Update data/ashrae_tables.py

data/ashrae_tables.py

@@ -189,6 +189,55 @@ class ASHRAETables:
             0.9: 1.15    # Dark surfaces
         }
 
+    def _load_occupancy_heat_gain_table(self) -> pd.DataFrame:
+        """
+        Load heat gain table for occupancy types based on ASHRAE Handbook—Fundamentals (2017, Chapter 18).
+        
+        Returns:
+            pd.DataFrame: DataFrame with columns 'occupancy_type', 'sensible_gain' (W), 'latent_gain' (W).
+        """
+        BTUH_TO_W = 0.293071  # Conversion factor: 1 Btu/h = 0.293071 W
+        data = [
+            {'occupancy_type': 'Seated, resting', 'sensible_gain': 240 * BTUH_TO_W, 'latent_gain': 100 * BTUH_TO_W},
+            {'occupancy_type': 'Seated, light work', 'sensible_gain': 275 * BTUH_TO_W, 'latent_gain': 150 * BTUH_TO_W},
+            {'occupancy_type': 'Seated, typing', 'sensible_gain': 300 * BTUH_TO_W, 'latent_gain': 200 * BTUH_TO_W},
+            {'occupancy_type': 'Standing, light work', 'sensible_gain': 350 * BTUH_TO_W, 'latent_gain': 250 * BTUH_TO_W},
+            {'occupancy_type': 'Standing, medium work', 'sensible_gain': 400 * BTUH_TO_W, 'latent_gain': 300 * BTUH_TO_W},
+            {'occupancy_type': 'Walking', 'sensible_gain': 450 * BTUH_TO_W, 'latent_gain': 350 * BTUH_TO_W},
+            {'occupancy_type': 'Light exercise', 'sensible_gain': 500 * BTUH_TO_W, 'latent_gain': 400 * BTUH_TO_W},
+            {'occupancy_type': 'Medium exercise', 'sensible_gain': 600 * BTUH_TO_W, 'latent_gain': 500 * BTUH_TO_W},
+            {'occupancy_type': 'Heavy exercise', 'sensible_gain': 800 * BTUH_TO_W, 'latent_gain': 600 * BTUH_TO_W},
+            {'occupancy_type': 'Dancing', 'sensible_gain': 900 * BTUH_TO_W, 'latent_gain': 700 * BTUH_TO_W}
+        ]
+        return pd.DataFrame(data)
+    
+    def _load_equipment_heat_gain_table(self) -> pd.DataFrame:
+        """
+        Load heat gain table for equipment types based on ASHRAE Handbook—Fundamentals (2017, Chapter 18).
+        
+        Returns:
+            pd.DataFrame: DataFrame with columns 'equipment_type', 'sensible_gain' (W), 'latent_gain' (W).
+        """
+        BTUH_TO_W = 0.293071  # Conversion factor: 1 Btu/h = 0.293071 W
+        data = [
+            {'equipment_type': 'Computer', 'sensible_gain': 500 * BTUH_TO_W, 'latent_gain': 0.0},
+            {'equipment_type': 'Monitor', 'sensible_gain': 200 * BTUH_TO_W, 'latent_gain': 0.0},
+            {'equipment_type': 'Printer (small)', 'sensible_gain': 300 * BTUH_TO_W, 'latent_gain': 0.0},
+            {'equipment_type': 'Printer (large)', 'sensible_gain': 1000 * BTUH_TO_W, 'latent_gain': 0.0},
+            {'equipment_type': 'Copier (small)', 'sensible_gain': 600 * BTUH_TO_W, 'latent_gain': 0.0},
+            {'equipment_type': 'Copier (large)', 'sensible_gain': 1500 * BTUH_TO_W, 'latent_gain': 0.0},
+            {'equipment_type': 'Server', 'sensible_gain': 2000 * BTUH_TO_W, 'latent_gain': 0.0},
+            {'equipment_type': 'Refrigerator (small)', 'sensible_gain': 800 * BTUH_TO_W, 'latent_gain': 200 * BTUH_TO_W},
+            {'equipment_type': 'Refrigerator (large)', 'sensible_gain': 1200 * BTUH_TO_W, 'latent_gain': 300 * BTUH_TO_W},
+            {'equipment_type': 'Microwave', 'sensible_gain': 500 * BTUH_TO_W, 'latent_gain': 100 * BTUH_TO_W},
+            {'equipment_type': 'Coffee maker', 'sensible_gain': 400 * BTUH_TO_W, 'latent_gain': 100 * BTUH_TO_W},
+            {'equipment_type': 'TV (small)', 'sensible_gain': 300 * BTUH_TO_W, 'latent_gain': 0.0},
+            {'equipment_type': 'TV (large)', 'sensible_gain': 600 * BTUH_TO_W, 'latent_gain': 0.0},
+            {'equipment_type': 'Projector', 'sensible_gain': 700 * BTUH_TO_W, 'latent_gain': 0.0},
+            {'equipment_type': 'Lab equipment', 'sensible_gain': 1500 * BTUH_TO_W, 'latent_gain': 0.0}
+        ]
+        return pd.DataFrame(data)
+    
     def _load_heat_gain_table(self) -> pd.DataFrame:
         """
         Load heat gain table for internal sources based on ASHRAE Handbook—Fundamentals (2017, Chapter 18).
@@ -200,10 +249,10 @@ class ASHRAETables:
         # Get occupancy and equipment heat gain tables
         occupancy_df = self._load_occupancy_heat_gain_table()
         equipment_df = self._load_equipment_heat_gain_table()
-    
+        
         # Prepare data for consolidated table
         data = []
-    
+        
         # People: Map occupancy types to heat gains
         for _, row in occupancy_df.iterrows():
             data.append({
@@ -213,38 +262,43 @@ class ASHRAETables:
                 'latent': row['latent_gain']
             })
         
