Added time as input feature

src/data/dataset.py

@@ -33,6 +33,14 @@ class NrvDataset(Dataset):
             dataframe["wind_forecast"].to_numpy(), dtype=torch.float32
         )
 
+        self.quarter = torch.tensor(
+            dataframe["quarter"].to_numpy(), dtype=torch.float32
+        )
+
+        self.day_of_week = torch.tensor(
+            dataframe["day_of_week"].to_numpy(), dtype=torch.float32
+        )
+
         self.sequence_length = sequence_length
         self.predict_sequence_length = predict_sequence_length
 
@@ -125,6 +133,20 @@ class NrvDataset(Dataset):
             ]
             features.append(wind_forecast.view(-1))
 
+        ### Time Features ###
+        time_feature = 0
+        if self.data_config.QUARTER:
+            time_feature += self.quarter[actual_idx].item()
+
+        if self.data_config.DAY_OF_WEEK:
+            d_w = self.day_of_week[actual_idx].item()
+            if self.data_config.QUARTER:
+                d_w *= 96
+            time_feature += d_w
+
+        if time_feature is not None:
+            features.append(torch.tensor([time_feature]))
+
         if not features:
             raise ValueError(
                 "No features are configured to be included in the dataset."
@@ -193,6 +215,20 @@ class NrvDataset(Dataset):
             ]
             features.append(wind_forecast.view(-1))
 
+        ### Time Features ###
+        time_feature = 0
+        if self.data_config.QUARTER:
+            time_feature += self.quarter[idx]
+
+        if self.data_config.DAY_OF_WEEK:
+            d_w = self.day_of_week[idx].item()
+            if self.data_config.QUARTER:
+                d_w *= 96
+            time_feature += d_w
+
+        if time_feature is not None:
+            features.append(torch.tensor([time_feature]))
+
         target = self.nrv[
             idx
             + self.sequence_length : idx

src/data/preprocessing.py

@@ -31,13 +31,14 @@ class DataConfig:
 
         ### TIME ###
         self.YEAR: bool = False
-        self.DAY: bool = False
+        self.DAY_OF_WEEK: bool = False
         self.QUARTER: bool = False
 
 
 class DataProcessor:
-    def __init__(self, data_config: DataConfig):
+    def __init__(self, data_config: DataConfig, path:str="./"):
         self.batch_size = 2048
+        self.path = path
 
         self.train_range = (
             -np.inf,
@@ -62,6 +63,13 @@ class DataProcessor:
             self.wind_forecast, on="datetime", how="left"
         )
 
+        self.all_features["quarter"] = (
+            self.all_features["datetime"].dt.hour * 4
+            + self.all_features["datetime"].dt.minute / 15
+        )
+
+        self.all_features["day_of_week"] = self.all_features["datetime"].dt.dayofweek
+
         self.output_size = 96
 
         self.data_config = data_config
@@ -103,7 +111,7 @@ class DataProcessor:
         )
 
     def get_nrv_history(self):
-        df = pd.read_csv(history_data_path, delimiter=";")
+        df = pd.read_csv(self.path + history_data_path, delimiter=";")
         df = df[["datetime", "netregulationvolume"]]
         df = df.rename(columns={"netregulationvolume": "nrv"})
         df["datetime"] = pd.to_datetime(df["datetime"])
@@ -114,7 +122,7 @@ class DataProcessor:
         return df
 
     def get_load_forecast(self):
-        df = pd.read_csv(forecast_data_path, delimiter=";")
+        df = pd.read_csv(self.path + forecast_data_path, delimiter=";")
         df = df.rename(
             columns={
                 "Day-ahead 6PM forecast": "load_forecast",
@@ -129,7 +137,7 @@ class DataProcessor:
         return df
 
     def get_pv_forecast(self):
-        df = pd.read_csv(pv_forecast_data_path, delimiter=";")
+        df = pd.read_csv(self.path + pv_forecast_data_path, delimiter=";")
 
         df = df.rename(
             columns={"dayahead11hforecast": "pv_forecast", "Datetime": "datetime"}
@@ -142,7 +150,7 @@ class DataProcessor:
         return df
 
     def get_wind_forecast(self):
-        df = pd.read_csv(wind_forecast_data_path, delimiter=";")
+        df = pd.read_csv(self.path + wind_forecast_data_path, delimiter=";")
 
         df = df.rename(
             columns={"dayaheadforecast": "wind_forecast", "datetime": "datetime"}
@@ -267,3 +275,14 @@ class DataProcessor:
         )
         input, _, _ = next(iter(data_loader))
         return input.shape[-1]
+
+    def get_time_feature_size(self):
+        time_feature_size = 1
+        if self.data_config.QUARTER:
+            time_feature_size *= 96
+        if self.data_config.DAY_OF_WEEK:
+            time_feature_size *= 7
+        
+        if time_feature_size == 1:
+            return 0
+        return time_feature_size