基于大数据的电力预测方法研究

Chunwei Wang, Dan Yang, Shangce Gao, Shujian Zhao, Ling Li, Xinya Wang
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摘要

本文主要基于大数据进行电量预测研究。分析了不同地区、不同行业、不同时期的电力变化趋势。数据被深入挖掘。利用时间序列、多元线性回归、灰色预测等算法对电量进行预测,提高短期、中期、长期的电量预测能力,为电量预测提供可靠的数据支持。通过实例验证,该方法能有效提高电力预测的准确性,实现对未来用电量的准确预测,提高客户服务能力,提高客户服务部门的工作效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on Electricity Forecasting Method Based on Big Data
This paper is mainly based on big data to carry out electricity forecasting research. The trend of electricity changes in different regions, industries and periods is analyzed. Data is dug deep. By using algorithms such as time series, multiple linear regression and grey forecasting to forecast the electricity quantity, improve the short-term, mid-term and long-term electricity forecasting ability, and provide reliable data support for electricity forecasting. It has been verified by examples that this method can effectively improve the accuracy of electricity forecasting, achieve accurate forecasting of future electricity consumption, improve customer service capabilities, and improve the work efficiency of customer service departments.
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