基于电力大数据的工业用户营商环境优化

作为工业大国,工业用电占我国电力消费的60%以上,为进一步优化工业用户营商环境,激发市场主体活力,以工业用户历史用电数据为数据源,结合现有云平台系统Spark内存批处理的大数据处理框架,建立工业用户电力运营成本分析优化模型。分析工业用户历史负荷情况,依据算法预测出其未来一年的负荷,将现有电价规则训练为电价分析模型,代入工业用户未来一年的负荷情况,可得到该工业用户最优的变压器配置和电价策略。科学分析工业用户电力运营成本,制定优化方案,可切实帮助用户降低企业用电成本,解决企业受电成本虚高问题,实现由被动服务向主动优质服务转变。

Optimization of Industrial User Business Environment Based on Electric Power Big Data

As a large industrial country,industrial electricity accounts for more than 60%of China's power consumption.In order to further optimize the business environment of industrial users and stimulate the vitality of market entities,this article uses historical electricity consumption data of industrial users as the data source,combined with the existing cloud platform system spark memory batch processing big data processing framework to establish the analysis and optimization model of power operation cost of industrial users.At the same time,this paper analyzes the historical load situation of industrial users,predicts their load in the next year based on the algorithm,trains the existing electricity price rules into an electricity price analysis model,and substitutes the load situation of industrial users in the next year to obtain the optimal transformer configuration for the industrial user and electricity price strategy.Scientific analysis of the power operating costs of industrial users and formulating optimization plans can effectively help users reduce the cost of enterprise electricity,solve the problem of falsely high enterprise electricity costs,and realize the transition from passive service to active high-quality service.