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 +9,316723431.2,319373630.0 +10,380238801.9,343127650.0 +11,244227473.0,333881150.0 +12,364073058.1,315865570.0 +13,316723431.2,321350340.0 +14,334702116.4,339486850.0 +15,244227473.0,355201730.0 +16,364073058.1,351205660.0 +17,316723431.2,340886050.0 +18,334702116.4,344197900.0 +19,340252127.5,356064770.0 +20,364073058.1,273813060.0 +21,316723431.2,292272900.0 +22,334702116.4,293830340.0 +23,340252127.5,293426180.0 +24,338685923.8,300031800.0 +25,316723431.2,349388480.0 +26,334702116.4,348153700.0 +27,340252127.5,350227100.0 +28,338685923.8,351487040.0 +29,323789488.1,347344860.0 +30,334702116.4,305196900.0 +31,340252127.5,312199360.0 +32,338685923.8,318501820.0 +33,323789488.1,309094460.0 +34,327864337.1,311822370.0 +35,340252127.5,332496220.0 +36,338685923.8,331613600.0 +37,323789488.1,330546100.0 +38,327864337.1,327573700.0 +39,308162792.6,328044900.0 +40,338685923.8,335969060.0 +41,323789488.1,331136770.0 +42,327864337.1,323793180.0 +43,308162792.6,321710080.0 +44,291282417.7,328650000.0 +45,323789488.1,319168770.0 +46,327864337.1,319473630.0 +47,308162792.6,315658180.0 +48,291282417.7,316581950.0 +49,326988587.6,322722780.0 +50,327864337.1,304423680.0 +51,308162792.6,311155600.0 +52,291282417.7,315857400.0 +53,326988587.6,318633470.0 +54,338531879.5,318105200.0 +55,308162792.6,332961340.0 +56,291282417.7,335313200.0 +57,326988587.6,345534460.0 +58,338531879.5,343698900.0 +59,343441750.8,337725980.0 +60,291282417.7,301866880.0 +61,326988587.6,310824100.0 +62,338531879.5,321738430.0 +63,343441750.8,311232700.0 +64,328373555.6,309342560.0 +65,326988587.6,306569470.0 +66,338531879.5,310643680.0 +67,343441750.8,314744000.0 +68,328373555.6,308885660.0 +69,307601302.1,306099400.0 +70,338531879.5,321590140.0 +71,343441750.8,318885440.0 +72,328373555.6,318865300.0 +73,307601302.1,314202980.0 +74,296591378.5,314701920.0 +75,343441750.8,322403870.0 +76,328373555.6,318382370.0 +77,307601302.1,315469380.0 +78,296591378.5,312369950.0 +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 +29,121261267.3,119103896.0 +30,126043317.8,112915770.0 +31,127571038.2,113519840.0 +32,126803223.9,115918376.0 +33,121261267.3,112486350.0 +34,124085800.1,111817100.0 +35,127571038.2,122143170.0 +36,126803223.9,119428750.0 +37,121261267.3,117216230.0 +38,124085800.1,115505320.0 +39,111506694.9,116549080.0 +40,126803223.9,123125660.0 +41,121261267.3,118972860.0 +42,124085800.1,113853950.0 +43,111506694.9,114263896.0 +44,106343825.4,117712260.0 +45,121261267.3,118652850.0 +46,124085800.1,116123000.0 +47,111506694.9,112103380.0 +48,106343825.4,114997496.0 +49,123690748.8,118147590.0 +50,124085800.1,113974550.0 +51,111506694.9,114976400.0 +52,106343825.4,114994730.0 +53,123690748.8,119030696.0 +54,126477907.1,118913830.0 +55,111506694.9,128059680.0 +56,106343825.4,128777160.0 +57,123690748.8,132264690.0 +58,126477907.1,134432380.0 +59,127784089.0,130678440.0 +60,106343825.4,117309140.0 +61,123690748.8,120235380.0 +62,126477907.1,124647930.0 +63,127784089.0,121395256.0 +64,121978961.0,120084936.0 +65,123690748.8,115211520.0 +66,126477907.1,116868504.0 +67,127784089.0,118030080.0 +68,121978961.0,115720190.0 +69,115593904.4,115036584.0 +70,126477907.1,122232480.0 +71,127784089.0,120755330.0 +72,121978961.0,119494640.0 +73,115593904.4,118518104.0 +74,108270758.4,118762740.0 +75,127784089.0,121758630.0 +76,121978961.0,119449144.0 +77,115593904.4,117008670.0 +78,108270758.4,116879120.0 +79,106730768.7,118586610.0 diff --git a/浙江行业电量/7月预测1.csv