Abstract
Energy conservation and CO2 emission reduction have both recently become critical environmental issues. Despite the considerable efforts of governments and technological developments by private enterprise, such as energy saving appliances and solar power systems, CO2 emissions per household are still increasing. Continued effort not only from companies, but also from each household and individuals is necessary. This paper describes a smart home system that is aware of household situations, performs automatic energy conservation when necessary, mines data on individual activities and gives advice and suggestions to individuals. Initially, the system records related objects and domestic human activities and structures and places the recorded data into three data logs: a space log, a device log, and a person log. Secondly, the system recognizes a device- or appliance-related situation and deduces individual activities by applying data mining techniques to the structured data logs. Finally, the system automatically conserves energy according to situation and gives appropriate advice to individuals by making them aware of their activities. A long-term objective of this system is to build a perception-influence relational model with which the system can adopt personalized presentation styles to give personalized advice to different individuals. It is expected that people’s behavior under this system will shift imperceptibly towards lifestyles and domestic routines that conserve energy and reduce CO2 emissions.
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Tamura, T., Huang, R., Ma, J., Yang, S. (2011). Individual Activity Data Mining and Appropriate Advice Giving towards Greener Lifestyles and Routines. In: Hsu, CH., Yang, L.T., Ma, J., Zhu, C. (eds) Ubiquitous Intelligence and Computing. UIC 2011. Lecture Notes in Computer Science, vol 6905. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23641-9_12
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DOI: https://doi.org/10.1007/978-3-642-23641-9_12
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