To enable the transition from fossil fuels towards renewable energy, the low-voltage grid needs to be reinforced at a faster pace and on a larger scale than was historically the case. To efficiently plan reinforcements, one needs to estimate the currents and voltages throughout the grid, which are unknown but can be calculated from the grid layout and the electricity consumption time series of each consumer. However, for many consumers, these time series are unknown and have to be estimated from the available consumer information. We refer to this task as scenario generation. The state-of-the-art approach that generates electricity consumption scenarios is complex, resulting in a computationally expensive procedure with only limited interpretability. To alleviate these drawbacks, we propose a fast and interpretable scenario generation technique based on predictive clustering trees (PCTs) that does not compromise accuracy. In our experiments on three datasets from different locations, we found that our proposed approach generates time series that are at least as accurate as the state-of-the-art while being at least 7 times faster in training and prediction. Moreover, the interpretability of the PCT allows domain experts to gain insight into their data while simultaneously building trust in the predictions of the model.

Inductive logic programming, or relational learning, is a powerful paradigm for machine learning or data mining. However, in order for ILP to become practically useful, the efficiency of ILP systems must improve substantially. To this end, the notion of a query pack is introduced: it structures sets of similar queries. Furthermore, a mechanism is described for executing such query packs. A complexity analysis shows that considerable efficiency improvements can be achieved through the use of this query pack execution mechanism. This claim is supported by empirical results obtained by incorporating support for query pack execution in two existing learning systems.

Inductive logic programming, or relational learning, is a powerful paradigm for machine learning or data mining. However, in order for ILP to become practically useful, the efficiency of ILP systems must improve substantially. To this end, the notion of a query pack is introduced: it structures sets of similar queries. Furthermore, a mechanism is described for executing such query packs. A complexity analysis shows that considerable efficiency improvements can be achieved through the use of this query pack execution mechanism. This claim is supported by empirical results obtained by incorporating support for query pack execution in two existing learning systems.