Abstract
The active participation of end-users in the smart grid brings opportunities to those who want to participate. The participation and remuneration of end-users are issues that have been studied over the years in scientific publications. However, they heavily depend on energy predictions for participation requests and participation assessment for remuneration. These energy predictions are crucial to remunerate end-users according to their participation. The study of energy prediction models is needed and must take into account data privacy and security issues. One possible solution is to include prediction models in energy management systems. This paper proposed five energy prediction models that are able to be executed in a system on a chip, such as a single-board computer. The models, based on a mathematical approach and on a learning approach, were tested in a physical deployment using a Raspberry Pi 3 Model B. The results show the ability of having energy prediction models being executed in energy management systems deployed in a system on a chip, resulting in satisfactory prediction values.
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Acknowledgments
This work has received funding from the European Union’s Horizon 2020 research and innovation programme under project DOMINOES (grant agreement No 771066). The work has been done also in the scope of projects UIDB/00760/2020, and COLORS (PTDC/EEI-EEE/28967/2017).
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Gomes, L., Vale, Z. (2022). Energy Predictions for System on a Chip Solutions. In: Sanjurjo González, H., Pastor López, I., García Bringas, P., Quintián, H., Corchado, E. (eds) 16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021). SOCO 2021. Advances in Intelligent Systems and Computing, vol 1401. Springer, Cham. https://doi.org/10.1007/978-3-030-87869-6_23
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DOI: https://doi.org/10.1007/978-3-030-87869-6_23
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