考虑到新型电力负荷随机性增强，传统的准确预测方法已无法满足要求，提出一种EMD-BiLSTM-ANFIS(Empirical Mode Decomposition-Bi-directional Long Short-Term Memory-Adaptive Network-based Fuzzy Inference System)分位数预测负荷概率密度的方法，使用负荷预测区间取代点预测的准确数值，能为电力系统分析与决策提供更多数据，增强预测的可靠性。首先将原始负荷序列通过EMD(Empirical Mode Decomposition)分解成若干分量，并通过计算样本熵分为3类分量。然后将重构后的3类分量与由相关性筛选的外界因素特征采用BiLSTM、ANFIS模型进行训练和分位数回归(QR:Quantile Regression)，并将分量的预测区间结果累加得到最终负荷的预测区间。最后利用核密度估计输出任意时刻用户负荷概率密度预测结果。通过与CNN-BiLSTM(Convolutional Neural Network-Bidirectional Long Short-Term Memory)、LSTM(Long Short-Term Memory)模型对比点预测及区间预测结果，证明了该方法的有效性。
Load Interval Forecast Based on EMD-BiLSTM-ANFIS
Considering that the randomness of the new power load is enhanced,the traditional accurate forecasting methods can not meet the requirements,an EMD-BiLSTM-ANFIS(Empirical Mode Decomposition Bi-directional Long Short Term Memory Adaptive Network is proposed based Fuzzy Inference System)quantile method to predict the load probability density.It replaces the accurate value of point prediction with the load prediction interval,which can provide more data for power System analysis and decision-making,The reliability of prediction is enhanced.First,the original load sequence is decomposed into several components by EMD,and then divided into three types of components by calculating the sample entropy.Then,the reconstructed three types of components and the characteristics of external factors screened by correlation.And they are used together with the Bilstm and ANFIS models for prediction training and QR(Quantile Regression),and accumulate the results of the prediction interval of the components to obtain the prediction interval of the final load.Finally,the kernel density estimation is used to output the user load probability density prediction results at any time.The validity of this method is proved by comparing the point prediction and interval prediction results with CNN-BiLSTM(Convolutional Neural Network-Bidirectional Long Short-Term Memory)and LSTM(Long Short-Term Memory)models.
empirical mode decompositiontwo way long and short term neural networkfuzzy inference systemquantile regressionprobability density prediction
万方数据
维普
