update code

.gradio/flagged/ Temperature File MaxTemp CSV/f29000ce64ba7a9a36ca/MAX_TEMP_BY_DAY_202507171739.csv

@@ -0,0 +1,504 @@
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.gradio/flagged/dataset2.csv

@@ -0,0 +1,2 @@
+📥 Load File (MW) CSV,🌡️ Temperature File (MaxTemp) CSV,Forecast Model,Regression Model,Forecast Horizon (days),ΔTemp (°C),📈 Forecast Load MW,📊 Simulated Load Limit,timestamp
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-369391752.14857125], [16.4474, 61.77, 17.4474, -84893039.74674386], [16.2663, 55.98, 17.2663, -383528764.1778529], [15.5243, 54.42, 16.5243, -372863781.7963062], [16.1223, 60.33, 17.1223, -647909248.8809935], [16.4763, 61.81, 17.4763, -37861353.30949956], [16.1751, 59.6, 17.1751, -550969737.8231794], [16.2393, 59.98, 17.2393, -433100105.05974746], [16.109, 60.07, 17.109, -672327724.2042626], [16.5281, 59.44, 17.5281, 46437655.5988724], [15.977, 53.22, 16.977, -805829171.9830074], [15.925, 58.42, 16.925, -756096014.7271926], [16.3557, 59.3, 17.3557, -234125069.03043592], [16.609, 59.38, 17.609, -6162497.291859817], [17.3577, 65.7, 18.3577, -541110353.4616629], [16.4632, 61.31, 17.4632, -59180214.63568101], [16.029, 57.22, 17.029, -819205771.261583], [15.5495, 52.56, 16.549500000000002, -396965234.92796594], [15.9846, 54.28, 16.9846, -813097864.1973195], [15.5799, 57.91, 16.579900000000002, -426040003.7852142], [15.6009, 57.21, 16.6009, -446124548.0616077], [15.0935, 53.67, 16.0935, -974149174.0858811], [15.6705, 55.37, 16.6705, -512690466.23475456], [15.7441, 52.59, 16.7441, -583082011.8891828], [15.2331, 53.94, 16.2331, -596947002.7397844], [15.305, 57.15, 16.305, -402671672.36878145], [16.1735, 56.6, 17.1735, -553907298.7643154], [15.8891, 56.91, 16.8891, -721761008.0832694], [15.822, 59.34, 16.822, -657586107.0858462], [16.67, 64.25, 17.67, -49747136.301314786], [16.2097, 55.45, 17.2097, -487444982.470899], [16.3083, 60.92, 17.3083, -311263544.3635541], [16.705, 61.74, 17.705, -74754716.06080176], [16.1248, 61.3, 17.1248, -643319309.9104525], [15.6427, 59.98, 16.642699999999998, -486102355.2403084], [15.3452, 61.23, 16.3452, -294050416.72332233], [15.6615, 59.22, 16.6615, -504082804.4020569], [15.7284, 58.92, 16.7284, -568066424.0254015], [16.8565, 59.94, 17.8565, -183001811.3055339], [16.2367, 64.15, 17.2367, -437873641.5890748], [16.1303, 63.75, 17.1303, -633221444.175306], [16.5334, 64.2, 17.5334, 47853874.98860579], [16.5644, 65.33, 17.5644, 25704304.344497908], [16.3955, 62.66, 17.3955, -169354788.05456284], [16.7075, 64.68, 17.7075, -76540971.75791076], [16.4859, 62.61, 17.4859, -22238370.963541094], [16.6285, 61.66, 17.6285, -20095291.729355354], [16.9642, 60.53, 17.9642, -259953706.73690596], [16.3735, 60.39, 17.3735, -205157455.93069878], [16.5567, 65.36, 17.5567, 31205971.89156457], [16.9783, 64.66, 17.9783, -270028188.86860263], [16.7448, 64.71, 17.7448, -103191906.75874364], [17.0865, 64.04, 18.0865, -347337335.4394449], [17.609, 64.84, 18.609, -748435864.51465], [17.0086, 63.49, 18.0086, -291677607.91753966], [17.5353, 64.45, 18.5353, -668005958.1842133], [17.835, 62.79, 18.835, -994766146.0341829], [17.7809, 55.33, 18.7809, -936118773.7252924], [17.1502, 47.996511156186614, 18.1502, -392851130.6017173], [16.7849, 45.266450304259635, 17.7849, -131843448.14036141], [17.1615, 42.536389452332656, 18.1615, -400925006.3526603], [17.0034, 39.80632860040568, 18.0034, -287962196.0675664], [15.9826, 52.08, 16.982599999999998, -811185050.4567062], [17.0907, 59.71, 18.0907, -350338245.01055586], [16.8425, 60.47, 17.8425, -172998779.40171096], [16.9218, 59.413834586466166, 17.9218, -229658810.1140048], [17.1861, 60.68421052631579, 18.1861, -418501762.4122241], [17.5831, 63.8, 18.5831, -716920142.9962356], [17.7993, 63.07, 