文章摘要
基于弹性网络模型的月度用电量预测方法
Monthly electricity consumption forecasting method based on elastic network model
投稿时间:2019-07-09  修订日期:2019-09-22
DOI:
中文关键词: 弹性网络  Lasso  Granger因果关系  因子筛选  用电量预测
英文关键词: elastic network  lasso  Granger causality  factor screening  electricity consumption forecasting
基金项目:国家电网有限公司总部科技项目:支持电力大数据分析的核心算法改进及其实用化技术(520940180016)资助
作者单位E-mail
胡春凤 中国电力科学研究院 m15600465900@163.com 
田世明 中国电力科学研究院  
苏航   
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中文摘要:
      电网规模的扩大和电力系统自动化水平的提高,为基于大数据技术的精准用电量预测提供了数据基础和实践依据。针对高维数据变量筛选和高精度预测等突出问题,提出一种弹性网络用电量预测模型,对用电量、经济、交通、气象4类,共340个变量,96个时间点的月度数据进行关联分析。对高维变量进行弹性网络因子筛选,并使用Granger因果关系分析找出了用电量数据与其它数据的依赖关系,对一年范围内的全社会月度用电量进行预测,预测结果的平均绝对百分误差控制在2%以内,取得了较好的预测效果。对比VAR模型,BP模型和Lasso,预测结果表明本文所选方法精度较高,验证了该方法的可行性及有效性。
英文摘要:
      The expansion of power grid scale and the improvement of power system automation level provide a data foun-dation and practical basis for accurate power consumption forecast based on big data technology. Aiming at the prominent problems such as high-dimensional data variable screening and high-precision prediction, this paper proposes an elastic network electricity consumption prediction model, which uses 340 variables for power con-sumption, economy, transportation and meteorology, and monthly data of 96 time points. Perform correlation analysis. Elastic network factor screening for high-dimensional variables, and Granger causality analysis to find out the dependence of electricity consumption data and other data, predicting the monthly electricity consumption of the whole society in a year, and the average absolute value of the prediction results. The percentage error is con-trolled within 2%, and a good prediction effect is obtained. Compared with VAR model, BP model and Lasso, the prediction results show that the method selected in this paper has high precision, which verifies the feasibility and effectiveness of the method.
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