Leveraging Graph Neural Networks to Forecast Electricity Consumption

• URL: http://arxiv.org/abs/2408.17366v1
• Date: Fri, 30 Aug 2024 15:54:50 GMT
• Title: Leveraging Graph Neural Networks to Forecast Electricity Consumption
• Authors: Eloi Campagne, Yvenn Amara-Ouali, Yannig Goude, Argyris Kalogeratos,
• Abstract summary: This research work offers a novel approach that extends beyond the conventional Generalized Additive Model framework. We introduce a range of methods for inferring graphs tailored to consumption forecasting, along with a framework for evaluating the developed models. We conduct experiments on electricity forecasting, in both a synthetic and a real framework considering the French mainland regions.
• Score: 3.157383076370605
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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