Semantic Decision Support Models for Energy Efficiency in Smart-Metered Homes

A promising approach to Smart Energy Grids is to empower communities of consumers with a novel role in the management of their electricity by sharing excess electricity and therefore becoming energy producers (prosumers). We achieve it using a framework to connect dynamic, context-aware, heterogeneous virtual and real entities on the Internet of Smart Meters (IoSM). We transform the smart electricity meters into fully-fledged intelligent computers on the IoSM and enable them to securely collect data from heterogeneous meters and sensors, detect smart meters with similar goals, exchange and aggregate data from multiple autonomous physical or virtual meters, and manage the actual energy demand and ensure the achievement of demand response for the community involved. Domain ontologies are used to store the semantics of the collected data from IoSM. A problem we have encountered is how our framework can use ontologies for community-grounded decision rules, which need to be consistent based on aggregation of collected data and contexts. We use Semantic Decision Table (SDT), which is a decision table enhanced with ontology technologies, to tackle the problem. This paper records our efforts in 1) the general design of the framework; 2) decision support models, namely SDTs, for fulfilling this requirement; 3) an algorithm of creating invocational SDTs; 4) the implementation of visualizing invocational SDTs.