准确的燃气负荷预测对于城市合理供应和调度能源起着非常重要的作用. 由于燃气负荷数据本身具有周期性, 随机性的复杂特点以及单阶段单预测模型的局限性, 本文提出了一种基于模糊编码遗传算法(Fuzzy Coding of Genetic Algorithms, FCGA)和改进的LSTM-BPNN残差修正模型的多阶段混合模型. 首先第一阶段先用LSTM进行燃气负荷...
dim=1).type(torch.FloatTensor)[:2000] /255. # shape from (2000,28,28) to (2000,1,28,28), valueinrange(0,1)test_y= test_loader.test_labels[:2000]# 定义模型classBPNN(nn.Module):
Algorithms, FCGA)和改进的LSTM-BPNN 残差修正模型的多阶段混合模型. 首先第一阶段先用LSTM 进行燃气负荷初步预测, 然后计算出燃气负荷残差值, 第二阶段先用BPNN 去预测残差值, 然后用Adam 自适应学习率算法在学习过程中自动调节LSTM-BPNN 残差模型的学习率, 加快拟合速度, 接着用模糊编码遗传算法去优化BPNN 的...
利用K均值聚类及分段三次Hermite插值法,实现了样本数据的等距化;搭建LSTM-BPNN-SVR预测模型强化了对样本数据特征信息的提取,实现了对地铁车辆轴箱温度高精度预测的目的.通过与实测数据进行对比,该模型预测精度可达到99.15%,验证了该方法的有效性;通过与其他六种常规模型的预测结果对比,LSTM-BPNN-SVR预测模型具有更高的...
首先,深度学习模型已经在各种研究领域中取得了很大的发展,主要说一下自己就CNN和RNN,LSTM模型的一些理解。(适合理解不透彻或者掌握不到精髓的读者,初学者可以看上面的博客,大神可以忽略) 首先,CNN是为了获取图像或者文本的local information 的,局部信息特征的获取比较擅长。但是光靠这些局部信息也不算完美啊,而且现在很...
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LSTM–BPNN is the most appropriate here because it has excellent performance and robustness in prediction compared with six typical artificial intelligence algorithms. In terms of SHAP, this study visualizes all features to understand the technical–tactical model and uses local interpretation to diagnose...
Finally, the proposed novel hybrid Prophet-LSTM optimized by BPNN approach will be introduced. 2.1. ARIMA model selection An ARIMA model is a statistics analysis model for electrical load forecasting, and is mainly consists of three stages and three sections that are: Identification, Estimation and...
tensorflow1.1.0 + matplotlib2.0.2 + mysql-connector-python-rf2.2.2 + Structure common: common method such as data loading, model evaluation & model visualization etc. mlp: predict copper price using sklearn.neural_network.MLPRegressor lstm: predict copper price using keras.layers.recurrent.LSTM ...
Using LSTM–BPNN to calculate the SHAP value of each feature, the global results find that the receive-attack and serve-attack phases of the ending match have essential impacts on the mutual winning probabilities. Finally, case applications show that the SHAP can directly obtain each feature ...