Abstract
The efficient use of resources is a matter of great concern in today’s society. Specifically, power consumption has led to small networks that incorporate management systems, which are also being integrated in other networks that increasingly cover larger installations (homes and offices, buildings, districts, cities, etc.). From the natural evolution of the common services management, the concepts of Smart Grid and even Smart Cities have arisen strongly, pursuing the efficient management of resources, which is the strategic investment of these complex and smart systems. One of the biggest restrictions on setting up an intelligent environment is the lack of interoperability between different devices. In this context, the need to be able to identify the devices connected to the network will allow to automatically execute a software adapter that makes the device interact with the whole system in a standard way. Achieving that objective could also bring other positive consequences, such as being able to anticipate energy demand. Based on the idea that the amount of consumption data is large and is increasing, the suitability of using big data techniques to handle this information, combined with automatic machine learning techniques to extract value from such a large amount of data, is proposed in this work in progress.
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Acknowledgments
This work is being possible thanks to the financial support of the Spanish Department of Science and Technology, the Ministry of Industry, Energy and Tourism and FEDER funds. We would like to acknowledge the continuous support given by the University of Oviedo, and their management of the DHCompliant 2 project (TSI-020100-2011-313). We are also very grateful to all the members of the DHCompliant 2 consortium, composed by Infobotica Research Group, Ingenium, Ingeniería y Domótica S.L. and Domotica DaVinci. We are also very grateful to the Ministry of Economy and Competitiveness of Spain for making possible the Smart Home Energy Project (IPT-2011-1237-920000) and to all the members of the SHE consortium (Ingenia, Satec, Ingho, Tecopysa, Cotesa, IAT, Infobotica Research Group).
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Rodriguez, M., González, I., Zalama, E. (2014). Identification of Electrical Devices Applying Big Data and Machine Learning Techniques to Power Consumption Data. 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_4
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DOI: https://doi.org/10.1007/978-3-319-02332-8_4
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