diff --git a/examples/crunch_example.ipynb b/examples/crunch_example.ipynb
index 12f7372..d2567b9 100644
--- a/examples/crunch_example.ipynb
+++ b/examples/crunch_example.ipynb
@@ -54015,7 +54015,7 @@
     }
    ],
    "source": [
-    "# Probability weighted and unweighted AEP values are returned\n",
+    "# Probability weighted and unweighted AEP values are returned in kWh\n",
     "mycruncher_mc.set_probability_turbine_class('Wind1VelX', 2)\n",
     "mycruncher_mc.compute_aep('GenPwr')"
    ]
diff --git a/pCrunch/crunch.py b/pCrunch/crunch.py
index 1f5771d..75a8b13 100644
--- a/pCrunch/crunch.py
+++ b/pCrunch/crunch.py
@@ -448,7 +448,7 @@ def set_probability_turbine_class(self, windspeed, turbine_class, idx=None):
         
     def compute_aep(self, pwrchan, loss_factor=0.0, idx=None):
         """
-        Computes annual energy production based on all outputs in the list.
+        Computes annual energy production [kWh] based on all outputs in the list.
 
         Parameters
         ----------
@@ -463,10 +463,10 @@ def compute_aep(self, pwrchan, loss_factor=0.0, idx=None):
 
         Returns
         ----------
-        aep_weighted : float
+        aep_weighted_kWh : float
             Weighted AEP where each output is weighted by the probability of
             occurence determined from its average wind speed
-        aep_unweighted : float
+        aep_unweighted_kWh : float
             Unweighted AEP that assumes all outputs in the list are equally
             likely (just a mean)
         """
@@ -485,16 +485,16 @@ def compute_aep(self, pwrchan, loss_factor=0.0, idx=None):
         prob = prob / prob.sum()
 
         # Calculate scaling factor for year with losses included
-        fact = (1.0 - loss_factor) * 365.0 * 24.0 * 60.0 * 60.0
+        fact = (1.0 - loss_factor) * 365.0 * 24.0 #* 60.0 * 60.0 # Add these in for kW-s
 
         # Sum with probability.  Finding probability weighted average per second, scaled by total seconds
-        aep_weighted = fact * np.dot(E, prob) / np.dot(T, prob)
+        aep_weighted_kWh = fact * np.dot(E, prob) / np.dot(T, prob)
 
         # Assume equal probability
         prob = np.ones(E.shape)/E.size
-        aep_unweighted = fact * np.dot(E, prob) / np.dot(T, prob)
+        aep_unweighted_kWh = fact * np.dot(E, prob) / np.dot(T, prob)
 
-        return aep_weighted, aep_unweighted
+        return aep_weighted_kWh, aep_unweighted_kWh
 
     
     def compute_total_fatigue(self, lifetime=0.0, availability=1.0,
diff --git a/pCrunch/test/test_crunch.py b/pCrunch/test/test_crunch.py
index b84cc82..fe668be 100644
--- a/pCrunch/test/test_crunch.py
+++ b/pCrunch/test/test_crunch.py
@@ -290,7 +290,7 @@ def test_aep(self):
         myout = AeroelasticOutput(data)
         myouts = [myout]*10
 
-        aep_true = myout.compute_energy('WindVxi') * 8760*60*60/myout.elapsed_time
+        aep_true = myout.compute_energy('WindVxi') * 8760/myout.elapsed_time
         
         myobj = Crunch(myouts, magnitude_channels=mc)
         aep_w1, aep_uw1 = myobj.compute_aep('Wind')
diff --git a/pyproject.toml b/pyproject.toml
index 677b8a0..a3a6c48 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "pCrunch"
-version = "2.1.0"
+version = "2.1.1"
 description = "IO and Post Processing for generic time series data of multibody aeroelastic wind turbine simulations."
 readme = "README.rst"
 requires-python = ">=3.9"