Abstract
Energy efficiency in public buildings has become a major research field, due to the impacts of the energy consumption in terms of pollution and economic aspects. For this reason, governments know that it is necessary to adopt measures in order to minimize the environmental impact and saving energy. Technology advances of the last few years allow us to monitor and control the energy consumption in buildings, and become of great importance to extract hidden knowledge from raw data and give support to the experts in decision-making processes to achieve real energy saving or pollution reduction among others. Prediction techniques are classical tools in machine learning, used in the energy efficiency paradigm to reduce and optimize the energy using. In this work we have used two prediction techniques, symbolic regression and neural networks, with the aim of predict the energy consumption in public buildings at the University of Granada. This paper concludes that symbolic regression is a promising and more interpretable results, whereas neural networks lack of interpretability take more computational time to be trained. In our results, we conclude that there are no significant differences in accuracy considering both techniques in the problems addressed.
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Acknowledgements
This work has been supported by the project TIN201564776-C3-1-R. We thank reviewers for their constructive comments and useful ideas for future works.
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Rueda Delgado, R., Baca Ruíz, L.G., Pegalajar Cuéllar, M., Delgado Calvo-Flores, M., Pegalajar Jiménez, M.d.C. (2018). A Comparison Between NARX Neural Networks and Symbolic Regression: An Application for Energy Consumption Forecasting. In: Medina, J., Ojeda-Aciego, M., Verdegay, J., Perfilieva, I., Bouchon-Meunier, B., Yager, R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications. IPMU 2018. Communications in Computer and Information Science, vol 855. Springer, Cham. https://doi.org/10.1007/978-3-319-91479-4_2
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