get 1 year ago
parent 359effce4c
commit 8a2d8f2a9a

@ -0,0 +1,81 @@
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@ -0,0 +1,81 @@
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@ -0,0 +1,81 @@
,real,pred
0,50.73,-3852886.8
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1 real pred
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@ -0,0 +1,81 @@
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59,2501636.637,2571584.0
60,2178461.623,2486985.5
61,2333089.921,2497864.5
62,2464330.824,2443193.2
63,2501636.637,2499882.5
64,2391662.7,2499099.0
65,2333089.921,2403869.2
66,2464330.824,2416259.8
67,2501636.637,2374731.2
68,2391662.7,2414558.5
69,2132738.014,2411228.2
70,2464330.824,2412634.2
71,2501636.637,2417201.2
72,2391662.7,2387954.0
73,2132738.014,2420058.0
74,2089421.05,2411550.0
75,2501636.637,2422625.8
76,2391662.7,2422028.2
77,2132738.014,2402778.5
78,2089421.05,2425684.2
79,2169828.292,2417022.0
1 real pred
2 0 2704531.598 -2316342.0
3 1 2847401.395 20676706.0
4 2 2853452.254 621314.75
5 3 1445328.766 19298740.0
6 4 2859827.803 15913489.0
7 5 2847401.395 160415.81
8 6 2853452.254 11473635.0
9 7 1445328.766 1780781.9
10 8 2859827.803 9122751.0
11 9 2596527.222 9748031.0
12 10 2853452.254 1740487.1
13 11 1445328.766 7060606.5
14 12 2859827.803 2215774.0
15 13 2596527.222 5502353.0
16 14 2493576.394 6607667.0
17 15 1445328.766 2590090.2
18 16 2859827.803 4988868.5
19 17 2596527.222 2414713.2
20 18 2493576.394 4189292.0
21 19 2590630.491 5007022.5
22 20 2859827.803 2663833.5
23 21 2596527.222 3738563.2
24 22 2493576.394 2267029.0
25 23 2590630.491 3384996.8
26 24 2542066.764 3895295.0
27 25 2596527.222 2791455.5
28 26 2493576.394 3239150.8
29 27 2590630.491 2324025.0
30 28 2542066.764 3109880.2
31 29 2348001.351 3393537.5
32 30 2493576.394 2799275.0
33 31 2590630.491 2971549.0
34 32 2542066.764 2372881.5
35 33 2348001.351 2928644.5
36 34 2358799.425 3085286.2
37 35 2590630.491 2734414.0
38 36 2542066.764 2794325.2
39 37 2348001.351 2387036.8
40 38 2358799.425 2778122.0
41 39 2280758.623 2866578.2
42 40 2542066.764 2720565.0
43 41 2348001.351 2730321.8
44 42 2358799.425 2451633.2
45 43 2280758.623 2726225.2
46 44 2178461.623 2774300.0
47 45 2348001.351 2671150.2
48 46 2358799.425 2668631.5
49 47 2280758.623 2480967.5
50 48 2178461.623 2668544.2
51 49 2333089.921 2699806.2
52 50 2358799.425 2573983.0
53 51 2280758.623 2581621.8
54 52 2178461.623 2458319.0
55 53 2333089.921 2591283.8
56 54 2464330.824 2604670.0
57 55 2280758.623 2550985.0
58 56 2178461.623 2560103.5
59 57 2333089.921 2483460.2
60 58 2464330.824 2577773.5
61 59 2501636.637 2571584.0
62 60 2178461.623 2486985.5
63 61 2333089.921 2497864.5
64 62 2464330.824 2443193.2
65 63 2501636.637 2499882.5
66 64 2391662.7 2499099.0
67 65 2333089.921 2403869.2
68 66 2464330.824 2416259.8
69 67 2501636.637 2374731.2
70 68 2391662.7 2414558.5
71 69 2132738.014 2411228.2
72 70 2464330.824 2412634.2
73 71 2501636.637 2417201.2
74 72 2391662.7 2387954.0
75 73 2132738.014 2420058.0
76 74 2089421.05 2411550.0
77 75 2501636.637 2422625.8
78 76 2391662.7 2422028.2
79 77 2132738.014 2402778.5
80 78 2089421.05 2425684.2
81 79 2169828.292 2417022.0

