diff --git a/各地级市日电量模型/丽水.py b/各地级市日电量模型/丽水.py index 6d99fc4..39c694c 100644 --- a/各地级市日电量模型/丽水.py +++ b/各地级市日电量模型/丽水.py @@ -24,12 +24,10 @@ print(len(df_eval),len(df_train),len(data)) df_train = df_train[['tem_max','tem_min','holiday','24ST','rh','prs','售电量']] - # IQR = df['售电量'].describe()['75%'] - df['售电量'].describe()['25%'] # high = df['售电量'].describe()['75%'] + 1.5*IQR # low = df['售电量'].describe()['25%'] - 1.5*IQR # print('异常值数量：',len(df[(df['售电量'] >= high) | (df['售电量'] <= low)])) -# # df_train = df_train[(df['售电量'] <= high) & (df['售电量'] >= low)] diff --git a/文档处理.py b/文档处理.py new file mode 100644 index 0000000..798f839 --- /dev/null +++ b/文档处理.py @@ -0,0 +1,24 @@ +import pandas as pd +import os +import re +file_dir1 = r'C:\Users\鸽子\Desktop\一版结果\电压等级电量预测结果\偏差率' +file_dir2 = r'C:\Users\鸽子\Desktop\一版结果\电压等级电量预测结果\月底3天预测结果' +file_dir3 = r'C:\Users\鸽子\Desktop\一版结果\行业电量预测结果\偏差' +print(os.listdir(file_dir3)) +str1 = '丽水电压等级10kv以下月底偏差率:0.00229' + +print(re.split('电压等级|月底偏差率:',str1)) +with open(os.path.join(file_dir3,'9月底偏差率.txt'),'r',encoding='utf-8') as f: + lines = f.readlines() + list_city = [] + list_industry = [] + list_loss = [] + for i in lines: + i = re.split(':|：|其中', i) + print(i) + list_city.append(i[0][:2]) + list_industry.append(i[-2].replace(i[0][:2],'')) + list_loss.append(i[-1][:-2]) +df_level = pd.DataFrame({'城市':list_city,'行业':list_industry,'偏差':list_loss}) +df_level.to_csv(os.path.join(file_dir3,'9月底偏差率.csv'),encoding='gbk') +print(df_level) diff --git a/浙江行业电量/杭州日电量数据预处理.py b/浙江行业电量/杭州日电量数据预处理.py index d6937de..853bf40 100644 --- a/浙江行业电量/杭州日电量数据预处理.py +++ b/浙江行业电量/杭州日电量数据预处理.py @@ -27,7 +27,6 @@ print(tq_df.columns) print(tq_df.head()) - print(tq_df.info()) def jq(y,x): a=365.242 * (y - 1900) + 6.2 + 15.22 * x - 1.9 * math.sin(0.262 * x) diff --git a/浙江行业电量/浙江总行业.py b/浙江行业电量/浙江总行业.py new file mode 100644 index 0000000..1c8c44b --- /dev/null +++ b/浙江行业电量/浙江总行业.py @@ -0,0 +1,158 @@ +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): + _x = data[i:(i + days_for_train)] + dataset_x.append(_x) + dataset_y.append(data[i + days_for_train]) + 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)] + + +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + +data = pd.read_excel(r'C:\Users\鸽子\Desktop\浙江133行业日电量数据.xlsx', sheet_name=0,index_col=' stat_date ') + +data.columns = data.columns.map(lambda x: x.strip()) + +data.index = pd.to_datetime(data.index,format='%Y%m%d') +data.sort_index(inplace=True) +# print(data.head()) +data = data.loc['2021-01':'2023-09'] + +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 +# print('len(data):', len(data)) + + +df_result = pd.DataFrame({'预测值':[],'实际值':[],'偏差率':[],'行业':[]}) +for industry in data.columns: + df = data[industry] + df = df[df.values != 0] # 去除0值行 + df = normal(df) + df = df.astype('float32').values # 转换数据类型 + + + # 标准化到0~1 + max_value = np.max(df) + min_value = np.min(df) + df = (df - min_value) / (max_value - min_value) + + dataset_x, dataset_y = create_dataset(df, DAYS_FOR_TRAIN) + # print('len(dataset_x:)', len(dataset_x)) + + # 划分训练集和测试集 + train_size = len(dataset_x) - 3 + 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, 1) + + # 转为pytorch的tensor对象 + train_x = torch.from_numpy(train_x).to(device) + train_y = torch.from_numpy(train_y).to(device) + + model = LSTM_Regression(DAYS_FOR_TRAIN, 32, output_size=1, 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(2000): + 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(),save_filename) + # torch.save(model.state_dict(),os.path.join(model_save_dir,model_file)) + + # 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) + + 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, 'r', label='prediction') + # plt.plot(df, 'b', label='real') + # plt.plot((train_size, train_size), (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) + for i in range(3): + next_1_8 = x[1:] + next_9 = model(x.reshape(-1,1,DAYS_FOR_TRAIN)) + # print(next_9,next_1_8) + x = torch.concatenate((next_1_8, next_9.view(-1))) + result_list.append(next_9.view(-1).item()) + + + # 反归一化 + pred = np.array(result_list) * (max_value - min_value) + min_value + + df = df * (max_value - min_value) + min_value + + + # 打印指标 + # print(abs(pred - df[-3:]).mean() / df[-3:].mean()) + + result_eight = pd.DataFrame({'预测值': np.round(pred,1),'实际值': df[-3:]}) + target = (result_eight['预测值'].sum() - result_eight['实际值'].sum()) / df[-31:].sum() + result_eight['偏差率'] = round(target, 5) + result_eight['行业'] = industry + + df_result = pd.concat((df_result,result_eight)) + print(df_result) + + + + + + # result_eight.to_csv(f'9月{excel[:2]}.txt', sep='\t', mode='a') + # with open(fr'./偏差/9月底偏差率.txt', 'a', encoding='utf-8') as f: + # f.write(f'{excel[:2]}{industry}:{round(target, 5)}\n') +