diff --git a/bpeng/ecm/weatherstripping.py b/bpeng/ecm/weatherstripping.py
new file mode 100644
index 0000000000000000000000000000000000000000..93b7a079e0f08ba6e979b3aa8feb40d53b096806
--- /dev/null
+++ b/bpeng/ecm/weatherstripping.py
@@ -0,0 +1,82 @@
+"""
+Calculate the savings from Weatherstripping
+
+"""
+
+import pandas as pd
+
+
+def weatherstripping_window_doors(weather, hs_overall_eff, fuel_price, cost,
+                                  T_indoor, existing_air_leakage_rating,
+                                  proposed_air_leakage_rating):
+
+    weather_data = pd.DataFrame(weather, index=[0])
+    heating_season = weather_data[[
+        'JAN', 'FEB', 'MAR', 'APR', 'OCT', 'NOV', 'DEC'
+    ]]
+    df2 = heating_season.T
+
+    density_air = 0.0765          # lb/(cu ft)
+    capacity_air = 0.24           # btu/lb F
+
+    inflitration_reduce = existing_air_leakage_rating - proposed_air_leakage_rating
+    df2['monthly loss'] = ''
+
+    for i in range(len(df2)):
+        df2['monthly loss'][i] = inflitration_reduce * (
+            60 * 24 * 30.5) * density_air * capacity_air * (
+                T_indoor - df2[0][i])
+
+    annual_loss = (sum(df2['monthly loss']) / 1000000)                      # MMBTU
+    annual_saving = (annual_loss / hs_overall_eff * fuel_price)
+    payback = (cost / annual_saving)
+
+    # print('Annual heating demand reduction per sq ft (kBtu):',
+    #       annual_loss)
+    # print('Savings/yr($):', round(annual_saving, 2))
+    # print('Simple payback:', round(payback, 2))
+
+    return annual_loss, annual_saving, payback
+
+
+def weatherstripping_ac(weather, hs_overall_eff, fuel_price, estimated_costs, T_indoor):
+
+    """
+    the function is to calcuate the savings for a single weatherstripped AC unit
+
+    Assumptions:
+    weather data for the average monthly temperature in New York
+    Air density = 0.0765 #lb/(cu ft)
+    Heat capacity of Air = 0.24 # btu/lb F
+    Air flow rate reduction for eache weatherstripped AC = 13 cfm - from the SWA study
+    """
+
+    weather_data = pd.DataFrame(weather, index=[0])
+    heating_season = weather_data[[
+        'JAN', 'FEB', 'MAR', 'APR', 'OCT', 'NOV', 'DEC'
+    ]]
+    df2 = heating_season.T
+
+    density_air = 0.0765
+    capacity_air = 0.24
+
+    df2['air flow rate'] = ''
+    df2['monthly loss'] = ''
+
+    for i in range(len(df2)):
+
+        df2['air flow rate'][i] = 13
+        df2['monthly loss'][i] = df2['air flow rate'][i] * (
+            60 * 24 * 30.5) * density_air * capacity_air * (
+                T_indoor - df2[0][i])
+
+    annual_loss = sum(df2['monthly loss']) / 1000000              # MMBTU
+    annual_saving = annual_loss / hs_overall_eff * fuel_price     # $
+    payback = estimated_costs / annual_saving                     # simple payback
+
+    # print('Annual heating demand reduction (MMBtu):', round(
+    #     annual_loss, 2))
+    # print('Savings/yr($):', round(annual_saving, 2))
+    # print('Simple payback:', round(payback, 2))
+
+    return annual_loss, annual_saving, payback
diff --git a/bpeng/tests/test_ecmsavings.py b/bpeng/tests/test_ecmsavings.py
new file mode 100644
index 0000000000000000000000000000000000000000..b0b29d1ecd20f73065c0bed44cd36e103a2a648f
--- /dev/null
+++ b/bpeng/tests/test_ecmsavings.py
@@ -0,0 +1,67 @@
+from bpeng.ecm.weatherstripping import weatherstripping_ac, weatherstripping_window_doors
+
+
+"""
+The overall efficiency of Heating system should be paased from utility bill and energy plus simulation result
+The default value is listing below:
+
+heating_system_overall_eff = float(0.5)
+fuel_price = 12
+
+Estimated costs for install a single window weahterstripping(3'*6') - for $48
+
+window_weatherstripping_costs = 48
+single_window_area = 18
+existing_air_leakage_rating_window = 0.6 #cfm/sq ft
+proposed_air_leakage_rating_window = 0.25 # cfm/ sq ft
+
+"""
+
+avg_temp = {
+            "JAN": 32.9,
+            "FEB": 34.7,
+            "MAR": 42.8,
+            "APR": 52.7,
+            "MAY": 62.6,
+            "JUN": 71.6,
+            "JUL": 76.1,
+            "AUG": 75.2,
+            "SEP": 77.0,
+            "OCT": 57.2,
+            "NOV": 47.3,
+            "DEC": 37.4
+        }
+
+T_indoor = 75                           # F
+ac_weatherstripping_costs = 25          # $
+hs_overall_eff = 0.5
+fuel_price = 12                         # per MMBTU
+
+""" weatherstripping for windows """
+window_costs = 2.8                          # $/ sq ft
+existing_air_leakage_rating_window = 0.6    # cfm/ sq ft
+proposed_air_leakage_rating_window = 0.25   # cfm/ sq ft
+
+""" weatherstripping for exterior door """
+door_costs = 3                              # $/ sq ft
+existing_air_leakage_rating_door = 0.8      # cfm/ sq ft#
+proposed_air_leakage_rating_door = 0.45     # cfm/ sq ft
+
+
+def test_weatherstripping():
+
+    result_ac = weatherstripping_ac(avg_temp, hs_overall_eff, fuel_price, ac_weatherstripping_costs, T_indoor)
+
+    result_window = weatherstripping_window_doors(avg_temp, hs_overall_eff, fuel_price,
+                                                  window_costs, T_indoor,
+                                                  existing_air_leakage_rating_window,
+                                                  proposed_air_leakage_rating_window)
+
+    result_door = weatherstripping_window_doors(avg_temp, hs_overall_eff, fuel_price,
+                                                door_costs, T_indoor,
+                                                existing_air_leakage_rating_door,
+                                                proposed_air_leakage_rating_door)
+
+    print('weatherstripping AC', result_ac)
+    print('weatherstripping windows', result_window)
+    print('weatherstripping door', result_door)