# coding=UTF-8 ''' @Date : 2022.05.29 @Author : Jethro ''' import numpy as np import pandas as pd import matplotlib matplotlib.use('TkAgg') import matplotlib.pyplot as plt from sklearn.preprocessing import MinMaxScaler from models import modelss from Decomposition import Decomposition from sklearn.metrics import mean_squared_error as mse from sklearn.metrics import mean_absolute_error as mae from sklearn.metrics import r2_score as r2 import warnings warnings.filterwarnings('ignore') def mape(y_true, y_pred): return np.mean(np.abs((y_pred - y_true) / y_true)) * 100 def denoise(data, imfs): data = data.reshape(-1) denoise_data = 0 for i in range(imfs.shape[1]): denoise_data += imfs[:,i] pearson_corr_coef = np.corrcoef(denoise_data, data) if pearson_corr_coef[0,1] >=0.995: print(i) break return denoise_data def IMF_decomposition(data, length): Decomp = Decomposition(data, length) # emd_imfs = Decomp.EMD() # eemd_imfs = Decomp.EEMD() # vmd_imfs = Decomp.VMD() ssa_imfs = Decomp.SSA() # emd_denoise = denoise(data, emd_imfs) # eemd_denoise = denoise(data, eemd_imfs) # vmd_denoise = denoise(data, vmd_imfs) ssa_denoise = denoise(data, ssa_imfs) # emd_denoise, eemd_denoise, vmd_denoise, return ssa_denoise def Data_partitioning(data,test_number, input_step, pre_step): # 导入数据 dataset = data.reshape(-1,1) test_number = test_number # #归一化 scaled_tool = MinMaxScaler(feature_range=[0, 1]) data_scaled = scaled_tool.fit_transform(dataset) # 切片 step_size = input_step data_input= np.zeros((len(data_scaled) - step_size - pre_step, step_size)) data_label = np.zeros((len(data_scaled) - step_size - pre_step, 1)) for i in range(len(data_scaled) - step_size-pre_step): data_input[i, :] = data_scaled[i:step_size + i,0] data_label[i, 0] = data_scaled[step_size + i + pre_step,0] # data_label = data_scaled[step_size+1:,0] # 划分数据集 X_train = data_input[:-test_number] Y_train = data_label[:-test_number] X_test = data_input[-test_number:] Y_test = data_label[-test_number:] return X_train, X_test, Y_train, Y_test, scaled_tool def single_model(data,test_number,flag, input_step, pre_step): X_train, X_test, Y_train, Y_test, scaled_tool = Data_partitioning(data, test_number, input_step, pre_step) model = modelss(X_train, X_test, Y_train, Y_test, scaled_tool) if flag == 'tcn_gru': pre = model.run_tcn_gru() if flag == 'tcn_lstm': pre = model.run_tcn_lstm() if flag == 'tcn_rnn': pre = model.run_tcn_rnn() if flag == 'tcn_bpnn': pre = model.run_tcn_bpnn() if flag == 'gru': pre = model.run_GRU() if flag == 'lstm': pre = model.run_LSTM() if flag == 'rnn': pre = model.run_RNN() if flag == 'bpnn': pre = model.run_BPNN() data_pre = pre[:, 0] return data_pre if __name__ == '__main__': test_number, imfs_number, input_step, pre_step= 200, 15, 20, 2 data = pd.read_csv('10 min wind speed data.csv', header= None) ssa_denoise = IMF_decomposition(data.iloc[:,2].values, imfs_number) np.savetxt('ssa_denoise_3.csv',ssa_denoise[-test_number:],delimiter = ',') # pre_emd = single_model(emd_denoise, test_number, 'tcn_gru', input_step, pre_step) # pre_eemd = single_model(eemd_denoise, test_number, 'tcn_gru', input_step, pre_step) # pre_vmd = single_model(vmd_denoise, test_number, 'tcn_gru', input_step, pre_step) pre_ssa_tcn_gru = single_model(ssa_denoise, test_number, 'tcn_gru', input_step, pre_step) pre_ssa_tcn_lstm = single_model(ssa_denoise, test_number, 'tcn_lstm', input_step, pre_step) pre_ssa_tcn_rnn = single_model(ssa_denoise, test_number, 'tcn_rnn', input_step, pre_step) pre_ssa_tcn_bpnn = single_model(ssa_denoise, test_number, 'tcn_bpnn', input_step, pre_step) pre_ssa_gru = single_model(ssa_denoise, test_number, 'gru', input_step, pre_step) pre_ssa_lstm = single_model(ssa_denoise, test_number, 