Online identification of appliances from power consumption data collected by smart meters
| Autor: | I. González Alonso, E. Zalama Casanova, M. Rodríguez Fernández |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Consumption (economics)
business.industry Smart meter Computer science Big data Context (language use) 02 engineering and technology 010501 environmental sciences Computer security computer.software_genre 01 natural sciences Smart grid Artificial Intelligence 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Smart environment Computer Vision and Pattern Recognition Electricity business Telecommunications computer 0105 earth and related environmental sciences Efficient energy use |
| Zdroj: | Pattern Analysis and Applications. 19:463-473 |
| ISSN: | 1433-755X 1433-7541 |
| DOI: | 10.1007/s10044-015-0487-x |
| Popis: | The efficient use of resources is a matter of great concern in today's society, especially in the energy sector. Although the main strategy to decrease energy use has long been focused on supply, over the last few years, there has been a shift to the demand side. Under this new line of action, demand-side management networks have emerged and extended from the household level to larger installations, with the appearance of the concepts of Smart Grids and even Smart Cities. The extended use of Smart Meters for measuring residential electricity consumption facilitates the creation of such intelligent environments. In this context, this article proposes a system which extracts value from the collected consumer information to identify the appliances belonging to that smart environment by means of machine learning techniques. Considering the large amount of information that would be handled when millions of homes were sending data, big data technology has been used. An experiment to evaluate the classification method was carried out with seven devices and three different configurations. The results are also reported, achieving promising results, with recognition rates of 75 % after 1 h of training and 100 % after 4 h. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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