@ -86,7 +86,7 @@ for excel in os.listdir(file_dir)[1:]:
dataset_y = np.concatenate((dataset_y,y)) dataset_y = np.concatenate((dataset_y,y))
print(dataset_x.shape,dataset_y.shape)
# 训练 # 训练
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
@ -112,22 +112,23 @@ train_y = torch.from_numpy(train_y).to(device).type(torch.float32)
model = LSTM_Regression(DAYS_FOR_TRAIN, 32, output_size=5, num_layers=2).to(device) # 导入模型并设置模型的参数输入输出层、隐藏层等 model = LSTM_Regression(DAYS_FOR_TRAIN, 32, output_size=5, num_layers=2).to(device) # 导入模型并设置模型的参数输入输出层、隐藏层等
train_loss = [] # train_loss = []
loss_function = nn.MSELoss() # loss_function = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.005, betas=(0.9, 0.999), eps=1e-08, weight_decay=0) # optimizer = torch.optim.Adam(model.parameters(), lr=0.005, betas=(0.9, 0.999), eps=1e-08, weight_decay=0)
for i in range(1500): # for i in range(1500):
out = model(train_x) # out = model(train_x)
loss = loss_function(out, train_y) # loss = loss_function(out, train_y)
loss.backward() # loss.backward()
optimizer.step() # optimizer.step()
optimizer.zero_grad() # optimizer.zero_grad()
train_loss.append(loss.item()) # train_loss.append(loss.item())
if i % 100 == 0: # if i % 100 == 0:
print(f'epoch {i+1}: loss:{loss}') # print(f'epoch {i+1}: loss:{loss}')
# 保存模型 # 保存/读取模型
torch.save(model.state_dict(),'hy5.pth') # torch.save(model.state_dict(),'hy5.pth')
model.load_state_dict(torch.load('hy5.pth'))
# for test # for test
model = model.eval() # 转换成测试模式 model = model.eval() # 转换成测试模式
# model.load_state_dict(torch.load(os.path.join(model_save_dir,model_file))) # 读取参数 # model.load_state_dict(torch.load(os.path.join(model_save_dir,model_file))) # 读取参数
@ -139,25 +140,40 @@ pred_test = model(dataset_x) # 全量训练集
pred_test = pred_test.view(-1) pred_test = pred_test.view(-1)
pred_test = np.concatenate((np.zeros(DAYS_FOR_TRAIN), pred_test.cpu().detach().numpy())) pred_test = np.concatenate((np.zeros(DAYS_FOR_TRAIN), pred_test.cpu().detach().numpy()))
plt.plot(pred_test.reshape(-1), 'r', label='prediction') # plt.plot(pred_test.reshape(-1), 'r', label='prediction')
plt.plot(dataset_y.reshape(-1), 'b', label='real') # plt.plot(dataset_y.reshape(-1), 'b', label='real')
plt.plot((train_size*5, train_size*5), (0, 1), 'g--') # 分割线 左边是训练数据 右边是测试数据的输出 # plt.plot((train_size*5, train_size*5), (0, 1), 'g--') # 分割线 左边是训练数据 右边是测试数据的输出
plt.legend(loc='best') # plt.legend(loc='best')
plt.show() # plt.show()
# 创建测试集 # 创建测试集
# result_list = []
# 以x为基础实际数据滚动预测未来3天
# x = torch.from_numpy(df[-14:-4]).to(device)
# pred = model(x.reshape(-1,1,DAYS_FOR_TRAIN)).view(-1).detach().numpy()
df_eval = pd.read_excel(r'C:\Users\user\Desktop\浙江各地市行业电量数据\ 杭州 .xlsx',index_col='stat_date')
df_eval.columns = df_eval.columns.map(lambda x:x.strip())
df_eval.index = pd.to_datetime(df_eval.index)
x,y = create_dataset(df_eval.loc['2023-7']['第三产业'],10)
x = (x - min_value) / (max_value - min_value)
x = x.reshape(-1,1,10)
x = torch.from_numpy(x).type(torch.float32).to(device)
pred = model(x)
# 反归一化
pred = pred * (max_value - min_value) + min_value
# df = df * (max_value - min_value) + min_value
df = pd.DataFrame({'real':y.reshape(-1),'pred':pred.view(-1).cpu().detach().numpy()})
