刘家军, 王明军, 姚李孝, 张小庆, 薛美娟. 基于Theil不等系数的IOWHA算子组合模型年用电量预测新方法[J]. 现代电力, 2010, 27(5): 44-48.
引用本文: 刘家军, 王明军, 姚李孝, 张小庆, 薛美娟. 基于Theil不等系数的IOWHA算子组合模型年用电量预测新方法[J]. 现代电力, 2010, 27(5): 44-48.
Liu Jiajun, Wang Mingjun, Yao Lixiao, Zhang Xiaoqing, Xue Meijuan. New Forecasting Method of Annual Electricity Demand Using Combination Model with Induced Ordered Weighted Harmonic Averaging (IOWHA) Operator Based on Theil Coefficient[J]. Modern Electric Power, 2010, 27(5): 44-48.
Citation: Liu Jiajun, Wang Mingjun, Yao Lixiao, Zhang Xiaoqing, Xue Meijuan. New Forecasting Method of Annual Electricity Demand Using Combination Model with Induced Ordered Weighted Harmonic Averaging (IOWHA) Operator Based on Theil Coefficient[J]. Modern Electric Power, 2010, 27(5): 44-48.

基于Theil不等系数的IOWHA算子组合模型年用电量预测新方法

New Forecasting Method of Annual Electricity Demand Using Combination Model with Induced Ordered Weighted Harmonic Averaging (IOWHA) Operator Based on Theil Coefficient

  • 摘要: 配电网中长期负荷预测是配电网规划的基础, 精确的预测可提高配电网运行的可靠性和经济性。本文以年用电量预测作为研究对象, 年用电量预测采用4种主要方法, 即分别按照年度、季度、月度和行业用电量预测得到对应年用电量预测值, 在此基础上再按其发展序列预测结合起来, 建立了一种线性组合预测模型。并提出采用Theil不等系数的IOWHA算子算法对组合模型的权重系数进行求解, 该方法可以克服传统的组合预测方法赋予不变的加权平均系数和以单一误差指标作为预测精度衡量的缺陷, 文中的实例分析表明了新模型能有效地提高组合预测精度, 降低预测的风险性。从而证明这种组合模型具有较好的实用性。

     

    Abstract: Middle and longterm load forecasting is the foundation of distribution network planning, precise load forecasting can improve the reliability and economy of distribution network planning. In this paper, the annual electricity demand forecasting is used as research object, and forecasting value of annual electricity demand is obtained by annual, quarterly, monthly and industry electricity consumption, based on which a linear combination model for load forecasting is established by its developing sequence forecasting. Then the Theil coefficient and IOWHA operator are used to evaluate the weight coefficients. The model can overcome such disadvantages as constant weighted average deficiencies and single error index for measuring forecasting precision using traditional combination forecasting method. The example shows that the new model can improve forecasting precision effectively, reduce the risk of forecasting, and possess better practicability.

     

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