基于电力大数据分析的接电成本预测模型

Electricity connection cost prediction model based on electric power big data analysis

为提升电网的运营效率和资源利用率,提高预算及成本控制的精准度,同时帮助用户制定合理的供电方案、用电策略,推进用电服务及电力营商环境不断优化,构建基于电力大数据分析的接电成本预测模型。首先,分析电力大数据中的数据类型,并采用基于MapReduce并行化处理的聚类挖掘算法,从电力系统中挖掘与接电成本相关的电力大数据,获取聚类结果。其次,通过时间序列分析法构建接电总成本预测模型,并通过多元回归方法构建接电成本影响因素预测模型,经模型预测后,获取最佳接电成本预测结果。最后,经实验验证:该模型可精准预测企业用户接入时产生的接电成本,还能够有效预测不同设备价格、不同电压等级下的接电成本变化。

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.