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电力系统短期负荷预测的多神经网络Boosting集成模型
引用本文:高琳,高峰,管晓宏,周佃民.电力系统短期负荷预测的多神经网络Boosting集成模型[J].西安交通大学学报,2004,38(10):1026-1030.
作者姓名:高琳  高峰  管晓宏  周佃民
作者单位:1. 西安交通大学电气工程学院,710049,西安
2. 西安交通大学机械制造系统工程国家重点实验室,710049,西安
基金项目:国家杰出青年科学基金资助项目 (6970 0 2 5),国家自然科学基金资助项目 (5993 71 50,60 2 740 54),国家高技术研究发展计划资助项目 (2 0 0 1AA41 3 91 0 )
摘    要:提出了一种改进的多神经网络集成自适应Boosting回归算法.算法中采用相对误差模型代替绝对误差模型,可以更接近于回归预测问题的要求,并在Boosting迭代过程中,在对训练集采样得到新的训练子集的同时,也对校验集采样得到新的校验子集,保证了两者的一致性.进而采用美国加州电力市场的实际数据,建立了由多个神经网络集成的电力系统短期负荷预测模型.预测结果表明,与传统的单网络预测模型相比,Boosting集成预测模型能显著提高模型输出的稳定性,增强网络结构及模型选择的可靠性,获得更高的预测精度.

关 键 词:短期负荷预测  Boosting算法  神经网络集成
文章编号:0253-987X(2004)10-1026-05
修稿时间:2003年11月24

Neural Networks Ensemble Model Based on Boosting Algorithm for Short-Term Load Forecasting
Gao Lin,Gao Feng,Guan Xiaohong,Zhou Dianmin.Neural Networks Ensemble Model Based on Boosting Algorithm for Short-Term Load Forecasting[J].Journal of Xi'an Jiaotong University,2004,38(10):1026-1030.
Authors:Gao Lin  Gao Feng  Guan Xiaohong  Zhou Dianmin
Institution:Gao Lin~1,Gao Feng~2,Guan Xiaohong~2,Zhou Dianmin~2
Abstract:A revised adaptive boosting algorithm for neural networks ensemble model is proposed. In the algorithm, the relevant error criterion is used instead of the absolute error criterion, for it is more closed to the essential of the predict regression model. And at each step of boosting iteration, the new validation subset is obtained from validation sampled subsets, while getting new training subset from training sampled subset. The correspondence between the two subsets is guaranteed. The proposed algorithm is applied to build a neural network ensemble load forecast model using the real data from the California power market of the United States. The numerical simulation results show that the proposed ensemble model can improve the stability of model outputs significantly, and increase the reliability in network structure determination and model selection. With the ensemble model, the better forecasting accuracy is achieved in comparison with the single neural network model.
Keywords:short-term load forecasting  adaptive Boosting algorithm  neural network ensemble
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