diff --git a/pvnet/data/pv_site_datamodule.py b/pvnet/data/pv_site_datamodule.py
index 1b52fcf4..06101386 100644
--- a/pvnet/data/pv_site_datamodule.py
+++ b/pvnet/data/pv_site_datamodule.py
@@ -75,7 +75,6 @@ def __init__(
 
     def _get_datapipe(self, start_time, end_time):
         data_pipeline = pvnet_site_netcdf_datapipe(
-            self.configuration,
             keys=["pv", "nwp"],
         )
 
@@ -89,7 +88,6 @@ def _get_datapipe(self, start_time, end_time):
     def _get_premade_batches_datapipe(self, subdir, shuffle=False):
         filenames = list(glob.glob(f"{self.batch_dir}/{subdir}/*.nc"))
         data_pipeline = pvnet_site_netcdf_datapipe(
-            config_filename=self.configuration,
             keys=["pv", "nwp"],
             filenames=filenames,
         )
@@ -103,14 +101,14 @@ def _get_premade_batches_datapipe(self, subdir, shuffle=False):
                 data_pipeline.shuffle(buffer_size=100)
                 .sharding_filter()
                 # Split the batches and reshuffle them to be combined into new batches
-                .split_batches(splitting_key=BatchKey.sensor)
+                .split_batches(splitting_key=BatchKey.pv)
                 .shuffle(buffer_size=100 * self.batch_size)
             )
         else:
             data_pipeline = (
                 data_pipeline.sharding_filter()
                 # Split the batches so we can use any batch-size
-                .split_batches(splitting_key=BatchKey.sensor)
+                .split_batches(splitting_key=BatchKey.pv)
             )
 
         data_pipeline = (
diff --git a/pvnet/data/wind_datamodule.py b/pvnet/data/wind_datamodule.py
index 330271ef..ba804685 100644
--- a/pvnet/data/wind_datamodule.py
+++ b/pvnet/data/wind_datamodule.py
@@ -89,7 +89,6 @@ def _get_datapipe(self, start_time, end_time):
     def _get_premade_batches_datapipe(self, subdir, shuffle=False):
         filenames = list(glob.glob(f"{self.batch_dir}/{subdir}/*.nc"))
         data_pipeline = windnet_netcdf_datapipe(
-            config_filename=self.configuration,
             keys=["wind", "nwp"],
             filenames=filenames,
         )
@@ -103,14 +102,14 @@ def _get_premade_batches_datapipe(self, subdir, shuffle=False):
                 data_pipeline.shuffle(buffer_size=100)
                 .sharding_filter()
                 # Split the batches and reshuffle them to be combined into new batches
-                .split_batches(splitting_key=BatchKey.sensor)
+                .split_batches(splitting_key=BatchKey.wind)
                 .shuffle(buffer_size=100 * self.batch_size)
             )
         else:
             data_pipeline = (
                 data_pipeline.sharding_filter()
                 # Split the batches so we can use any batch-size
-                .split_batches(splitting_key=BatchKey.sensor)
+                .split_batches(splitting_key=BatchKey.wind)
             )
 
         data_pipeline = (
diff --git a/pvnet/models/base_model.py b/pvnet/models/base_model.py
index d0f462ce..ade7895b 100644
--- a/pvnet/models/base_model.py
+++ b/pvnet/models/base_model.py
@@ -27,6 +27,7 @@
     PredAccumulator,
     WeightedLosses,
 )
+
 from pvnet.optimizers import AbstractOptimizer
 from pvnet.utils import construct_ocf_ml_metrics_batch_df, plot_batch_forecasts
 
diff --git a/pvnet/models/multimodal/encoders/encoders3d.py b/pvnet/models/multimodal/encoders/encoders3d.py
index 8d093280..056d284b 100644
--- a/pvnet/models/multimodal/encoders/encoders3d.py
+++ b/pvnet/models/multimodal/encoders/encoders3d.py
@@ -37,8 +37,8 @@ def __init__(
         super().__init__(sequence_length, image_size_pixels, in_channels, out_features)
 
         # Check that the output shape of the convolutional layers will be at least 1x1
-        cnn_spatial_output_size = image_size_pixels - 12 * number_of_conv3d_layers
-        cnn_sequence_length = sequence_length - 6 * number_of_conv3d_layers
+        cnn_spatial_output_size = image_size_pixels - 2 * number_of_conv3d_layers
+        cnn_sequence_length = sequence_length #- 6 * number_of_conv3d_layers
         if not (cnn_spatial_output_size >= 1):
             raise ValueError(
                 f"cannot use this many conv3d layers ({number_of_conv3d_layers}) with this input "
@@ -51,8 +51,8 @@ def __init__(
             nn.Conv3d(
                 in_channels=in_channels,
                 out_channels=conv3d_channels,
-                kernel_size=(7, 13, 13),
-                padding=(0, 0, 0),
+                kernel_size=(3, 3, 3),
+                padding=(1, 0, 0),
             ),
             nn.ELU(),
         ]
@@ -61,8 +61,8 @@ def __init__(
                 nn.Conv3d(
                     in_channels=conv3d_channels,
                     out_channels=conv3d_channels,
-                    kernel_size=(7, 13, 13),
-                    padding=(0, 0, 0),
+                    kernel_size=(3, 3, 3),
+                    padding=(1, 0, 0),
                 ),
                 nn.ELU(),
             ]
diff --git a/pvnet/models/multimodal/multimodal.py b/pvnet/models/multimodal/multimodal.py
index f482c21e..a58582c0 100644
--- a/pvnet/models/multimodal/multimodal.py
+++ b/pvnet/models/multimodal/multimodal.py
@@ -297,7 +297,7 @@ def forward(self, x):
 
         if self.include_sun:
             sun = torch.cat(
-                (x[BatchKey.pv_solar_azimuth], x[BatchKey.pv_solar_elevation]), dim=1
+                (x[BatchKey.gsp_solar_azimuth], x[BatchKey.gsp_solar_elevation]), dim=1
             ).float()
             sun = self.sun_fc1(sun)
             modes["sun"] = sun