An Attribute Reducing Method for Electric Power Big Data Preprocessing Based on Cloud Computing Technology

In face of the conventional attribute reduction incapability of grid data preprocessing for its big volume,diversified types and high speed in the forthcoming age of big data,a new method of electric power big data preprocessing via attribute reduction based on cloud computing technology is put forward.The characteristics of the relative positive region theory of the rough set is analyzed,and a parallel attribute reducing algorithm named MP_POSRS that can calculate the number of elements in the relative positive region is designed by taking good advantages of the MapReduce model in this method.Finally,the experiments including operations on the decision table of power grid fault diagnosis and real data of wind power are performed on a Hadoop platform,the results showing that the method is effective and feasible for dealing with power grid big data,and with good speedup and scalability required by electric power big data preprocessing via attribute reduction.