摘要
针对目前电力负荷预测方法在处理大规模和多维数据时存在的预测准确率较低、稳定性较差问题,文章提出了一种基于大数据处理的电力负荷预测模型。文章构建了大数据分析平台,采用Hadoop进行数据分割,在数据管理层中处理数据并对数据进行整合;综合分析电力负荷、气象数据及时间特征等多种因素,构建优化目标函数,利用SVR模型捕捉负荷波动的非线性特征,构建预测模型,完成预测过程。实验结果表明,文章方法的预测准确性高于95%,预测过程中波动较小,稳定性方面优于传统模型。
In response to the problems of low prediction accuracy and poor stability in current power load forecasting methods when dealing with large-scale and multidimensional data,this paper proposes a power load forecasting model based on big data processing.Build a big data analysis platform,use Hadoop for data segmentation,process data in the data management layer,and integrate the data.Comprehensively analyze various factors such as power load,meteorological data,and time characteristics,construct an optimization objective function,use the SVR model to capture the nonlinear characteristics of load fluctuations,construct a prediction model,and complete the prediction process.The experimental results show that the proposed method achieves a prediction accuracy of over 95%,with minimal fluctuations during the prediction process and superior stability compared to traditional models.
作者
王京威
WANG Jingwei(Nanchang Institute of Science&Technology,Nanchang 330108,China)
出处
《无线互联科技》
2025年第22期65-68,共4页
Wireless Internet Science and Technology
关键词
大数据
电力负荷
负荷预测
预测建模
big data
power load
load forecasting
predictive modeling
