基于SA-DBN的超短期电力负荷预测

A Very Short-term Electric Load Forecasting Based on SA-DBN

针对超短期电力负荷预测,提出一种使用集合经验模态分解与样本熵对原始数据预处理,再用模拟退火算法优化深度置信网络的组合模型进行预测。为了减小时间序列数据因自相关性导致预测值滞后于真实值,对原始序列采用EEMD分解,根据各序列的SE值将序列重构,再使用SA对DBN各隐含层节点数寻优构成的SA-DBN模型对重构后的序列分别预测,将各序列的预测结果叠加得到最终的预测曲线。实验结果表明,相比于其他预测模型,本文模型能消除预测曲线的滞后性,预测的误差指标MAPE、MAE、RMSE分别降为1.9592%、9.4239、11.9771,并且将模型的预测精度提高到96.435%。

Aiming at the very short-term electric load forecasting,a combined model is proposed,which uses EEMD(ensemble empirical mode decomposition)and SE(sample entropy)to preprocess the original data,and then applies SA(simulated annealing)to optimize the deep belief network for forecasting.In order to reduce the time-series data of the predicted value behind the real value caused by the autocorrelation of the data,the original sequence is decomposed by EEMD,the sequence is reconstructed according to the SE of each decomposed sequence,and the reconstructed sequence is predicted separately by SA-DBN model composed of SA optimizing the number of nodes in each hidden layer of DBN,and the predicted results of each sequence are superimposed to obtain the final prediction curve.The experimental results show that compared with other prediction models,this model can eliminate the lag of prediction curve,the predicted error indexes MAPE,MAE and RMSE are reduced to 1.9592%,9.4239 and 11.9771 respectively,and the prediction accuracy of the model is increased to 96.435%.