Cloud-Enabled Adaptive Activity-Aware Energy-Saving System in a Dynamic Environment

Energy saving has become an important issue in recent years due to the problems relating to energy shortage and global warming. However, most prior works in a domestic environment often ignore users' perception of the deployed technologies, let alone users' high-level contexts such as on-going activities and preferences. Without the high-level contexts, an ES system may fail to provide sufficient clues for a user to determine the most desirable ES strategy. In addition, the prior works are more technology-oriented and assume that our real-life environment stayed fixed once the system has been established. This causes their energy-saving (ES) strategies to be less human-centric and less adaptive to users' context changes. Moreover, good ES strategies are often established after the collection of long-term data and thorough analysis such that efficient ES models can be well trained, but such models are often not reusable or not sharable among different users or even communities. This indirectly leads to extra "en-ergy" waste in setting up a new energy-efficient environment. To remedy these drawbacks, in this paper we propose a cloud-enabled adaptive activity-aware energy-saving system which not only can facilitate more human-centric and context-aware ES strategies in a dynamic environment, but also can share the promising ES models that embed these desirable ES strategies among different users to facilitate community-based energy-saving.