18.7993, -956065384.6399529], [17.0974, 64.73, 18.0974, -355125410.27886957], [17.3266, 64.89, 18.3266, -518889332.5896423], [18.5129, 64.42, 19.5129, -1916738805.7380168], [18.0217, 65.83, 19.0217, -1212492094.727996], [18.1277, 63.18, 19.1277, -1364467158.5859852], [17.503, 60.68, 18.503, -644927534.5775515], [17.7772, 64.4, 18.7772, -932107770.4435427], [18.3153, 66.36, 19.3153, -1633434347.074364], [18.1879, 64.38, 19.1879, -1450777525.0412865], [18.1087, 64.68, 19.1087, -1337226345.2529924], [18.1211, 68.61, 19.1211, -1355004560.2703075], [18.0639, 64.05, 19.0639, -1272995374.8677433], [18.6859, 64.74, 19.6859, -2164773579.7705398], [18.5845, 63.92, 19.5845, -2019393660.1931093], [18.5279, 62.61, 19.5279, -1938244711.0010142], [18.6516, 61.27, 19.6516, -2115596743.0692525], [18.416, 61.6, 19.416, -1777810657.7395046], [18.4897, 64.26, 19.4897, -1883476338.931388], [18.7387, 64.71, 19.7387, -2240474366.2960377], [18.2419, 64.35, 19.2419, -1528198783.9878092], [18.4934, 60.47009404388715, 19.4934, -1888781128.8962426], [18.6165, 60.009655172413794, 19.6165, -2065272924.7540429], [18.818, 59.549216300940444, 19.818, -2354168918.7860193], [19.0032, 59.088777429467086, 20.0032, -2619695162.4323964], [18.6945, 58.62833855799373, 19.6945, -2177103632.12125], [19.7996, 58.16789968652038, 20.7996, -3978618111.300639], [19.558, 66.29, 20.558, -3415126911.757276], [19.1364, 57.97, 20.1364, -2810667601.1671896], [18.6441, 56.14, 19.6441, -2104843790.437832], [18.4268, 58.68, 19.4268, -1793294909.5288653], [18.4142, 61.89, 19.4142, -1775229949.1079874], [18.6251, 62.38, 19.6251, -2077602977.1048002], [18.8096, 65.1, 19.8096, -2342125611.838778], [19.2329, 64.21, 20.2329, -2949022258.358686], [19.2395, 64.59, 20.2395, -2958484856.6744184], [20.0564, 50.32, 21.0564, -4631142125.391122], [19.9649, 51.67156133828996, 20.9649, -4398642330.650948], [20.0023, 53.48568773234201, 21.0023, -4493675033.637625], [20.108, 55.299814126394054, 21.108, -4762256763.736445], [19.9217, 57.1139405204461, 20.9217, -4288871935.7572136], [20.3082, 58.928066914498146, 21.3082, -4837200311.153], [20.2786, 60.742193308550185, 21.2786, -4867897626.149662], [20.3467, 62.55631970260223, 21.3467, -4797273060.228306], [20.4018, 64.64, 21.4018, -4740130423.190658], [20.289, 64.01, 21.289, -4857112083.042697], [21.1202, 63.6, 22.1202, -3965273803.818503], [20.5744, 64.66, 21.5744, -4561131890.473658], [20.9102, 64.09, 21.9102, -4212883296.694049], [20.8714, 65.36, 21.8714, -4253121669.0545216], [21.0777, 64.14, 22.0777, -4016494462.652691], [20.8257, 65.74, 21.8257, -4300515834.437887], [21.0135, 65.41, 22.0135, -4093867787.291575], [20.9057, 64.43, 21.9057, -4217550118.2307024], [20.6882, 63.1, 21.6882, -4443113159.168928], [21.0894, 64.85, 22.0894, -4002393716.573676], [21.4871, 64.14, 22.4871, -3593752505.2257805], [21.0005, 65.12, 22.0005, -4109535282.934999], [21.5205, 65.34, 22.5205, -3567541082.027603], [21.1192, 65.01, 22.1192, -3966478995.7910523], [21.2637, 60.8, 22.2637, -3792328755.7548366], [20.9549, 63.71, 21.9549, -4164492036.8841166], [21.3309, 66.51, 22.3309, -3716334071.2006006], [21.1187, 61.19, 22.1187, -3967081591.7773585], [20.8863, 61.027760416666666, 21.8863, -4237669304.4109583], [21.1084, 64.18, 22.1084, -3979495069.0948224], [21.0208, 64.93, 22.0208, -4085069885.891853], [20.8259, 62.89, 21.8259, -4300308420.147337], [21.2001, 65.01, 22.2001, -3868978965.2101955], [20.8005, 58.058749999999996, 21.8005, -4326650035.043133], [21.3461, 63.08, 22.3461, -3704405519.325979], [20.8995, 59.60673913043478, 21.8995, -4223979961.2367773], [20.407, 56.7, 21.407, -4734737651.637172], [20.7056, 54.03822784810127, 21.7056, -4425068115.89392], [20.2595, 53.63, 21.2595, -4887705690.8941]], ""metadata"": null}",2025-07-18 10:24:00.851641

