From 8a2d8f2a9a19c7c575526a3be72a8620704146bb Mon Sep 17 00:00:00 2001 From: get <2316994765@qq.com> Date: Tue, 31 Oct 2023 00:13:31 +0800 Subject: [PATCH] , --- 浙江行业电量/7月全行业.csv | 81 ++++++++ 浙江行业电量/7月第三产业.csv | 81 ++++++++ 浙江行业电量/7月预测1.csv | 81 ++++++++ 浙江行业电量/7月预测2.csv | 81 ++++++++ 浙江行业电量/行业电量_输出为5.py | 66 +++--- 电压等级_输出为5.py | 195 ++++++++++++++++++ 6 files changed, 560 insertions(+), 25 deletions(-) create mode 100644 浙江行业电量/7月全行业.csv create mode 100644 浙江行业电量/7月第三产业.csv create mode 100644 浙江行业电量/7月预测1.csv create mode 100644 浙江行业电量/7月预测2.csv create mode 100644 电压等级_输出为5.py diff --git a/浙江行业电量/7月全行业.csv b/浙江行业电量/7月全行业.csv new file mode 100644 index 0000000..1eafbe3 --- /dev/null +++ b/浙江行业电量/7月全行业.csv @@ -0,0 +1,81 @@ +,real,pred +0,366314897.6,325991300.0 +1,380820733.0,290698080.0 +2,380238801.9,257544720.0 +3,244227473.0,281246980.0 +4,364073058.1,275352350.0 +5,380820733.0,347751680.0 +6,380238801.9,321158200.0 +7,244227473.0,292571360.0 +8,364073058.1,298532380.0 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+79,291360430.5,316049300.0 diff --git a/浙江行业电量/7月第三产业.csv b/浙江行业电量/7月第三产业.csv new file mode 100644 index 0000000..02c37c6 --- /dev/null +++ b/浙江行业电量/7月第三产业.csv @@ -0,0 +1,81 @@ +,real,pred +0,135488382.9,126543270.0 +1,139728296.4,132820504.0 +2,137821025.8,111796240.0 +3,78600422.1,131913600.0 +4,126378434.7,121779730.0 +5,139728296.4,133275510.0 +6,137821025.8,132085740.0 +7,78600422.1,123508616.0 +8,126378434.7,122062880.0 +9,112584670.1,126883176.0 +10,137821025.8,127550950.0 +11,78600422.1,127659760.0 +12,126378434.7,120534296.0 +13,112584670.1,120153140.0 +14,126043317.8,126331544.0 +15,78600422.1,124620616.0 +16,126378434.7,125789710.0 +17,112584670.1,120741016.0 +18,126043317.8,122845240.0 +19,127571038.2,126469840.0 +20,126378434.7,89965520.0 +21,112584670.1,97125790.0 +22,126043317.8,97640040.0 +23,127571038.2,97961870.0 +24,126803223.9,100093896.0 +25,112584670.1,118327300.0 +26,126043317.8,119570870.0 +27,127571038.2,120226640.0 +28,126803223.9,122849880.0 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+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 diff --git a/浙江行业电量/行业电量_输出为5.py b/浙江行业电量/行业电量_输出为5.py index c0ddf93..9f53179 100644 --- a/浙江行业电量/行业电量_输出为5.py +++ b/浙江行业电量/行业电量_输出为5.py @@ -86,7 +86,7 @@ for excel in os.listdir(file_dir)[1:]: dataset_y = np.concatenate((dataset_y,y)) - +print(dataset_x.shape,dataset_y.shape) # 训练 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 -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()) - if i % 100 == 0: - print(f'epoch {i+1}: loss:{loss}') +# 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()) +# if i % 100 == 0: +# print(f'epoch {i+1}: loss:{loss}') -# 保存模型 -torch.save(model.state_dict(),'hy5.pth') +# 保存/读取模型 +# torch.save(model.state_dict(),'hy5.pth') +model.load_state_dict(torch.load('hy5.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 @@ -139,25 +140,40 @@ pred_test = model(dataset_x) # 全量训练集 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() +# 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 -# 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 # pred = pred * (max_value - min_value) + min_value # df = df * (max_value - min_value) + min_value -# print(pred) + # # 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:]}) diff --git a/电压等级_输出为5.py b/电压等级_输出为5.py new file mode 100644 index 0000000..a1c9db0 --- /dev/null +++ b/电压等级_输出为5.py @@ -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[(ndlow)] + +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) + + +