print(df)
df.to_csv('7月第三产业.csv',encoding='gbk')
# 反归一化 # 反归一化
# pred = pred * (max_value - min_value) + min_value # pred = pred * (max_value - min_value) + min_value
# df = df * (max_value - min_value) + min_value # df = df * (max_value - min_value) + min_value
# print(pred)
# # 打印指标 # # 打印指标
# print(abs(pred - df[-3:]).mean() / df[-3:].mean()) # print(abs(pred - df[-3:]).mean() / df[-3:].mean())
# result_eight = pd.DataFrame({'pred': np.round(pred,1),'real': df[-3:]}) # result_eight = pd.DataFrame({'pred': np.round(pred,1),'real': df[-3:]})

@ -0,0 +1,195 @@
import numpy as np
import pandas as pd
import torch
from torch import nn
from multiprocessing import Pool
import matplotlib.pyplot as plt
import os
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
DAYS_FOR_TRAIN = 10
torch.manual_seed(42)
class LSTM_Regression(nn.Module):
def __init__(self, input_size, hidden_size, output_size=1, num_layers=2):
super().__init__()
self.lstm = nn.LSTM(input_size, hidden_size, num_layers)
self.fc = nn.Linear(hidden_size, output_size)
def forward(self, _x):
x, _ = self.lstm(_x) # _x is input, size (seq_len, batch, input_size)
s, b, h = x.shape # x is output, size (seq_len, batch, hidden_size)
x = x.view(s * b, h)
x = self.fc(x)
x = x.view(s, b, -1) # 把形状改回来
return x
def create_dataset(data, days_for_train=5) -> (np.array, np.array):
dataset_x, dataset_y = [], []
for i in range(len(data) - days_for_train-5):
dataset_x.append(data[i:(i + days_for_train)])
dataset_y.append(data[i + days_for_train:i + days_for_train+5])
# print(dataset_x,dataset_y)
return (np.array(dataset_x), np.array(dataset_y))
def normal(nd):
high = nd.describe()['75%'] + 1.5*(nd.describe()['75%']-nd.describe()['25%'])
low = nd.describe()['25%'] - 1.5*(nd.describe()['75%']-nd.describe()['25%'])
return nd[(nd<high)&(nd>low)]
def data_preprocessing(data):
data.columns = data.columns.map(lambda x: x.strip())
data.index = pd.to_datetime(data.index)
data.sort_index(inplace=True)
data = data.loc['2021-01':'2023-08']
data.drop(columns=[i for i in data.columns if (data[i] == 0).sum() / len(data) >= 0.5], inplace=True) # 去除0值列
data = data[data.values != 0]
data = data.astype(float)
for col in data.columns:
data[col] = normal(data[col])
return data
if __name__ == '__main__':
# 拼接数据集
file_dir = r'C:\Users\user\Desktop\浙江各地市分电压日电量数据'
excel = os.listdir(file_dir)[0]
data = pd.read_excel(os.path.join(file_dir, excel), sheet_name=0, index_col=' stat_date ')
data = data_preprocessing(data)
df = data[data.columns[0]]
df.dropna(inplace = True)
dataset_x, dataset_y = create_dataset(df, DAYS_FOR_TRAIN)
for level in data.columns[1:]:
df = data[level]
df.dropna(inplace=True)
x, y = create_dataset(df, DAYS_FOR_TRAIN)
dataset_x = np.concatenate((dataset_x, x))
dataset_y = np.concatenate((dataset_y, y))
for excel in os.listdir(file_dir)[1:]:
data = pd.read_excel(os.path.join(file_dir,excel), sheet_name=0,index_col=' stat_date ')
data = data_preprocessing(data)
for level in data.columns:
df = data[level]
df.dropna(inplace=True)
x,y = create_dataset(df,DAYS_FOR_TRAIN)
dataset_x = np.concatenate((dataset_x,x))
dataset_y = np.concatenate((dataset_y,y))
