diff --git a/pvnet/data/utils.py b/pvnet/data/utils.py
index e216a22d..0005a8ba 100644
--- a/pvnet/data/utils.py
+++ b/pvnet/data/utils.py
@@ -1,27 +1,33 @@
 """Utils common between Wind and PV datamodules"""
 import numpy as np
 import torch
-from ocf_datapipes.batch import unstack_np_batch_into_examples
-from ocf_datapipes.utils.consts import BatchKey
+from ocf_datapipes.batch import BatchKey, unstack_np_batch_into_examples
 from torch.utils.data import IterDataPipe, functional_datapipe
 
 
 def copy_batch_to_device(batch, device):
     """Moves a dict-batch of tensors to new device."""
     batch_copy = {}
-    for k in list(batch.keys()):
-        if isinstance(batch[k], torch.Tensor):
-            batch_copy[k] = batch[k].to(device)
+            
+    for k, v in batch.items():
+        if isinstance(v, dict):
+            # Recursion to reach the nested NWP
+            batch_copy[k] = copy_batch_to_device(v)
+        elif isinstance(v, torch.Tensor):
+            batch_copy[k] = v.to(device)
         else:
-            batch_copy[k] = batch[k]
+            batch_copy[k] = v
     return batch_copy
 
 
 def batch_to_tensor(batch):
     """Moves numpy batch to a tensor"""
-    for k in list(batch.keys()):
-        if isinstance(batch[k], np.ndarray) and np.issubdtype(batch[k].dtype, np.number):
-            batch[k] = torch.as_tensor(batch[k])
+    for k, v in batch.items():
+        if isinstance(v, dict):
+            # Recursion to reach the nested NWP
+            batch[k] = batch_to_tensor(v)
+        elif isinstance(v, np.ndarray) and np.issubdtype(v.dtype, np.number):
+            batch[k] = torch.as_tensor(v)
     return batch