forked from EEBD_AI/wgz_forecast
58 lines
2.2 KiB
Python
58 lines
2.2 KiB
Python
import numpy as np
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import pandas as pd
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import numpy as np
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from vmdpy import VMD
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from skopt import gp_minimize
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from skopt.space import Real, Integer, Categorical
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from skopt.utils import use_named_args
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# 加载数据
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def do_vmd(dataframe):
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signal = dataframe['power'].values
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# 定义VMD函数，用于优化过程
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def vmd_objective(params):
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alpha, tau, K, DC, init, tol = params
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u, u_hat, omega = VMD(signal, alpha, tau, K, DC, init, tol)
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# 定义目标函数，这里使用模态的平均带宽作为优化目标
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# 你可以根据实际需求定义其他目标函数
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objective = np.mean([np.diff(np.where(um != 0)) for um in u])
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return -objective # gp_minimize用于最小化，所以取负值
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# 定义参数空间
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space = [
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Real(low=2, high=100, prior='log-uniform', name='alpha'),
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Real(low=0, high=1, name='tau'),
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Integer(low=2, high=10, name='K'),
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Categorical(categories=[0, 1], name='DC'),
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Categorical(categories=[0, 1], name='init'),
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Real(low=1e-6, high=1e-2, prior='log-uniform', name='tol')
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]
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# 使用贝叶斯优化
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@use_named_args(space)
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def objective_function(alpha, tau, K, DC, init, tol):
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return vmd_objective((alpha, tau, K, DC, init, tol))
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result = gp_minimize(objective_function, space, n_calls=50, random_state=0)
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print('最优参数:', result.x)
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print('最优目标函数值:', -result.fun) # 取负值回到原始目标函数的值
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# 使用找到的最优参数进行VMD分解
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alpha_opt, tau_opt, K_opt, DC_opt, init_opt, tol_opt = result.x
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u_opt, u_hat_opt, omega_opt = VMD(signal, alpha_opt, tau_opt, K_opt, DC_opt, init_opt, tol_opt)
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# 保存或处理分解得到的模态函数
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best_params = [alpha_opt, tau_opt, K_opt, DC_opt, init_opt, tol_opt]
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vmd_rst = u_opt.T
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vmd_train_data = pd.concat([data2vmd, pd.DataFrame.from_records(vmd_rst, index=data2vmd.index, columns=[f"vmd_{x}"for x in range(vmd_rst.shape[1])])], axis=1)
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return vmd_train_data
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if __name__ == '__main__':
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data2vmd = pd.read_csv('./data/pv_data_hourly.csv', index_col=0)
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train_data = do_vmd(data2vmd)
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train_data.to_csv('./data/vmd_train.csv', index=False, encoding='utf-8-sig') |