Leveraging Graph Neural Networks to Forecast Electricity Consumption
<|reference_start|>Leveraging Graph Neural Networks to Forecast Electricity Consumption: Accurate electricity demand forecasting is essential for several reasons, especially as the integration of renewable energy sources and the transition to a decentralized network paradigm introduce greater complexity and uncertainty. The proposed methodology leverages graph-based representations to effectively capture the spatial distribution and relational intricacies inherent in this decentralized network structure. This research work offers a novel approach that extends beyond the conventional Generalized Additive Model framework by considering models like Graph Convolutional Networks or Graph SAGE. These graph-based models enable the incorporation of various levels of interconnectedness and information sharing among nodes, where each node corresponds to the combined load (i.e. consumption) of a subset of consumers (e.g. the regions of a country). More specifically, we introduce a range of methods for inferring graphs tailored to consumption forecasting, along with a framework for evaluating the developed models in terms of both performance and explainability. We conduct experiments on electricity forecasting, in both a synthetic and a real framework considering the French mainland regions, and the performance and merits of our approach are discussed.<|reference_end|>
@article{campagne2024leveraging,
    title={Leveraging Graph Neural Networks to Forecast Electricity Consumption},
    author={Eloi Campagne, Yvenn Amara-Ouali, Yannig Goude, Argyris Kalogeratos},
    journal={arXiv preprint arXiv:2408.17366},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.17366},
    primaryClass={cs.LG cs.AI}
}