More than accuracy: end-to-end wind power forecasting that optimises the energy system

Weather forecast models are essential for sustainable energy systems. However, forecast accuracy may not be the best metric for developing forecast models. A more or less conservative forecast may be preferred over pure accuracy. For example, forecasting accurately in times of energy-deprived situations may be more important than in times of...

Generating quality datasets for real-time security assessment: Balancing historically relevant and rare feasible operating conditions

This paper presents a novel, unified approach for generating high-quality datasets for training machine-learned models for real-time security assessment in power systems. Synthetic data generation methods that extrapolate beyond historical data can be inefficient in generating feasible and rare operating conditions (OCs). The proposed...

Split-based sequential sampling for realtime security assessment

Machine learning (ML) for real-time security assessment requires a diverse training database to be accurate for scenarios beyond historical records. Generating diverse operating conditions is highly relevant for the uncertain future of emerging power systems that are completely different to historical power systems. In response, for the first...

Declarative Image Generation

Generating synthetic images has wide applications in several fields such as creating datasets for machine learning or using these images to investigate the behaviour of machine learning models. An essential requirement when generating images is to control aspects such as the entities or objects in the image. Controlling this helps in creating...

Can Invariant Risk Minimization resist the temptation of learning spurious correlations?

Learning algorithms can perform poorly in unseen environments when they learn<br/>spurious correlations. This is known as the out-of-domain (OOD) generalization problem. Invariant Risk Minimization (IRM) is a method that attempts to solve this problem by learning invariant relationships. Motivating examples as well as counterexamples have been...

Using Decision Trees produced by Generative Adversarial Imitation Learning to give insight into black box Reinforcement Learning models

Machine learning models are increasingly being used in fields that have a direct impact on the lives of humans. Often these machine learning models are black-box models and they lack transparency and trust which is holding back the implementation. To increase transparency and trust this research investigates whether imitation learning,...

Predictive and generative machine learning models for photonic crystals

The prediction and design of photonic features have traditionally been guided by theory-driven computational methods, spanning a wide range of direct solvers and optimization techniques. Motivated by enormous advances in the field of machine learning, there has recently been a growing interest in developing complementary data-driven methods...