Abstract
Deep learning models have achieved extensive popularity due to their capability for providing an end-to-end solution. But, these models require training a massive amount of data, which is a challenging issue and not always enough data is available. In order to get around this problem, a few shot learning methods emerged with the aim to achieve a level of prediction based only on a small number of data. This paper proposes a few-shot learning approach that can successfully learn and predict the electricity consumption combining both the use of temporal and spatial data. Furthermore, to use all the available information, both spatial and temporal, models that combine the use of Recurrent Neural Networks and Graph Neural Networks have been used. Finally, with the objective of validate the approach, some experiments using electricity data of consumption of thirty-six buildings of the University of Alicante have been conducted.
Supported by CENID (Centro de Inteligencia Digital) in the framework of the Agreement between the Diputación Provincial de Alicante and the University of Alicante.
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García-Sigüenza, J., Vicent, J.F., Llorens-Largo, F., Berná-Martínez, JV. (2023). Few-Shot Learning for Prediction of Electricity Consumption Patterns. In: Pertusa, A., Gallego, A.J., Sánchez, J.A., Domingues, I. (eds) Pattern Recognition and Image Analysis. IbPRIA 2023. Lecture Notes in Computer Science, vol 14062. Springer, Cham. https://doi.org/10.1007/978-3-031-36616-1_56
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