基于大数据挖掘电量预测方法的创新及应用

本文主要是基于营销系统数据开展电量预测出发，分析不同区域、行业以及时期的电量变化趋势，深入挖掘数据，发现隐藏信息并加以利用。全方位挖掘影响电量的因素，筛选出相关性强的因子形成因子储备库为后续建模做准备。通过运用时间序列、多元线性回归和灰色预测等算法对电量进行预测，丰富电量预测手段，提高短期、中期和长期的电量预测能力，并对预测结果进行可视化展示，为电量预测提供可靠的数据支撑。经实例验证，该方法能够有效的提高电量预测方面的精准度，实现未来用电量的精准预测，不仅能提升客户服务能力，提高客户服务部门的工作效率，还能有效提高电力公司的核心竞争力，使电力公司在市场愈发激烈的现状下做出更加明智的商业决策，抢占电力市场的主导地位。

Innovation and Application of Power Forecasting Method Based on Big Data Mining

This paper is based on the marketing system data to conduct electricity forecasting, analyze the trends of electricity consumption in different regions, industries and periods, drill down into the data, find hidden information and use it. The factors affecting electricity consumption are excavated in all directions, and the relevant factor formation factor reserve library is selected to prepare for subsequent modeling. Through the use of time series, multiple linear regression and gray prediction algorithms to predict electricity, enrich power forecasting methods, improve short-term, medium-term and long-term power forecasting capabilities, and visualize the forecast results to provide reliable data support for power forecasting. The example proves that the method can effectively improve the accuracy of power forecasting and achieve accurate prediction of power consumption in the future,which not only improves customer service capability, improves the work efficiency of customer service departments, but also effectively improves the core competition of power companies, So that power companies can make more wise business decisions in the increasingly fierce market situation and seize the leading position in the electricity market.