摘要
受多因素影响,季度供电量存在增长性及季节波动性的复杂二重趋势。针对现有预测方法中对供电量自身组分及外部影响因素信息利用不足的情况,提出利用行业分类及数据关联度寻优方法选择最优影响参数个数,从而确定模型参数N,构造最优GM(1,N)电量预测模型,提高预测准确度的方法。根据供电企业的用电用户类别,对供电量进行自身组分分析;计算各组分及外部影响因素与供电量的关联度,并对关联度由大到小进行排序;根据不同N的拟合精度确定最优GM(1,N)模型。应用该方法对某供电局的供电量数据进行预测分析表明,该算法具有预测精度好、结果可信度高的特点。
Affected by multiple factors, quarter power supply sequence presents the double trends of increasing and fluctuating. Existing forecasting algorithms can＇ t make full use of all components of power supply data and external influencing factors. In this paper, an optimal GM （1, N ） power supply forecasting algorithm is proposed to solve ＇all these problems and improve the forecasting accuracy. And this algorithm is based on industry category and data correlation optimization method to determine the N in the optimal GM （1. N ） model. Firstly, according to the power user category of power supply enterprise, the component of power supply data is analyzed. Then the correlation between power supply data and each factor is calculated and sorted. Finally, the GM （1, N ） model of different N is estab- lished, and optimal GM （1, N ） model is determined according to the fitting precision. The actual power supply data is used to test this forecasting algorithm. The results show that the forecasting ＇algorithm has the characteristics of high accuracy and high reliability.
出处
《电力需求侧管理》
2015年第5期11-15,32,共6页
Power Demand Side Management
基金
国家自然科学基金重点资助项目(50937001)
中央高校基本科研业务费专项资金资助项目(2012ZM0018)
关键词
组分分析
外部影响因素
GM(1
N)
关联度
电量预测
component analysis
external factors
GM （1, N ）
correlation
power supply forecasting
