电力系统短期负荷预测的多神经网络Boosting集成模型

Neural Networks Ensemble Model Based on Boosting Algorithm for Short-Term Load Forecasting

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.

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.