A Framework of Decision Support System based on Integrated Data for Electricity Management in Campus
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Abstract
This work presents a framework to support management plan in a university using sensor data and static data. The focused study is a student usage of a campus library. As data to reveal happenings for planning, data from several types of sensors including RFID, push sensor, light sensor and voltage sensor are used to detect students’ behavior in a library without interfering students’ privacy. The obtained sensor data is processed with ontological inference to infer students’ activity as additional information. All gathered data are processed and summarized with various aspects such as location, time, day and activity to inform planners of library usage statistic. From testing, enriched information with ontological inference can reveal more details than raw data of headcount. The results were more insight with student activity. A set of rules to manage facility is designed to suggest closing an area in specific date and time based on the headcount and inferred activity. From testing, the result showed that the suggested plan can help in reducing electricity cost smartly based on the statistic data of usage in the area.
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