diff --git a/pvnet/models/base_model.py b/pvnet/models/base_model.py
index 46706c00..b4d42f22 100644
--- a/pvnet/models/base_model.py
+++ b/pvnet/models/base_model.py
@@ -245,7 +245,7 @@ def save_pretrained(
 
 
 class BaseModel(pl.LightningModule, PVNetModelHubMixin):
-    """Abtstract base class for PVNet submodels"""
+    """Abstract base class for PVNet submodels"""
 
     def __init__(
         self,
@@ -257,6 +257,7 @@ def __init__(
         interval_minutes: int = 30,
         timestep_intervals_to_plot: Optional[list[int]] = None,
         use_weighted_loss: bool = False,
+        forecast_minutes_ignore: Optional[int] = 0,
     ):
         """Abtstract base class for PVNet submodels.
 
@@ -270,6 +271,8 @@ def __init__(
             interval_minutes: The interval in minutes between each timestep in the data
             timestep_intervals_to_plot: Intervals, in timesteps, to plot during training
             use_weighted_loss: Whether to use a weighted loss function
+            forecast_minutes_ignore: Number of forecast minutes to ignore when calculating losses.
+                For example if set to 60, the model doesnt predict the first 60 minutes
         """
         super().__init__()
 
@@ -292,10 +295,11 @@ def __init__(
         self.forecast_minutes = forecast_minutes
         self.output_quantiles = output_quantiles
         self.interval_minutes = interval_minutes
+        self.forecast_minutes_ignore = forecast_minutes_ignore
 
         # Number of timestemps for 30 minutely data
         self.history_len = history_minutes // interval_minutes
-        self.forecast_len = forecast_minutes // interval_minutes
+        self.forecast_len = (forecast_minutes - forecast_minutes_ignore) // interval_minutes
 
         self.weighted_losses = WeightedLosses(forecast_length=self.forecast_len)
 
@@ -334,7 +338,7 @@ def _quantiles_to_prediction(self, y_quantiles):
         y_median = y_quantiles[..., idx]
         return y_median
 
-    def _calculate_qauntile_loss(self, y_quantiles, y):
+    def _calculate_quantile_loss(self, y_quantiles, y):
         """Calculate quantile loss.
 
         Note:
diff --git a/tests/conftest.py b/tests/conftest.py
index 4ea9e3f1..990f8e3c 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -303,3 +303,12 @@ def multimodal_weighted_quantile_model(multimodal_model_kwargs):
         output_quantiles=[0.1, 0.5, 0.9], **multimodal_model_kwargs, use_weighted_loss=True
     )
     return model
+
+
+@pytest.fixture()
+def multimodal_quantile_model_ignore_minutes(multimodal_model_kwargs):
+    """ Only forecsat second half of the 8 hours"""
+    model = Model(
+        output_quantiles=[0.1, 0.5, 0.9], **multimodal_model_kwargs, ignore_minutes=240
+    )
+    return model
diff --git a/tests/models/multimodal/test_multimodal.py b/tests/models/multimodal/test_multimodal.py
index 0cc9a7df..c112e6a3 100644
--- a/tests/models/multimodal/test_multimodal.py
+++ b/tests/models/multimodal/test_multimodal.py
@@ -51,3 +51,15 @@ def test_weighted_quantile_model_backward(multimodal_weighted_quantile_model, sa
 
     # Backwards on sum drives sum to zero
     y_quantiles.sum().backward()
+
+
+def test_weighted_quantile_model_forward(multimodal_quantile_model_ignore_minutes, sample_batch):
+    y_quantiles = multimodal_quantile_model_ignore_minutes(sample_batch)
+
+    # check output is the correct shape
+    # batch size=2, forecast_len=8, num_quantiles=3
+    assert tuple(y_quantiles.shape) == (2, 8, 3), y_quantiles.shape
+
+    # Backwards on sum drives sum to zero
+    y_quantiles.sum().backward()
+