print(dataset_x,dataset_y,dataset_x.shape,dataset_y.shape)
# 训练
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# 标准化到0~1
max_value = np.max(dataset_x)
min_value = np.min(dataset_x)
dataset_x = (dataset_x - min_value) / (max_value - min_value)
dataset_y = (dataset_y - min_value) / (max_value - min_value)
# 划分训练集和测试集
train_size = int(len(dataset_x)*0.7)
train_x = dataset_x[:train_size]
train_y = dataset_y[:train_size]
# 将数据改变形状RNN 读入的数据维度是 (seq_size, batch_size, feature_size)
train_x = train_x.reshape(-1, 1, DAYS_FOR_TRAIN)
train_y = train_y.reshape(-1, 1, 5)
# 转为pytorch的tensor对象
train_x = torch.from_numpy(train_x).to(device).type(torch.float32)
train_y = torch.from_numpy(train_y).to(device).type(torch.float32)
model = LSTM_Regression(DAYS_FOR_TRAIN, 32, output_size=5, num_layers=2).to(device) # 导入模型并设置模型的参数输入输出层、隐藏层等
train_loss = []
loss_function = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.005, betas=(0.9, 0.999), eps=1e-08, weight_decay=0)
# for i in range(1500):
# out = model(train_x)
# loss = loss_function(out, train_y)
# loss.backward()
# optimizer.step()
# optimizer.zero_grad()
# train_loss.append(loss.item())
# # print(loss)
# # 保存模型
# torch.save(model.state_dict(),'dy5.pth')
model.load_state_dict(torch.load('dy5.pth'))
# for test
model = model.eval() # 转换成测试模式
# model.load_state_dict(torch.load(os.path.join(model_save_dir,model_file))) # 读取参数
dataset_x = dataset_x.reshape(-1, 1, DAYS_FOR_TRAIN) # (seq_size, batch_size, feature_size)
dataset_x = torch.from_numpy(dataset_x).to(device).type(torch.float32)
pred_test = model(dataset_x) # 全量训练集
# 模型输出 (seq_size, batch_size, output_size)
pred_test = pred_test.view(-1)
pred_test = np.concatenate((np.zeros(DAYS_FOR_TRAIN), pred_test.cpu().detach().numpy()))
# plt.plot(pred_test.reshape(-1), 'r', label='prediction')
# plt.plot(dataset_y.reshape(-1), 'b', label='real')
# plt.plot((train_size*5, train_size*5), (0, 1), 'g--') # 分割线 左边是训练数据 右边是测试数据的输出
# plt.legend(loc='best')
# plt.show()
# 创建测试集
# result_list = []
# 以x为基础实际数据滚动预测未来3天
df_eval = pd.read_excel(r'C:\Users\user\Desktop\浙江各地市分电压日电量数据\杭州.xlsx',index_col=' stat_date ')
df_eval.columns = df_eval.columns.map(lambda x:x.strip())
df_eval.index = pd.to_datetime(df_eval.index)
x,y = create_dataset(df_eval.loc['2023-7']['10kv以下'],10)
x = (x - min_value) / (max_value - min_value)
x = x.reshape(-1,1,10)
x = torch.from_numpy(x).type(torch.float32).to(device)
pred = model(x)
# 反归一化
pred = pred * (max_value - min_value) + min_value
# df = df * (max_value - min_value) + min_value
print(pred,y)
df = pd.DataFrame({'real':y.reshape(-1),'pred':pred.view(-1).cpu().detach().numpy()})
df.to_csv('7月全行业.csv',encoding='gbk')
# 打印指标
# print(abs(pred - df[-3:]).mean() / df[-3:].mean())
# result_eight = pd.DataFrame({'pred': np.round(pred,1),'real': df[-3:]})
# target = (result_eight['pred'].sum() - result_eight['real'].sum()) / df[-31:].sum()
# result_eight['loss_rate'] = round(target, 5)
# result_eight['level'] = level
# list_app.append(result_eight)
# print(target)
# print(result_eight)
# final_df = pd.concat(list_app,ignore_index=True)
# final_df.to_csv('市行业电量.csv',encoding='gbk')
# print(final_df)
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