diff --git a/ocf_data_sampler/numpy_batch/__init__.py b/ocf_data_sampler/numpy_batch/__init__.py
index b9c778b..9b28009 100644
--- a/ocf_data_sampler/numpy_batch/__init__.py
+++ b/ocf_data_sampler/numpy_batch/__init__.py
@@ -1,5 +1,6 @@
 """Conversion from Xarray to NumpyBatch"""
 
+from .datetime_features import make_datetime_numpy_batch
 from .gsp import convert_gsp_to_numpy_batch, GSPBatchKey
 from .nwp import convert_nwp_to_numpy_batch, NWPBatchKey
 from .satellite import convert_satellite_to_numpy_batch, SatelliteBatchKey
diff --git a/ocf_data_sampler/numpy_batch/datetime_features.py b/ocf_data_sampler/numpy_batch/datetime_features.py
new file mode 100644
index 0000000..0472e01
--- /dev/null
+++ b/ocf_data_sampler/numpy_batch/datetime_features.py
@@ -0,0 +1,42 @@
+"""Datapipes to trigonometric date and time to NumpyBatch"""
+
+import numpy as np
+import pandas as pd
+from numpy.typing import NDArray
+
+
+def _get_date_time_in_pi(
+    dt: pd.DatetimeIndex,
+) -> tuple[NDArray[np.float64], NDArray[np.float64]]:
+    """
+    Change the datetimes, into time and date scaled in radians
+    """
+
+    day_of_year = dt.dayofyear
+    minute_of_day = dt.minute + dt.hour * 60
+
+    # converting into positions on sin-cos circle
+    time_in_pi = (2 * np.pi) * (minute_of_day / (24 * 60))
+    date_in_pi = (2 * np.pi) * (day_of_year / 365)
+
+    return date_in_pi, time_in_pi
+
+
+def make_datetime_numpy_batch(datetimes: pd.DatetimeIndex, key_prefix: str = "wind") -> dict:
+    """ Make dictionary of datetime features"""
+    time_numpy_batch = {}
+
+    date_in_pi, time_in_pi = _get_date_time_in_pi(datetimes)
+
+    # Store
+    date_sin_batch_key = key_prefix + "_date_sin"
+    date_cos_batch_key = key_prefix + "_date_cos"
+    time_sin_batch_key = key_prefix + "_time_sin"
+    time_cos_batch_key = key_prefix + "_time_cos"
+
+    time_numpy_batch[date_sin_batch_key] = np.sin(date_in_pi)
+    time_numpy_batch[date_cos_batch_key] = np.cos(date_in_pi)
+    time_numpy_batch[time_sin_batch_key] = np.sin(time_in_pi)
+    time_numpy_batch[time_cos_batch_key] = np.cos(time_in_pi)
+
+    return time_numpy_batch
diff --git a/ocf_data_sampler/torch_datasets/process_and_combine.py b/ocf_data_sampler/torch_datasets/process_and_combine.py
index cae1e5f..13f1826 100644
--- a/ocf_data_sampler/torch_datasets/process_and_combine.py
+++ b/ocf_data_sampler/torch_datasets/process_and_combine.py
@@ -9,6 +9,7 @@
     convert_nwp_to_numpy_batch,
     convert_satellite_to_numpy_batch,
     convert_gsp_to_numpy_batch,
+    make_datetime_numpy_batch,
     make_sun_position_numpy_batch,
 )
 from ocf_data_sampler.numpy_batch.gsp import GSPBatchKey
diff --git a/tests/numpy_batch/test_datetime_features.py b/tests/numpy_batch/test_datetime_features.py
new file mode 100644
index 0000000..d60697f
--- /dev/null
+++ b/tests/numpy_batch/test_datetime_features.py
@@ -0,0 +1,26 @@
+import numpy as np
+import pandas as pd
+
+from ocf_data_sampler.numpy_batch.datetime_features import make_datetime_numpy_batch
+
+from ocf_data_sampler.numpy_batch import GSPBatchKey
+
+
+def test_calculate_azimuth_and_elevation():
+
+    # Pick the day of the summer solstice
+    datetimes = pd.to_datetime(["2024-06-20 12:00", "2024-06-20 12:30", "2024-06-20 13:00"])
+
+    # Calculate sun angles
+    datetime_features = make_datetime_numpy_batch(datetimes)
+
+    assert len(datetime_features) == 4
+
+    assert len(datetime_features["wind_date_sin"]) == len(datetimes)
+    assert (datetime_features["wind_date_cos"] != datetime_features["wind_date_sin"]).all()
+
+    # assert all values are between -1 and 1
+    assert all(np.abs(datetime_features["wind_date_sin"]) <= 1)
+    assert all(np.abs(datetime_features["wind_date_cos"]) <= 1)
+    assert all(np.abs(datetime_features["wind_time_sin"]) <= 1)
+    assert all(np.abs(datetime_features["wind_time_cos"]) <= 1)