Adaptive knowledge representation for a self-managing home energy usage system

Automated and effcient energy management has many potential benefits for producers and consumers of energy, and the environment. Focusing on energy management on the consumer side, this paper considers two forms of energy management: minimizing energy usage in single households and avoiding peaks in energy consumption in a larger area. A combination of context aware and autonomic computing is used to describe an automated and self-managing system that, by analyzing context information and adapting to its environment, can learn the behavior of household occupants. Based on this information, together with user defined policies, energy usage is lowered by selectively powering down devices. By powering specific thermostatically controlled devices on or off energy can also be redistributed over time. This is utilized to avoid global peaks in energy usage. The self-managing system reasons about context and other information and acts when required. This information is the knowledge with which it can adaptively reason, about to take to ensure effcient energy usage. This paper explores the requirements that hold for representing this knowledge and how the knowledge base can continuously and adaptively be updated: to be self-managing.