Add cooling demand

atlite/convert.py

@@ -401,6 +401,81 @@ def heat_demand(cutout, threshold=15.0, a=1.0, constant=0.0, hour_shift=0.0, **p
     )
 
 
+# cooling demand
+def convert_cooling_demand(ds, threshold, a, constant, hour_shift):
+    # Temperature is in Kelvin; take daily average
+    T = ds["temperature"]
+    T = T.assign_coords(
+        time=(T.coords["time"] + np.timedelta64(dt.timedelta(hours=hour_shift)))
+    )
+
+    T = T.resample(time="1D").mean(dim="time")
+    threshold += 273.15
+    cooling_demand = a * (T - threshold)
+
+    cooling_demand = cooling_demand.clip(min=0.0)
+
+    return (constant + cooling_demand).rename("cooling_demand")
+
+
+def cooling_demand(
+    cutout, threshold=23.0, a=1.0, constant=0.0, hour_shift=0.0, **params
+):
+    """
+    Convert outside temperature into daily cooling demand using the degree-day
+    approximation.
+
+    Since "daily average temperature" means different things in
+    different time zones and since xarray coordinates do not handle
+    time zones gracefully like pd.DateTimeIndex, you can provide an
+    hour_shift to redefine when the day starts.
+
+    E.g. for Moscow in summer, hour_shift = 3, for New York in summer,
+    hour_shift = -4
+
+    This time shift applies across the entire spatial scope of ds for
+    all times. More fine-grained control will be built in a some
+    point, i.e. space- and time-dependent time zones.
+
+    WARNING: Because the original data is provided every month, at the
+    month boundaries there is untidiness if you use a time shift. The
+    resulting xarray will have duplicates in the index for the parts
+    of the day in each month at the boundary. You will have to
+    re-average these based on the number of hours in each month for
+    the duplicated day.
+
+    Parameters
+    ----------
+    threshold : float
+        Outside temperature in degrees Celsius below which there is no
+        cooling demand. The default 23C is taken as a more liberal
+        estimation following European computational practices
+        (e.g. UK Met Office and European commission take as thresholds
+        22C and 24C, respectively)
+    a : float
+        Linear factor relating cooling demand to outside temperature.
+    constant : float
+        Constant part of cooling demand that does not depend on outside
+        temperature (e.g. due to ventilation).
+    hour_shift : float
+        Time shift relative to UTC for taking daily average
+
+    Note
+    ----
+    You can also specify all of the general conversion arguments
+    documented in the `convert_and_aggregate` function.
+
+    """
+    return cutout.convert_and_aggregate(
+        convert_func=convert_cooling_demand,
+        threshold=threshold,
+        a=a,
+        constant=constant,
+        hour_shift=hour_shift,
+        **params,
+    )
+
+
 # solar thermal collectors
 def convert_solar_thermal(
     ds, orientation, trigon_model, clearsky_model, c0, c1, t_store

atlite/cutout.py

@@ -32,6 +32,7 @@
 from atlite.convert import (
     coefficient_of_performance,
     convert_and_aggregate,
+    cooling_demand,
     csp,
     dewpoint_temperature,
     heat_demand,
@@ -644,6 +645,8 @@ def layout_from_capacity_list(self, data, col="Capacity"):
 
     convert_and_aggregate = convert_and_aggregate
 
+    cooling_demand = cooling_demand
+
     heat_demand = heat_demand
 
     temperature = temperature

doc/introduction.rst

@@ -19,7 +19,7 @@ relevant quantities, mainly you can create data for
 * **Solar (PV)** power generation: Using predefined or custom panel properties.
 * **Solar (thermal)** heat generation from solar collectors.
 * **Hydro (run-off)** power generation.
-* **Heating demand** (based on degree-day approx.).
+* **Heating and cooling demand** (based on degree-day approx.).
 
 How it works
 ========================

doc/release_notes.rst

@@ -11,3 +11,5 @@
 
 * The option ``capacity_factor_timeseries`` can be selected when creating capacity factors to obtain
  the capacity factor of the selected resource per grid cell.
+
+ * The methods ``convert_cooling_demand`` and ``cooling_demand`` are implemented to evaluate cooling demand using the cooling degree-days approximation.