Abstract
This study presents a work in progress of the Smart Home Energy project (SHE), in which tests and simulations have generated a large set of energy consumption data that has been evaluated analytically to define a prediction model for energy consumption, based on automatic machine learning. The SuperDoop Lambda Arquitecture developed by Ingenia for Big Data implementation used in the SHE project allows implementing a service to do predictions massively, developing a personalized home energy knowledge model for each home. These methods and related technology can be used also for other energy consumers, like shops, offices, buildings, industries, electrical vehicles, etc.
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Acknowledgments
This project is funded by the Ministry of Economy and Competitiveness of Spain (IPT-2011-1237-920000). We are also very grateful to all the members of the consortium (Ingenia, Satec, Ingho, Tecopysa, Cotesa, IAT, University of Oviedo).
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Cortés, A., Téllez, A.E., Gallardo, M., Peralta, J.J. (2014). Big Data Technology to Exploit Climate Information/Consumption Models and to Predict Future Behaviours. In: González Alonso, I. (eds) International Technology Robotics Applications. Intelligent Systems, Control and Automation: Science and Engineering, vol 70. Springer, Cham. https://doi.org/10.1007/978-3-319-02332-8_3
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DOI: https://doi.org/10.1007/978-3-319-02332-8_3
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