Include adjustable interval minutes

pvnet/models/base_model.py

@@ -240,6 +240,7 @@ def __init__(
         optimizer: AbstractOptimizer,
         output_quantiles: Optional[list[float]] = None,
         target_key: str = "gsp",
+        interval_minutes: int = 30,
     ):
         """Abtstract base class for PVNet submodels.
 
@@ -266,17 +267,16 @@ def __init__(
         self.output_quantiles = output_quantiles
 
         # Number of timestemps for 30 minutely data
-        # TODO Change, but make configurable, as India data is 15 minutely
-        self.history_len_30 = history_minutes // 15
-        self.forecast_len_30 = forecast_minutes // 15
+        self.history_len_30 = history_minutes // interval_minutes
+        self.forecast_len_30 = forecast_minutes // interval_minutes
         # self.forecast_len_15 = forecast_minutes // 15
         # self.history_len_15 = history_minutes // 15
 
         self.weighted_losses = WeightedLosses(forecast_length=self.forecast_len_30)
 
         self._accumulated_metrics = MetricAccumulator()
         self._accumulated_batches = BatchAccumulator(
-            key_to_keep=self._target_key
+            key_to_keep=self._target_key_name
         )
         self._accumulated_y_hat = PredAccumulator()
 

pvnet/models/multimodal/multimodal.py

@@ -58,6 +58,7 @@ def __init__(
         wind_history_minutes: Optional[int] = None,
         optimizer: AbstractOptimizer = pvnet.optimizers.Adam(),
         target_key: str = "gsp",
+            interval_minutes: int = 30,
     ):
         """Neural network which combines information from different sources.
 
@@ -99,6 +100,7 @@ def __init__(
                 `history_minutes` if not provided.
             optimizer: Optimizer factory function used for network.
             target_key: The key of the target variable in the batch.
+            interval_minutes: The interval between each sample of the target data
         """
 
         self.include_gsp_yield_history = include_gsp_yield_history
@@ -111,13 +113,15 @@ def __init__(
         self.embedding_dim = embedding_dim
         self.add_image_embedding_channel = add_image_embedding_channel
         self.target_key_name = target_key
+        self.interval_minutes = interval_minutes
 
         super().__init__(
             history_minutes=history_minutes,
             forecast_minutes=forecast_minutes,
             optimizer=optimizer,
             output_quantiles=output_quantiles,
-            target_key=BatchKey.gsp if target_key == "gsp" else BatchKey.wind,
+            target_key=target_key,
+            interval_minutes=interval_minutes
         )
 
         # Number of features expected by the output_network
@@ -278,8 +282,12 @@ def forward(self, x):
         # *********************** Sensor Data ************************************
         # add sensor yield history
         if self.include_wind:
-            # sensor_history = x[BatchKey.sensor][:, : self.history_len_30].float()
-            modes["wind"] = self.wind_encoder(x)
+            if self.target_key_name != "wind":
+                modes["wind"] = self.wind_encoder(x)
+            else:
+                # Target is wind, so only take the history
+                wind_history = x[BatchKey.wind][:, : self.history_len_30].float()
+                modes["wind"] = self.wind_encoder(wind_history)
 
         if self.include_sun:
             sun = torch.cat(