app.py

@@ -8,7 +8,7 @@ import os
 import numpy as np
 
 from utils.preprocessing import preprocess_load_data, preprocess_temperature_data
-from utils.merge_data import merge_load_temp
+from utils.merge_data import aggregate_daily_max, merge_load_temp
 from models.forecast import TimeSeriesForecaster
 from models.regression import LoadLimitRegressor
 from utils.simulate import simulate_with_model
@@ -37,9 +37,13 @@ def forecast_ui(load_file, temp_file,
     # 2. preprocess
     df_load = preprocess_load_data(df_load_raw)
     df_temp = preprocess_temperature_data(df_temp_raw)
+    
+    print("df_load:", df_load)
+    print("df_temp:", df_temp)
 
-    # 3. merge
-    df_merged = merge_load_temp(df_load, df_temp)
+    # 3. aggregate และ merge
+    df_daily = aggregate_daily_max(df_load)              # ✅ ต้องสรุป daily ก่อน
+    df_merged = merge_load_temp(df_daily, df_temp)       # ✅ ถึงจะ merge ได้
 
     # 4. สร้างและ train forecasting model (forecast max load)
     forecast_model = TimeSeriesForecaster(model_type=forecast_model_type, horizon=forecast_horizon)

models/forecast.py

@@ -33,7 +33,8 @@ class TimeSeriesForecaster:
         """
         if self.model_type in ['PatchTST', 'LSTM', 'BiLSTM', 'GRU', 'RNN']:
             X, y = self.preprocess(df, value_col)
-            self.dls = get_ts_dls(X, y, bs=32, splits=TSStandardSplit()(X))
+            splits = get_splits(y, valid_size=0.2, shuffle=False)
+            self.dls = get_ts_dls(X, y, bs=32, splits=splits)
             if self.model_type == 'PatchTST':
                 self.model = PatchTST(self.dls.vars, self.dls.len, self.dls.c)
             elif self.model_type == 'LSTM':
@@ -66,7 +67,8 @@ class TimeSeriesForecaster:
             future_steps = self.horizon
 
         if self.model_type in ['PatchTST', 'LSTM', 'BiLSTM', 'GRU', 'RNN']:
-            x_last = self.dls.items[-1][None, :]
+            device = next(self.learn.model.parameters()).device  # 👈 เอา device ที่โมเดลอยู่
+            x_last = torch.tensor(self.dls.items[-1][None, :]).to(device)  # 👈 ย้าย input ไป device เดียวกัน
             pred = self.learn.model(x_last).detach().cpu().numpy().flatten()
             pred = self.scaler.inverse_transform(pred.reshape(-1, 1)).flatten()
             return pred[:future_steps]

utils/merge_data.py

@@ -1,31 +1,44 @@
-# utils/merge_data.py
 import pandas as pd
 
 def aggregate_daily_max(df_load_15min: pd.DataFrame) -> pd.DataFrame:
     """
-    จากข้อมูลที่ resample มาแล้ว (ทุก 15 นาที) รวมให้เหลือแค่ daily max load
+    จากข้อมูลโหลด 15 นาที หาค่าสูงสุดต่อวัน และค่าเฉลี่ย
     """
     df = df_load_15min.copy()
-    df['date'] = df['datetime'].dt.date  # เป็น datetime.date ไม่เอาเวลา
+
+    # datetime ต้องเป็น datetime64 ก่อน
+    if not pd.api.types.is_datetime64_any_dtype(df['datetime']):
+        df['datetime'] = pd.to_datetime(df['datetime'])
+
+    df['date'] = df['datetime'].dt.date  # datetime.date type
     df_daily = df.groupby('date')['mw'].agg(['max', 'mean']).reset_index()
     df_daily = df_daily.rename(columns={'max': 'mw_max', 'mean': 'mw_mean'})
     return df_daily
 
