Short-term nodal voltage forecasting for power distribution grids: An ensemble learning approach

Highlights: • Joint model- and data-driven feature selection for voltage forecasting. • Ensemble learning approaches for deterministic and probabilistic voltage forecasting. • Real-world case study for a Swiss distribution grid. The integration of distributed energy resources (DER) complicates the operation of the power distribution grids, and the nodal voltage may violate frequently. Making accurate predictions of the nodal voltage is fundamental for voltage regulation of the distribution grid. Even though energy forecasting has been widely studied, voltage is still a rarely touched area. This paper enriches the research by proposing an ensemble approach for both deterministic and probabilistic short-term nodal voltage forecasting. Specifically, a new joint model- and data-driven feature selection is first performed to select the most relevant features for distribution grid voltage forecasting. Then, different individual forecasting models are trained using the selected features. On this basis, simple weighted averaging and quantile regression averaging approaches are applied to combine the individual models for deterministic and probabilistic forecasting, respectively. Finally, case studies are conducted on a real-world distribution grid to verify the effectiveness and superiority of the proposed method.