returned appliances

app.py

@@ -1,3 +1,4 @@
+# Constants
 import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
@@ -5,7 +6,6 @@ import seaborn as sns
 import streamlit as st
 from typing import Dict
 
-# Constants
 ONE_BR_UNITS = 23
 TWO_BR_UNITS = 45
 SOLAR_PANEL_RATING = 625  # W
@@ -19,6 +19,9 @@ LIGHTS_1BR = 5
 LIGHTS_2BR = 12
 LIGHT_POWER = 6  # Watts per light
 
+# Non-kitchen appliance consumption
+APPLIANCES = (300 * 24 * 30) + (300 * 6 * 30)  # kWh/month
+
 
 def initialize_session_state():
     """Initialize session state variables"""
@@ -26,8 +29,7 @@ def initialize_session_state():
         "solar_panels": 100,
         "batteries": 50,
         "panel_price": 13000,
-        "battery_price": 135000,
-        # "battery_price": 39000, 200Ah price
+        "battery_price": 39000,
         "grid_price": 28.44,
     }
     for key, value in defaults.items():
@@ -43,19 +45,30 @@ def calculate_lighting_consumption(occupancy_1br: float, occupancy_2br: float) -
     ) * 6  # 6 hours per day
 
 
+def calculate_appliance_consumption(
+    occupancy_1br: float, occupancy_2br: float
+) -> float:
+    """Calculate daily appliance consumption"""
+    return (
+        occupancy_1br * ONE_BR_UNITS * (250 - (LIGHTS_1BR * LIGHT_POWER * 6 / 1000))
+    ) + (
+        occupancy_2br * TWO_BR_UNITS * (400 - (LIGHTS_2BR * LIGHT_POWER * 6 / 1000))
+    )  # Daily kWh
+
+
 def total_consumption(
     occupancy_1br: float, occupancy_2br: float, common_area: float
 ) -> float:
     """Calculate total monthly consumption"""
     lighting = calculate_lighting_consumption(occupancy_1br, occupancy_2br)
-
-    return (lighting + common_area) * 30  # Monthly kWh
+    appliances = calculate_appliance_consumption(occupancy_1br, occupancy_2br)
+    return (lighting + appliances + common_area) * 30  # Monthly kWh
 
 
 def solar_production(panels: int) -> float:
     """Monthly solar production with losses"""
     daily_production = (
-        panels * SOLAR_PANEL_RATING * 6 * (1 - SYSTEM_LOSSES) / 1000
+        panels * SOLAR_PANEL_RATING * 5 * (1 - SYSTEM_LOSSES) / 1000
     )  # 5 sun hours
     return daily_production * 30  # Monthly kWh
 
@@ -65,13 +78,11 @@ def battery_storage(batteries: int) -> float:
     return batteries * BATTERY_CAPACITY * BATTERY_VOLTAGE * 0.8 / 1000  # kWh
 
 
-def financial_analysis(
-    consumption: float, common_area: float, production: float, storage: float
-) -> Dict:
+def financial_analysis(consumption: float, production: float, storage: float) -> Dict:
     """Detailed financial calculations"""
     solar_used = min(production, consumption)
     surplus = max(0, production - consumption)
-    # feed_in_revenue = surplus * FEED_IN_TARIFF / 100  # Convert to Ksh from cents/kWh
+    feed_in_revenue = surplus * FEED_IN_TARIFF / 100  # Convert to Ksh from cents/kWh
 
     # Account for battery storage
     grid_purchased = max(0, consumption - solar_used)
@@ -80,7 +91,9 @@ def financial_analysis(
         grid_offset = min(grid_purchased, storage)
         grid_purchased -= grid_offset
 
-    monthly_savings = (consumption - common_area) * st.session_state.grid_price / 100
+    monthly_savings = (
+        (consumption * st.session_state.grid_price / 100)
+       
     total_investment = (
         st.session_state.solar_panels * st.session_state.panel_price
         + st.session_state.batteries * st.session_state.battery_price
@@ -109,6 +122,8 @@ def create_consumption_breakdown(
     """Create detailed consumption breakdown"""
     breakdown = {
         "Lighting": calculate_lighting_consumption(occupancy_1br, occupancy_2br) * 30,
+        "Appliances": calculate_appliance_consumption(occupancy_1br, occupancy_2br)
+        * 30,
         "Common Areas": common_area * 30,
     }
     return pd.DataFrame.from_dict(breakdown, orient="index", columns=["kWh"])
@@ -195,7 +210,7 @@ def main():
             st.number_input(
                 "Battery Price (Ksh)",
                 5000,
-                300000,
+                100000,
                 step=1000,
                 key="battery_price",
                 help="Cost per battery unit",
@@ -229,7 +244,7 @@ def main():
     scenarios = {}
 
     # Common area consumption remains constant
-    common_area_consumption = (1782 + 180 + 12) / 1000  # kWh per day
+    common_area_consumption = 5.904  # kWh per day
 
     # Generate scenarios with different occupancy combinations
     occupancy_levels = [0.0, 0.25, 0.50, 0.75, 1.0]
@@ -256,9 +271,7 @@ def main():
         consumption = total_consumption(params["1br"], params["2br"], params["common"])
         production = solar_production(st.session_state.solar_panels)
         storage = battery_storage(st.session_state.batteries)
-        financials = financial_analysis(
-            consumption, common_area_consumption * 30, production, storage
-        )
+        financials = financial_analysis(consumption, production, storage)
         analysis_data.append({"Scenario": name, **financials})
 
     df = pd.DataFrame(analysis_data)
@@ -483,7 +496,7 @@ def main():
                 
                 **Savings Calculation:**
                 - Grid Price: {st.session_state.grid_price} Ksh/kWh
-                - Monthly Savings = (Total Consumption - Common Areas)× Grid Price)
+                - Monthly Savings = (Total Consumption × Grid Price) - (Grid Purchased × Grid Price)
                 - Payback Period = Total Investment / Annual Savings
                 
                 **Filtered Scenario Data:**
@@ -531,8 +544,8 @@ def main():
             fig3 = plt.figure(figsize=(8, 8))
             ax3 = fig3.add_subplot(111)
 
-            colors = ["#FF9800", "#4CAF50"]
-            explode = (0.1, 0)
+            colors = ["#FF9800", "#2196F3", "#4CAF50"]
+            explode = (0.1, 0, 0)
 
             wedges, texts, autotexts = ax3.pie(
                 breakdown_df["kWh"],