Using the Big Data generated by the Smart Home to improve energy efficiency management
| Autor: | María Rodríguez Fernández, Ignacio González Alonso, Adolfo Cortés García, Eduardo Zalama Casanova |
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| Rok vydání: | 2015 |
| Předmět: |
Consumption (economics)
Engineering business.industry Smart meter 020209 energy Big data 02 engineering and technology computer.software_genre General Energy Risk analysis (engineering) Home automation Demand curve 0202 electrical engineering electronic engineering information engineering Data mining business Raw data computer Wireless sensor network Efficient energy use |
| Zdroj: | Energy Efficiency. 9:249-260 |
| ISSN: | 1570-6478 1570-646X |
| DOI: | 10.1007/s12053-015-9361-3 |
| Popis: | A Smart Home is able to generate energy-related values such as electricity consumption, temperature, or luminosity without higher infrastructure requirements. The main aim of this research is to extract information from that raw data that could contribute to improving the energy efficiency management. This paper presents a system which, using different Machine Learning approaches to learn about the users’ consumption habits, is able to generate collaborative recommendations and consumption predictions that help the user to consume better, which will in turn improve the demand curve. Moreover, from consumption values, the system learns to identify devices, enabling the demand to be anticipated. Taking into account the fact that the amount of energy data is increasing in real-time, the use of Big Data techniques will be the key to handling all the operations and one of the more innovative features of the system. |
| Databáze: | OpenAIRE |
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