Abstract
In this study, we combine cloud computing with big data processing techniques to build a real-time energy monitoring system for smart campus. The monitor plat-form collects the electricity usage in campus buildings through smart meters and environmental sensors, and processes the huge amount of data by big data processing techniques. A Hadoop ecosystem is built on top of big data processing architecture to improve the capacity of big data storage and processing ability. Moreover, we compare the performance of Hive and HBase in searching energy data, and the performance of relational database and big data distributed database for data search. We also identify abnormal electrical condition through the MapReduce framework, and compared the difference of performances between Spark and Hadoop in real-time processing. The proposed system has been implemented in Tunghai University campus. It enables administrators to observe the real-time electricity usage and analyze historical data anytime and from anyplace.
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