-        # Lighting: Assume 1 W sensible gain per Watt, no latent gain
-        data.append({
-            'category': 'lighting',
-            'subcategory': 'general',
-            'sensible': 1.0,  # 1 W/W (100% sensible heat)
-            'latent': 0.0
-        })
-    
+        # Lighting: Add categories for different lighting technologies
+        lighting_types = [
+            {'subcategory': 'general', 'sensible': 1.0, 'latent': 0.0},  # Fallback for total lighting power
+            {'subcategory': 'LED', 'sensible': 0.80, 'latent': 0.0},     # 80% sensible heat
+            {'subcategory': 'Fluorescent', 'sensible': 0.85, 'latent': 0.0},  # Includes ballast losses
+            {'subcategory': 'Halogen', 'sensible': 0.95, 'latent': 0.0},      # High thermal output
+            {'subcategory': 'Incandescent', 'sensible': 0.98, 'latent': 0.0}  # Nearly all heat
+        ]
+        for lt in lighting_types:
+            data.append({
+                'category': 'lighting',
+                'subcategory': lt['subcategory'],
+                'sensible': lt['sensible'],
+                'latent': lt['latent']
+            })
+        
         # Equipment: Use a generic value (adjustable in cooling_load.py via radiation_fraction)
-        # Aggregate equipment gains as a baseline (e.g., average or typical office equipment)
-        equipment_sensible = equipment_df['sensible_gain'].mean()  # Example: mean sensible gain
-        equipment_latent = equipment_df['latent_gain'].mean()      # Example: mean latent gain
         data.append({
             'category': 'equipment',
             'subcategory': 'office',
             'sensible': 1.0,  # 1 W/W sensible, scalable by power in cooling_load.py
             'latent': 0.0     # Assume no latent gain for generic equipment
         })
-    
+        
         return pd.DataFrame(data)
-
+    
     def get_heat_gain(self, source: str, subcategory: Optional[str] = None) -> Tuple[float, float]:
         """
         Get sensible and latent heat gain for an internal source.
         
         Args:
             source (str): Source type ('people', 'lighting', 'equipment').
-            subcategory (str, optional): Subcategory (e.g., 'Seated, resting' for people, 'general' for lighting).
-    
+            subcategory (str, optional): Subcategory (e.g., 'Seated, resting' for people, 'LED' for lighting).
+        
         Returns:
             Tuple[float, float]: Sensible and latent heat gain values (Watts).
-    
+        
         Raises:
             ValueError: If source or subcategory is invalid.
         """