+
 def merge_load_temp(df_daily_load: pd.DataFrame, df_temp: pd.DataFrame) -> pd.DataFrame:
     """
-    รวมข้อมูล Load (daily) กับ Temperature (daily) โดยใช้ date เป็น key
-    df_temp ต้องมีคอลัมน์ 'date' ที่เป็น datetime64[ns] หรือ datetime.date
+    รวม daily load กับอุณหภูมิ โดยใช้ 'date' เป็น key
     """
     df_temp = df_temp.copy()
-    # ถ้าไม่มี 'date' คอลัมน์ ให้สร้างจาก year,month,day
+
+    # สร้าง date ถ้ายังไม่มี
     if 'date' not in df_temp.columns:
-        df_temp['date'] = pd.to_datetime(df_temp[['year', 'month', 'day']]).dt.date
+        if {'year', 'month', 'day'}.issubset(df_temp.columns):
+            df_temp['date'] = pd.to_datetime(df_temp[['year', 'month', 'day']]).dt.date
+        else:
+            raise ValueError("❌ df_temp ไม่มีคอลัมน์ 'date' หรือ [year, month, day]")
 
-    # มึงอย่าไปแปลงเป็น datetime แล้วเอาเวลาออกอีกที ให้มันมี datatype เดียวกันระหว่าง 2 df นี่แหละดีที่สุด
+    # ตรวจสอบชนิดข้อมูล date ให้เหมือนกันทั้งสองฝั่ง
+    if df_daily_load['date'].dtype != df_temp['date'].dtype:
+        df_temp['date'] = pd.to_datetime(df_temp['date']).dt.date
+        df_daily_load['date'] = pd.to_datetime(df_daily_load['date']).dt.date
 
+    # Merge
     df_merged = pd.merge(df_daily_load, df_temp[['date', 'MaxTemp']], on='date', how='inner')
     return df_merged
 
+
 # ตัวอย่างใช้
 if __name__ == '__main__':
     load_fp = 'load_15min.csv'

utils/preprocessing.py

@@ -3,34 +3,37 @@ import pandas as pd
 
 def preprocess_load_data(df_load: pd.DataFrame) -> pd.DataFrame:
     """
-    รับ DataFrame load ดิบ แปลง datetime, sort, resample 15 นาที เติม missing
+    รับ DataFrame load ดิบ แปลง datetime จาก 'd/m/yyyy', sort, resample 15 นาที เติม missing
     """
     df_load = df_load.copy()
 
-    # ลบ space รอบคอลัมน์เผื่อมีมั่ว
+    # ล้างชื่อคอลัมน์
     df_load.columns = df_load.columns.str.strip()
 
-    # รวม date + time เป็น datetime พร้อม dayfirst=True (วัน/เดือน/ปี)
-    df_load['datetime'] = pd.to_datetime(df_load['date'].astype(str) + ' ' + df_load['time'].astype(str), dayfirst=True)
+    # รวม date + time เป็น datetime — ฟอร์แมต d/m/yyyy รองรับด้วย dayfirst=True
+    datetime_str = df_load['date'].astype(str) + ' ' + df_load['time'].astype(str)
+    df_load['datetime'] = pd.to_datetime(datetime_str, dayfirst=True, errors='coerce')
 
-    # Drop คอลัมน์เดิม
+    # เช็คพวก parse ไม่ได้
+    if df_load['datetime'].isna().any():
+        raise ValueError("❌ บางแถวแปลง datetime ไม่ได้ — ตรวจ format 'date' กับ 'time' ให้ชัวร์ว่าเป็น d/m/yyyy!")
+
+    # ทิ้ง date, time เดิม
     df_load = df_load.drop(columns=['date', 'time'])
 
-    # Sort และตั้ง index เป็น datetime
+    # Sort & set index
     df_load = df_load.sort_values('datetime').set_index('datetime')
 
-    # สร้าง full range datetime ทุก 15 นาที ตั้งแต่วันแรกถึงวันสุดท้าย
-    start = df_load.index.min().normalize()
-    end = df_load.index.max().normalize() + pd.Timedelta(days=1) - pd.Timedelta(minutes=15)
+    # เตรียมช่วงเวลา 15 นาที
+    start = df_load.index.min().floor('D')
+    end = df_load.index.max().ceil('D')
     full_idx = pd.date_range(start=start, end=end, freq='15T')
 
-    # Reindex เติมช่วงเวลาทั้งหมด
+    # Reindex + fill
     df_load = df_load.reindex(full_idx)
-
-    # เติม missing ด้วย linear interpolation แล้ว forward fill / back fill อีกที
     df_load['mw'] = df_load['mw'].interpolate(method='time').fillna(method='ffill').fillna(method='bfill')
 
-    # Reset index กลับเป็นคอลัมน์ datetime
+    # Reset index
     df_load = df_load.reset_index().rename(columns={'index': 'datetime'})
 
     return df_load