A hybrid approach of neural networks and grey modeling for adaptive electricity load forecasting
A hybrid approach of neural networks and grey modeling for adaptive electricity load forecasting
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DOI：
10.1007/s00521-006-0031-4
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发表时间：
2006-03
影响因子：
6
通讯作者：
C. Chiang;Ming-Che Ho;Jen-An Chen
中科院分区：
文献类型：
--
作者：
C. Chiang;Ming-Che Ho;Jen-An Chen
本文提出了一种神经网络与灰色模型有效融合的电力负荷自适应预测方法。融合运用了这两种吸引人的技术的互补力量。大量实验结果表明，在预测精度方面，所提出的融合方案优于单个方法和统计自回归方法。除了融合方案外，本文还提出了一种灰色关联分析方法来自动评估预测任务中每个输入变量的重要性。这种分析有助于预测者在众多输入变量中选择优势变量，从而消除了获取问题专业领域知识的负担，减少了不相关输入对预测的干扰。实验结果验证了灰色关联分析方法的有效性。
This paper proposes an effective fusion of neural networks and grey modeling for adaptive electricity load forecasting. The fusion employs the complementary strength of these two appealing techniques. In terms of forecasting accuracy, the proposed fusion scheme outperforms the individual ones and the statistical autoregressive methods according to the results of a substantial number of experiments. In addition to the fusion scheme, this paper also proposes a grey relational analysis to automatically assess the importance of each input variable for the forecasting task. This analysis helps the forecaster choose dominant ones among the many input variables, thus removing much burden of acquiring professional domain knowledge for problems and reducing the interference of irrelevant inputs on the forecasting. Experimental results are shown in this paper to verify the effectiveness of the grey relational analysis.