b/浙江行业电量/7月预测1.csv new file mode 100644 index 0000000..3e83662 --- /dev/null +++ b/浙江行业电量/7月预测1.csv @@ -0,0 +1,81 @@ +,real,pred +0,50.73,-3852886.8 +1,47.88,19293490.0 +2,48.31,-787532.5 +3,57.75,17889912.0 +4,49.95,14566443.0 +5,47.88,-2105174.2 +6,48.31,9359497.0 +7,57.75,-433206.5 +8,49.95,7017683.0 +9,39.61,7703169.0 +10,48.31,-808642.3 +11,57.75,4597720.0 +12,49.95,-360913.0 +13,39.61,3072979.0 +14,56.33,4244487.0 +15,57.75,-51642.7 +16,49.95,2367718.2 +17,39.61,-302210.2 +18,56.33,1611450.0 +19,52.92,2502897.2 +20,49.95,276224.3 +21,39.61,1291416.2 +22,56.33,-257380.97 +23,52.92,980514.25 +24,21.66,1559440.0 +25,39.61,357924.16 +26,56.33,741489.3 +27,52.92,-223402.89 +28,21.66,639626.94 +29,17.11,1005113.4 +30,56.33,332503.4 +31,52.92,443570.0 +32,21.66,-191401.42 +33,17.11,425019.1 +34,21.02,662274.2 +35,52.92,272184.7 +36,21.66,272402.28 +37,17.11,-157965.39 +38,21.02,281494.4 +39,21.65,444933.7 +40,21.66,216378.39 +41,17.11,174190.5 +42,21.02,-121474.26 +43,21.65,189702.75 +44,17.95,310332.28 +45,17.11,170874.25 +46,21.02,115950.27 +47,21.65,-86960.06 +48,17.95,130457.57 +49,32.64,225947.27 +50,21.02,136433.27 +51,21.65,80172.625 +52,17.95,-57612.453 +53,32.64,93036.65 +54,36.2,172486.55 +55,21.65,111274.96 +56,17.95,57095.465 +57,32.64,-35822.484 +58,36.2,68347.86 +59,36.29,137399.97 +60,17.95,91150.695 +61,32.64,40596.707 +62,36.2,-20513.984 +63,36.29,51582.69 +64,34.19,112940.664 +65,32.64,78077.64 +66,36.2,31135.309 +67,36.29,-9841.227 +68,34.19,40612.53 +69,14.42,97921.65 +70,36.2,70152.695 +71,36.29,25340.3 +72,34.19,-927.8079 +73,14.42,34880.83 +74,17.77,88737.21 +75,36.29,62387.992 +76,34.19,19532.104 +77,14.42,3516.0425 +78,17.77,29056.807 +79,126.08,80504.98 diff --git a/浙江行业电量/7月预测2.csv b/浙江行业电量/7月预测2.csv new file mode 100644 index 0000000..319047e --- /dev/null +++ b/浙江行业电量/7月预测2.csv @@ -0,0 +1,81 @@ +,real,pred +0,2704531.598,-2316342.0 +1,2847401.395,20676706.0 +2,2853452.254,621314.75 +3,1445328.766,19298740.0 +4,2859827.803,15913489.0 +5,2847401.395,160415.81 +6,2853452.254,11473635.0 +7,1445328.766,1780781.9 +8,2859827.803,9122751.0 +9,2596527.222,9748031.0 +10,2853452.254,1740487.1 +11,1445328.766,7060606.5 +12,2859827.803,2215774.0 +13,2596527.222,5502353.0 +14,2493576.394,6607667.0 +15,1445328.766,2590090.2 +16,2859827.803,4988868.5 +17,2596527.222,2414713.2 +18,2493576.394,4189292.0 +19,2590630.491,5007022.5 +20,2859827.803,2663833.5 +21,2596527.222,3738563.2 +22,2493576.394,2267029.0 +23,2590630.491,3384996.8 +24,2542066.764,3895295.0 +25,2596527.222,2791455.5 +26,2493576.394,3239150.8 +27,2590630.491,2324025.0 +28,2542066.764,3109880.2 +29,2348001.351,3393537.5 +30,2493576.394,2799275.0 +31,2590630.491,2971549.0 +32,2542066.764,2372881.5 +33,2348001.351,2928644.5 +34,2358799.425,3085286.2 +35,2590630.491,2734414.0 +36,2542066.764,2794325.2 +37,2348001.351,2387036.8 +38,2358799.425,2778122.0 +39,2280758.623,2866578.2 +40,2542066.764,2720565.0 +41,2348001.351,2730321.8 +42,2358799.425,2451633.2 +43,2280758.623,2726225.2 +44,2178461.623,2774300.0 +45,2348001.351,2671150.2 +46,2358799.425,2668631.5 +47,2280758.623,2480967.5 +48,2178461.623,2668544.2 +49,2333089.921,2699806.2 +50,2358799.425,2573983.0 +51,2280758.623,2581621.8 +52,2178461.623,2458319.0 +53,2333089.921,2591283.8 +54,2464330.824,2604670.0 +55,2280758.623,2550985.0 +56,2178461.623,2560103.5 +57,2333089.921,2483460.2 +58,2464330.824,2577773.5 +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 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) + + +