Complete tolerance relation based parallel filling for incomplete energy big data

In addition, we propose the adaptive management architecture that handles incomplete energy big data in green data centers. Our proposed architecture integrates the techniques for preprocessing energy data, filling incomplete energy data and building decision model. It increases the power assignment efficiency between solar power and utility, while enhancing load performance and service availability. As a result, it can provide better service for green data centers. We perform comprehensive experiments on an energy data set and the results show the Completing Incomplete Big Data (CIBD) algorithm can guarantee the completeness of data while improving the filling accuracy by 10% compared to general filling algorithms such as MEAN or ERS. The proposed algorithm and architecture show more benefit as the data missing rate increases. We further utilize the filled data to establish the random forest model and yield desirable results. Compared to the Hadoop based filling algorithm, the processing speed of the CIBD algorithm improves by 50% on the 4GB data size.