return

An internship at TNO means working in an environment where substance and impact are central. You will become part of a knowledge organisation where research and practice come together, and where experts collaborate on solutions to current societal and technological challenges.

Your internship is a period in which you can discover what suits you, where your strengths lie and what you would like to learn next. You are part of a professional working environment, gain insight into how things work in practice, and have the opportunity to build experience that goes beyond this internship alone. For many students, an internship is therefore also a first step in discovering whether TNO could be a potential next step after graduation.

In addition, we offer you:

• A professional and innovative internship environment in which you actively contribute to societal and technological challenges, working alongside leading experts in your field.
• Personal and dedicated supervision, with focus on your learning objectives, development and study assignment.
• Room to develop: you gain relevant work experience, develop both your subject-specific and professional skills, and build a valuable network.
• Use of a laptop and the facilities you need to perform your work effectively.
• A monthly internship allowance of € 615 for a full‑time internship, for MSc, BSc and vocational education (MBO) students.
• Up to eight hours of leave per internship month for a full‑time internship, allowing you to balance your internship with your studies and personal life.
• A contribution towards travel expenses if you are not entitled to a student travel card.
• A free membership to Jong TNO: the network for young colleagues, where you can meet other TNO colleagues and participate in sports activities, professional and personal development activities, and social events such as the annual ski trip.

TNO as an employer

Our people are at the heart of TNO. Their curiosity, expertise and entrepreneurial mindset make it possible to deliver high-impact research and innovations that contribute to society's sustainable wellbeing and prosperity. That is why we invest in an inspiring and inclusive working environment where colleagues can excel, have autonomy and continue to grow.

Your talent and ambition have every opportunity to flourish at TNO. You work with experts (both within and beyond TNO), have access to advanced technology and the freedom to explore, experiment and innovate. Our strength