Distribution Network Operators (DNOs) are confronted with a significant challenge to expand the capacity of the electricity distribution grid to facilitate the energy transition. Grid expansion planning for the distribution grid is a complex problem with many constraints and objectives. Earlier research has focused on metaheuristics such as genetic algorithms to optimise grid expansion designs in terms of resolved congestions, redundancy and costs. However, this algorithm is not used in practice by its intended users; grid architects. This research first identifies why the algorithm is not yet used and proposes several ideas to improve user adoption. Then a technical implementation of sketch-based optimisation using a novel shape similarity measure is presented. We investigate its behaviour with a realistic case study. Furthermore, a qualitative user study is done to evaluate the potential impact of sketch-based optimisation on distribution grid expansion planning. We conclude that the novel shape similarity measure enables sketch-based optimisation of distribution grid expansion planning and that it has potential to accelerate electricity grid design processes. Alliander, the largest DNO of the Netherlands, will implement sketch-based optimisation in their distribution grid design application later this year, making it available to 20+ grid architects to accelerate their design process.

Sustained attention is a cognitive state where the learners’ attention is completely focused on the learning environment and content-related thoughts for a continuous stretch of time. Sustained attention is vital to perform well on learning tasks, such as reading. Learning analytics platforms that detect changes in sustained attention can prevent ineffective learning by providing direct feedback to the learner. Prior research has found that eye gaze and blink patterns can be good indicators of cognitive state. In this paper we investigate the following main research question: "How can webcam-based eye gaze and blink pattern tracking indicate changes in learners' sustained attention in the remote learning context?". While other studies rely on expensive eye trackers to perform detection, this research explores the use of widely used laptop webcams for detecting changes in sustained attention. We collected webcam data through a small case study involving several different reading tasks. A machine learning classification model was trained on the collected webcam data. The resulting detection model performs well on validation data with a F1-score of 0.889. The model does not perform well on testing data however, showing that it is not usable in practice. We give several possible explanations for this behavior, most of them originating from an overfitted model due to the small size of the user study. Our findings indicate that future work should focus on different experimental settings and larger user studies.