A big data management system for energy consumption prediction models

In this work, we develop a prototype about a big data management system for storing, indexing, and searching for huge-scale energy usage data. Rather than existing, commercial relational databases such as Oracle and IBM-DB2, this system is able to provide us with high availability and performance at low cost. It is also able to manage unstructured data and store big data in distributed environment. In addition, using data access APIs, target data is quickly retrieved from our proposed system. To utilize our prototype system, we also propose an energy consumption prediction model based on penalized linear regression-based map/reduce algorithms. Then, we exploit discriminate features with respect to time stamp. Finally, given a time stamp (e.g., 2014-01-05 12:01:08), our proposed learning model will give us a predicted value about the energy usage (e.g., 90 watt) at that time. According to our experimental results obtained from about 7.5 million records, each of which consists of an energy usage and time stamp during three months in 2014, it turns out that our prediction model can predict real values that are very close to actual energy usage at that time, and is about 1.72 times faster than in a single machine.