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