In order to improve the operation efficiency and resource utilization of the power grid

improve the accuracy of budget and cost control

at the same time

help users to develop reasonable power supply plan and electricity consumption strategy

promote the continuous optimization of electricity service and electricity business environment

and build a power connection cost prediction model based on big data analysis of power. Firstly

analysis the data types in the power data

and using the MapReduce parallelization processing cluster mining algorithm

mining from the power system and the power cost related power data

obtain the clustering results. Then

through the time series analysis method build the total cost prediction model

and through the multivariate regression method to build the cost of factors prediction model

after the model prediction

get the best cost prediction results. Finally

by experiment

the model can accurately predict the power connection cost generated when enterprise users access

and can also effectively predict the change of power connection cost under different equipment prices and different voltage levels.