摘要
电力系统的稳定运行依赖于物资供应的精准匹配,而传统需求预测方法在应对复杂多变的电力设备需求时,逐渐显现局限性。随着能源结构加速调整与智能电网建设规模不断扩大,物资需求呈现出动态化、区域差异化等新特征。人为经验主导的预测方式不仅难以捕捉多因素耦合影响下的需求波动规律,还容易因信息滞后导致库存冗余或供应缺口。在此背景下,融合大数据分析技术的预测体系为电力物资管理提供了新的解决思路,其本质在于将海量业务数据转化为具有决策价值的规律性认知,推动需求预测从粗放式估算向精细化建模转变。
The stable operation of power system depends on the accurate matching of material supply,and the traditional demand forecasting method gradually shows its limitations when dealing with the complex and changeable demand for power equipment.With the accelerated adjustment of energy structure and the expansion of smart grid construction scale,material demand has shown new characteristics such as dynamic and regional differentiation.The forecasting method dominated by human experience is not only difficult to capture the demand fluctuation rule under the influence of multi-factor coupling,but also more likely to lead to inventory redundancy or supply gap due to information lag.In this context,the forecasting system integrating big data analysis technology provides a new solution for the management of power materials.Its essence is to transform massive business data into regular cognition with decision-making value,and promote the transformation of demand forecasting from extensive estimation to fine modeling.
作者
李猷
LI You(Xinjiang Information Industry Co.,Ltd.,Urumqi Xinjiang 830011)
出处
《中国科技纵横》
2025年第20期55-57,共3页
China Science & Technology Overview
关键词
大数据分析
电力物资
需求预测
big data analysis
electric power materials
demand forecasting
