Abstract
Global energy transition to renewable sources is among the substantial challenges facing humanity. In this context, the precise estimation of the renewable potential of given areas is valuable to decision-makers. This is particularly difficult for the urban case. In Chile, valuable data for solving this problem is available, however, standard machine learning algorithms struggle with their variable-length input. We take advantage of the ability of 1-D Convolutional Neural Network to fuse feature extraction and learning over a heterogeneous representation of the data. In the present manuscript, we propose an architecture to estimate the PV potential of Chilean cities and extract the relevant features over heterogeneous representations of available data. To this end, we describe and examine the performance of said architecture over the available data. We also extract its intermediate convolutional features and use them as inputs of other machine learning algorithms to compare performances. The network outperforms all other tested machine learning algorithms, while the intermediate learned convolutional representations improve the results of all non-linear algorithms explored.
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Valderrama, A., Valle, C., Ibarra, M., Allende, H. (2021). A Heterogeneous 1D Convolutional Architecture for Urban Photovoltaic Estimation. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Bevilacqua, V. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12836. Springer, Cham. https://doi.org/10.1007/978-3-030-84522-3_36
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