'lstm', input_step, pre_step) pre_ssa_rnn = single_model(ssa_denoise, test_number, 'rnn', input_step, pre_step) pre_ssa_bpnn = single_model(ssa_denoise, test_number, 'bpnn', input_step, pre_step) # np.savetxt('pre_emd.csv', pre_emd, delimiter=',') # np.savetxt('pre_eemd.csv', pre_eemd, delimiter=',') # np.savetxt('pre_vmd.csv', pre_vmd, delimiter=',') np.savetxt('pre_ssa_tcn_gru.csv', pre_ssa_tcn_gru, delimiter=',') np.savetxt('pre_ssa_tcn_lstm.csv', pre_ssa_tcn_lstm, delimiter=',') np.savetxt('pre_ssa_tcn_rnn.csv', pre_ssa_tcn_rnn, delimiter=',') np.savetxt('pre_ssa_tcn_bpnn.csv', pre_ssa_tcn_bpnn, delimiter=',') np.savetxt('pre_ssa_gru.csv', pre_ssa_gru, delimiter=',') np.savetxt('pre_ssa_lstm.csv', pre_ssa_lstm, delimiter=',') np.savetxt('pre_ssa_rnn.csv', pre_ssa_rnn, delimiter=',') np.savetxt('pre_ssa_bpnn.csv', pre_ssa_bpnn, delimiter=',') Actual = ssa_denoise[-test_number:] print('实验一：不同分解方法对预测结果影响') # print('#########################') # print('EMD分解方法： MAE : ', mae(Actual, pre_emd)) # print('EMD分解方法： R2 : ', mape(Actual, pre_emd)) # print('EMD分解方法： RMSE : ', np.sqrt(mse(Actual, pre_emd))) # print('#########################') # print('EEMD分解方法： MAE : ', mae(Actual, pre_eemd)) # print('EEMD分解方法： R2 : ', mape(Actual, pre_eemd)) # print('EEMD分解方法： RMSE : ', np.sqrt(mse(Actual, pre_eemd))) # print('#########################') # print('VMD分解方法： MAE : ', mae(Actual, pre_vmd)) # print('VMD分解方法： R2 : ', mape(Actual, pre_vmd)) # print('VMD分解方法： RMSE : ', np.sqrt(mse(Actual, pre_vmd))) # print('#########################') print('SSA分解方法： MAE : ', mae(Actual, pre_ssa_tcn_gru)) print('SSA分解方法： R2 : ', mape(Actual, pre_ssa_tcn_gru)) print('SSA分解方法： RMSE : ', np.sqrt(mse(Actual, pre_ssa_tcn_gru))) print('实验二：采用不同时间信息提取模型对实验结果影响') print('#########################') print('TCN-LSTM方法： MAE : ', mae(Actual, pre_ssa_tcn_lstm)) print('TCN-LSTM方法： R2 : ', mape(Actual, pre_ssa_tcn_lstm)) print('TCN-LSTM方法： RMSE : ', np.sqrt(mse(Actual, pre_ssa_tcn_lstm))) print('#########################') print('TCN-RNN方法： MAE : ', mae(Actual, pre_ssa_tcn_rnn)) print('TCN-RNN方法： R2 : ', mape(Actual, pre_ssa_tcn_rnn)) print('TCN-RNN方法： RMSE : ', np.sqrt(mse(Actual, pre_ssa_tcn_rnn))) print('#########################') print('TCN-BPNN方法： MAE : ', mae(Actual, pre_ssa_tcn_bpnn)) print('TCN-BPNN方法： R2 : ', mape(Actual, pre_ssa_tcn_bpnn)) print('TCN-BPNN方法： RMSE : ', np.sqrt(mse(Actual, pre_ssa_tcn_bpnn))) print('实验三：与基模性对比') print('#########################') print('GRU方法： MAE : ', mae(Actual, pre_ssa_gru)) print('GRU方法方法： R2 : ', mape(Actual, pre_ssa_tcn_lstm)) print('GRU方法方法： RMSE : ', np.sqrt(mse(Actual, pre_ssa_tcn_lstm))) print('#########################') print('LSTM方法： MAE : ', mae(Actual, pre_ssa_lstm)) print('LSTM方法： R2 : ', mape(Actual, pre_ssa_lstm)) print('LSTM方法： RMSE : ', np.sqrt(mse(Actual, pre_ssa_lstm))) print('#########################') print('RNN方法： MAE : ', mae(Actual, pre_ssa_rnn)) print('RNN方法： R2 : ', mape(Actual, pre_ssa_rnn)) print('RNN方法： RMSE : ', np.sqrt(mse(Actual, pre_ssa_rnn))) print('#########################') print('BPNN方法： MAE : ', mae(Actual, pre_ssa_bpnn)) print('BPNN方法： R2 : ', mape(Actual, pre_ssa_bpnn)) print('BPNN方法： RMSE : ', np.sqrt(mse(Actual, pre_ssa_bpnn))) plt.figure(2) plt.plot(pre_ssa_tcn_gru, color = 'black', label= 'pre_ssa_tcn_gru') plt.plot(pre_ssa_tcn_lstm, color= 'm', label= 'pre_ssa_tcn_lstm') plt.plot(pre_ssa_tcn_rnn, color= 'y', label= 'pre_ssa_tcn_rnn') plt.plot(pre_ssa_tcn_bpnn, color = 'red', label= 'pre_ssa_tcn_bpnn') plt.plot(pre_ssa_gru, color= 'y', label= 'pre_ssa_gru') plt.plot(pre_ssa_lstm, color = 'red', label= 'pre_ssa_lstm') plt.plot(Actual, color= 'blue', label= 'Actual') plt.legend() plt.show()