基于EEMD-BP神经网络的含电采暖的配电变压器短期负荷预测

Short-term load forecasting of distribution transformer with electric heating based on EEMD-BP neutral network

为了快速准确地预测含高比例电采暖设备的配电变压器的短期负荷,提出了基于集成经验模态分解(Ensemble Empirical Mode Decomposition,EEMD)及BP神经网络算法组合的含电采暖的配电变压器短期负荷预测方法,该方法考虑了采暖日天气类型、采暖日温度等环境条件对居民采暖行为的影响。首先运用EEMD方法将日负荷序列分解成4组频率由低至高的分量序列及1组剩余分量序列,再将各分量序列及温度数据、气象数据输入BP神经网络中进行预测,最后各个预测分量相加得到最终的预测结果。将该方法应用于北京地区冬季"煤改电"工程中,对某个含高比例电采暖负荷的配电变压器进行短期预测,算例表明,EEMD-BP组合预测方法能够有效减小负荷预测误差。

In order to quickly and accurately conduct the short-term load forecasting of distribution transformer with electric heating equipments,a method in which the back propagation（ BP） neural network algorithm is intelligently combined with ensemble empirical mode decomposition（ EEMD） is proposed considering the influence of weather type and temperature on the heating behavior of residents. Firstly,the daily load sequence is decomposed into four series of low-to-high frequency sub-sequences and a remnant sub-sequence by EEMD method. Secondly,each sub-sequence,temperature data and meteorological data are input into the BP neural network to predict. Finally,the predicted components are summed to obtain the final prediction result. EEMD-BP combined method is applied in Coal-to-electricity project and forecasts a certain distribution network load with a large proportion of electric heating. Simulation results show that EEMD-BP combined forecasting method can effectively reduce the prediction error.