diff --git a/peak_load_class.py b/peak_load_class.py
new file mode 100644
index 0000000..f1f8017
--- /dev/null
+++ b/peak_load_class.py
@@ -0,0 +1,44 @@
+from pathlib import Path
+import pandas as pd
+import hub.helpers.constants as cte
+
+def Peak_load (building):
+  array = [None] * 12
+  heating = 0
+  cooling = 0
+  for system in building.energy_systems:
+    for demand_type in system.demand_types:
+      if demand_type == cte.HEATING:
+        heating = 1
+      if demand_type == cte.COOLING:
+        cooling = 1
+  if cte.MONTH in building.heating_peak_load.keys() and cte.MONTH in building.cooling_peak_load.keys():
+    peak_lighting = 0
+    peak_appliances = 0
+    for thermal_zone in building.internal_zones[0].thermal_zones:
+      lighting = thermal_zone.lighting
+      for schedule in lighting.schedules:
+        for value in schedule.values:
+          if value * lighting.density * thermal_zone.total_floor_area > peak_lighting:
+            peak_lighting = value * lighting.density * thermal_zone.total_floor_area
+      appliances = thermal_zone.appliances
+      for schedule in appliances.schedules:
+        for value in schedule.values:
+          if value * appliances.density * thermal_zone.total_floor_area > peak_appliances:
+            peak_appliances = value * appliances.density * thermal_zone.total_floor_area
+
+    monthly_electricity_peak = [0.9 * peak_lighting + 0.7 * peak_appliances] * 12
+    conditioning_peak = []
+    for i, value in enumerate(building.heating_peak_load[cte.MONTH]):
+      if cooling * building.cooling_peak_load[cte.MONTH][i] > heating * value:
+        conditioning_peak.append(cooling * building.cooling_peak_load[cte.MONTH][i])
+      else:
+        conditioning_peak.append(heating * value)
+      monthly_electricity_peak[i] += 0.8 * conditioning_peak[i]
+
+    electricity_peak_load_results = pd.DataFrame(monthly_electricity_peak
+                                                 , columns=[f'{building.name} electricity peak load W'])
+  else:
+    electricity_peak_load_results = pd.DataFrame(array, columns=[f'{building.name} electricity peak load W'])
+
+    return electricity_peak_load_results
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