【摘要】 针对在电力系统短期负荷预测应用中,单个神经网络存在预测精度较低、预测结果不稳定、泛化能力差的特点,本文提出一种新的基于多神经网络自适应集成的预测模型。通过对某地区的实际负荷数据进行预测分析表明,该方法以很小的运算时间代价、较小的存储空间代价显著地提高了单个网络的预测精度和泛化能力,具有良好的应用价值。更多还原

【Abstract】 Against that in the application of short term load forecasting in power system,single neural networks having problems like low forecasting accuracy,unstable forecasting results and poor generalization ability,the paper proposes a new model based on multiural networks that is adaptively integrated.The forecasting experimental results to practical load data of some local area show that this method notably improves the forecasting accuracy and generalization ability of single neural networks,with tiny operation cost and less memory space,so it has good application value.更多还原